updated

adience_align-master/.gitignore

@@ -0,0 +1,54 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+bin/
+build/
+develop-eggs/
+dist/
+eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.cache
+nosetests.xml
+coverage.xml
+
+# Translations
+*.mo
+
+# Mr Developer
+.mr.developer.cfg
+.project
+.pydevproject
+
+# Rope
+.ropeproject
+
+# Django stuff:
+*.log
+*.pot
+
+# Sphinx documentation
+docs/_build/
+

adience_align-master/CREDIT.TXT

@@ -0,0 +1,32 @@
+This implements a face alignment method, as preprocessing to tasks such as age and gender estimation, 
+and face recognition as described in [1] and [2].
+
+The code calls an executable of facial landmarks detection, by X.Zhu and D. Ramanan, implementing the algorithm described in [3]. 
+
+(The rest of this text is a quotation from their code: Copyright (C) 2012 Xiangxin Zhu, Deva Ramanan)
+
+It includes pre-trained face models. 
+
+Much of the detection code is built on top of part-based model implementation of [4]. 
+
+The training code implements a quadratic program (QP) solver described in [5].
+
+In the training code, we use the positive samples from MultiPIE dataset (available at www.multipie.org) and the negative images from the INRIAPerson dataset [6] (included in the package). 
+
+Acknowledgements: We graciously thank the authors of the previous code releases and image benchmarks for making them publicly available.
+
+References
+==========
+
+[1] E. Eidinger, R. Enbar, T. Hassner, Age and Gender Estimation of Unfiltered Faces, submitted to IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2014
+
+[2] http://www.openu.ac.il/home/hassner/Adience/links.html
+
+[3] X. Zhu, D. Ramanan. Face Detection, Pose Estimation and Landmark Localization in the Wild. CVPR 2012.
+
+[4] P. Felzenszwalb, R. Girshick, D. McAllester. Discriminatively Trained Deformable Part Models. http://people.cs.uchicago.edu/~pff/latent.
+
+[5] D. Ramanan. Dual Coordinate Descent Solvers for Large Structured Prediction Problems. UCI Technical Report, to appear.
+
+[6] N. Dalal, B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005.
+

adience_align-master/LICENSE.txt

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+Copyright (C) 2014 Adience SER Ltd. (www.adience.com)
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+

adience_align-master/README.md

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+adience_align
+========
+
+This project provides alignment tools for faces, to be used as a preprocessing step before computer vision tasks on face images.
+
+Homepage for the project: http://www.openu.ac.il/home/hassner/Adience/
+
+
+See the test for example usage.
+
+Specificaly, the "pipeline" test, shows how to use the full process (just remember to change the location of the model files to where you stor the *.xml and other model files)
+
+Installation
+=========
+in the root of the repository:
+
+```
+python setup.py sdist
+sudo pip install dist/adience-<version_number>.tar.gz
+```
+
+
+
+CopyRight
+=========
+(contact: Eran Eidinger (eran@adience.com), Roee Enbar (roee.e@adience.com))
+
+See the LICENSE.txt file (basically, an MIT license).
+
+
+With any publication that uses this alignment code, or it's derivative, we kindly ask that you cite the paper:
+E. Eidinger, R. Enbar, and T. Hassner, Age and Gender Estimation of Unfiltered Faces, Transactions on Information Forensics and Security (IEEE-TIFS), special issue on Face Recognition in the Wild
+
+For more details, please see:
+http://www.openu.ac.il/home/hassner/Adience/publications.html
+
+Compilation notes
+========
+1. The shared objects were compiled for linux 64bit on Ubuntu 13.10
+2. The SO uses boost-1.53, so make sure it is installed on your system and available at /usr/local/, or use LD_LIBRARY_PATH="yourpath" to point it at the right place. Alternatively, place "libboost_system.so.1.53.0" and "libboost_filesystem.so.1.53.0". at the "adiencealign/resources/" subfolder
+3. For landmarks detection, we use the file libPartsBasedDetector.so, compiled from the project https://github.com/wg-perception/PartsBasedDetector. You can either compile it yourselves, or use the version under "resources" subfolder, compiled with boost 1.53, on a linux ubuntu 14.04 machine.
+
+We will release the source code for the shared object in the near future
+
+Running the test
+========
+1. run ```./clear_test.sh``` to delete results of old tests.
+2. run ```python test_pipeline.py```
+3. results are in the "outputs" subfolder

adience_align-master/adiencealign(old)/affine_alignment/affine_aligner.py

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+'''
+Created on May 7, 2014
+
+@author: eran
+'''
+from adiencealign.common.landmarks import fidu_transform, shift_vector,\
+    WEIGHTS3
+
+class AffineAligner(object):
+    def __init__(self, fidu_model_file, ):
+        
+        self.shift = ( 0.25, 0.25 )
+        fidu_model = [(int(x.split(',')[1]),int(x.split(',')[2])) for x in file(fidu_model_file,'r')]
+        self.fidu_model = shift_vector(fidu_model, self.shift)
+        self.WEIGHTS3 = WEIGHTS3
+        
+    def align(self, img, fidu_points):
+        # create bs1 image
+        funneled_img, R = fidu_transform(self.fidu_model, fidu_points, WEIGHTS3, img, self.shift)
+        return funneled_img, R      

adience_align-master/adiencealign(old)/cascade_detection/cascade_detector.py

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+'''
+Created on May 7, 2014
+
+@author: eran
+'''
+import cv2
+import pickle
+import numpy as np
+from shapely.geometry.polygon import Polygon
+import math
+from adiencealign.common.files import make_path, expand_path
+from adiencealign.common.images import pad_image_for_rotation
+
+class CascadeDetector(object):
+    '''
+    This is a haar cascade classifier capable of detecting in multiple angles
+    '''
+    def __init__(self, cascade_file = './resources/haarcascade_frontalface_default.xml', 
+                 min_size = (10, 10),
+                 min_neighbors = 20,
+                 scale_factor = 1.04,
+                 angles = [0],
+                 thr = 0.4, 
+                 cascade_type = 'haar'):
+        '''
+        cascade_type - is a string defining the type of cascade
+        '''
+        print(expand_path('.'))
+        self.cascade_file = cascade_file.rsplit('/',1)[1]
+        self._cascade_classifier = cv2.CascadeClassifier(cascade_file)
+        self.scale_factor = scale_factor
+        self.min_neighbors = min_neighbors
+        self.min_size = min_size
+        self.cascade_type = cascade_type
+        self.angles = angles
+        self.thr = thr
+        
+    def __str__(self):
+        return ''.join([str(x) for x in ['cascade_file:',self.cascade_file,
+                          ',scale_factor:',self.scale_factor,
+                          ',min_neighbors:',self.min_neighbors,
+                          ',min_neighbors:',self.min_neighbors,
+                          ',cascade_type:',self.cascade_type
+                          ]])
+    
+    def save_configuration(self, target_file):
+        file_path = target_file.rsplit('/',1)[0]
+        make_path(file_path)
+        config = {'min_size':self.min_size, 'min_neighbours':self.min_neighbors, 'scale_factor':self.scale_factor, 'cascade_file':self.cascade_file}
+        pickle.dump(obj=config, file = open(target_file,'w'), protocol = 2)
+    
+    @staticmethod
+    def load_configuration(target_file):
+        return pickle.load(open(target_file,'r'))
+        
+    def detectMultiScaleWithScores(self, img, scaleFactor = None, minNeighbors = None, minSize = None, flags = 4):
+        scaleFactor = self.scale_factor if not scaleFactor else scaleFactor
+        minNeighbors = self.min_neighbors if not minNeighbors else minNeighbors
+        minSize = self.min_size if not minSize else minSize
+        return self._cascade_classifier.detectMultiScale(img, 
+                                                         scaleFactor = scaleFactor, 
+                                                         minNeighbors = minNeighbors, 
+                                                         minSize = minSize, 
+                                                         flags = flags)
+        
+    def detectWithAngles(self, img, angels = None, resolve = True, thr = None ):
+        '''
+        angles - a list of angles to test. If None, default to the value created at the constructor (which defaults to [0])
+        resolve - a boolean flag, whether or not to cluster the boxes, and resolve cluster by highest score.
+        thr - the maximum area covered with objects, before we break from the angles loop
+        
+        returns - a list of CascadeResult() objects
+        '''
+        
+        if thr == None:
+            thr = self.thr
+            
+        original_size = img.shape[0] * img.shape[0]
+        if angels == None:
+            angels = self.angles
+            
+        results = []
+        total_area = 0
+        for angle in angels:
+
+            # the diagonal of the image is the diameter of the rotated image, so the big_image needs to bound this circle
+            # by being that big
+
+            big_image, x_shift, y_shift, diag, rot_center = pad_image_for_rotation(img)
+            
+            # find the rotation and the inverse rotation matrix, to allow translations between old and new coordinates and vice versa
+            rot_mat = cv2.getRotationMatrix2D(rot_center, angle, scale = 1.0)
+            inv_rot_mat = cv2.invertAffineTransform(rot_mat)
+            
+            # rotate the image by the desired angle
+            rot_image = cv2.warpAffine(big_image, rot_mat, (big_image.shape[1],big_image.shape[0]), flags=cv2.INTER_CUBIC)
+            faces = self.detectMultiScaleWithScores(rot_image, scaleFactor = 1.03, minNeighbors = 20, minSize = (15,15), flags = 4)
+            for face in faces:
+                xp = face[0]
+                dx = face[2]
+                yp = face[1]
+                dy = face[3]
+                score = 1
+                dots = np.matrix([[xp,xp+dx,xp+dx,xp], [yp,yp,yp+dy,yp+dy], [1, 1, 1, 1]])
+                # these are the original coordinates in the "big_image"
+#                print dots
+                originals_in_big = inv_rot_mat * dots
+#                print originals_in_big
+                shifter = np.matrix([[x_shift]*4, [y_shift]*4])
+#                print shifter
+                # these are the original coordinate in the original image
+                originals = originals_in_big - shifter
+#                print originals
+                points = np.array(originals.transpose())
+                x = points[0,0]
+                y = points[0,1]
+                box_with_score = ([x,y,dx,dy], score)
+                
+                cascade_result = CascadeResult.from_polygon_points(points, score, self.cascade_type)
+#                print cascade_result
+                
+                results.append(cascade_result)
+            
+            #################
+            # test and see, if we found enough objects, break out and don't waste our time
+                total_area += cascade_result.area     
+            
+        if resolve:
+            return resolve_angles(results, width = img.shape[1], height = img.shape[0])
+        else:
+            return results
+
+class BoxInImage(object):
+    def __init__(self, originals, dx, dy, score = None, angle = 0):
+        self.originals = originals
+        self.dx = dx
+        self.dy = dy
+        self.score = score
+        self.angle = angle
+    
+    def __str__(self):
+        return ",".join([str(x) for x in [self.originals, self.dx, self.dy, self.score, self.angle]])
+
+def resolve_angles(list_of_results, width, height, thr = 0.3):
+    '''
+    we want to cluster the boxes into clusters, and then choose the best box in each cluster by score
+        * thr - decides what the maximum distance is for a box to join a cluster, in the sense of how much of it's area is covered by the best box in the cluster
+              note, that two squares, centered, with 45 degrees rotation, will overlap on 77% of their area (thr == 0.22)    
+    '''
+    clusters = []
+    for box in list_of_results:
+#        total_polygon = Polygon([(0,0), (width,0), (width,height), (0,height)])
+#        if box.polygon.intersection(total_polygon).area < box.area:
+#            # this means the box is outside the image somehow
+#            continue
+    
+        area = box.area
+        closest_cluster = None
+        dist_to_closest_cluster = 1.0
+        for n,cluster in enumerate(clusters):
+            dist = 1.0
+            for cluster_box in cluster:
+                local_dist = 1.0 - box.overlap(cluster_box)/area
+                dist = min(dist, local_dist)
+            if dist < dist_to_closest_cluster:
+                dist_to_closest_cluster = dist
+                closest_cluster = n
+        if closest_cluster == None or dist_to_closest_cluster > thr:
+            # no good cluster was found, open a new cluster
+            clusters.append([box])
+        else:
+            clusters[n].append(box)
+    
+    centroids = []
+    for cluster in clusters:
+        centroids.append(sorted(cluster,key=lambda x: x.score)[-1])
+    
+    return centroids
+            
+        
+            
+        
+    
+def resolve_boxes(dict_of_list_of_cascade_results, min_overlap = 0.7):
+    '''
+    Say you tried two different cascades to detect faces.
+    enter a dictionary (the key is a string describing a cascade type) of detected objects
+    This function returns a unified results list, where it resolves overlapping boxes, and chooses one of them.
+    
+    The bigger boxes are selected instead of smaller ones, whether they contain them, or enough of them, determined by min_overlap
+    
+    '''
+    final_faces = []
+    for cascade_str, faces in dict_of_list_of_cascade_results.items():
+        # go through each cascade type
+        for face in faces:
+            if type(face) == CascadeResult:
+                new_res = face
+            else:
+                new_res = CascadeResult(face,cascade_type = cascade_str)
+            to_add = True
+            for old_index,old_res in enumerate(final_faces):
+                ratio = new_res.area / old_res.area
+                if ratio >1.0:
+                    # new_box is bigger
+                    if new_res.overlap(old_res)/old_res.area > min_overlap:
+                        # the new box contains the old one, we want to replace it:
+                        final_faces[old_index] = new_res
+                        to_add = False
+                        break
+                if ratio <=1.0:
+                    # the new_box is smaller
+                    if new_res.overlap(old_res)/new_res.area > min_overlap:
+                        # the old box contains the new one, we therefore dont need to add the new box:
+                        to_add = False
+                        break
+            if to_add:
+                # if there was no hit, this is a new face, we can add it
+                final_faces.append(new_res)
+    return final_faces
+
+def most_centered_box( cascade_results, xxx_todo_changeme ):
+    ( rows, cols ) = xxx_todo_changeme
+    best_err = 1e10
+    for i, cascade in enumerate( cascade_results ):
+        err = ( cascade.x + cascade.dx / 2 - cols / 2 ) ** 2 + ( cascade.y + cascade.dy / 2 - rows / 2 ) ** 2
+        if err < best_err:
+            index = i
+    return cascade_results[ index ]
+        
+class CascadeResult(object):
+    def __init__(self, box_with_score, cascade_type = None, angle = 0):
+        self.x = box_with_score[0][0]
+        self.y = box_with_score[0][1]
+        self.dx = box_with_score[0][2]
+        self.dy = box_with_score[0][3]
+        self.score = box_with_score[1]
+        self.cascade_type = cascade_type
+        self.angle = angle
+        
+    @staticmethod
+    def from_polygon_points(points, score, cascade_type = None):
+        '''
+        an alternative generator, allows giving the polygon points instead of [x,y,dx,dy]
+        '''
+        x = points[0,0]
+        y = points[0,1]
+        top = points[1,] - points[0,]
+        left = points[3,] - points[0,]
+        dx = math.sqrt(sum([i*i for i in top]))
+        dy = math.sqrt(sum([i*i for i in left]))
+        angle = math.atan(float(top[1])/top[0]) * 180 / math.pi if top[0] != 0 else (970 if top[1] >0 else -90)
+        return CascadeResult(([x,y,dx,dy],score), cascade_type, angle)
+        
+    
+    def __str__(self):
+        return ''.join([str(x) for x in ['center:',self.center,
+                                         ',\nx:',self.x,
+                                         ',\ny:',self.y,
+                                         ',\ndx:',self.dx,
+                                         ',\ndy:',self.dy,
+                                         ',\nscore:',self.score,
+                                         ',\nangle:',self.angle,
+                                         ',\ncascade_type:',self.cascade_type,
+                                         ',\npoints_int:\n',self.points_int
+                                         ]])
+    
+    @property
+    def points(self):
+        x = self.x
+        y = self.y
+        dx = self.dx
+        dy = self.dy
+        a = self.angle/180.0*math.pi
+        dots = np.matrix([[x,y,1],[x+dx,y,1],[x+dx,y+dy,1],[x,y+dy,1]])
+        dots = dots.transpose()
+        rot_mat = cv2.getRotationMatrix2D((dots[0,0],dots[1,0]), -self.angle, scale = 1.0)
+        points = rot_mat * dots
+        points = points.transpose() 
+        return points
+
+    @property
+    def center(self):
+        return tuple(int(x) for x in (self.points.sum(0)/4.0).tolist()[0])
+    
+    @property
+    def points_int(self):
+        return self.points.astype(int)
+        
+    @property
+    def score_with_type(self):
+        if self.cascade_type:
+            return self.cascade_type + ' ' + str(self.score)
+        else:
+            return str(self.score)
+        
+    @property
+    def filename_encode(self):
+        
+        return '_'.join([str(x) for x in ['loct'] + self.cvformat_result[0] + ['ang', int(self.angle),self.cascade_type, self.score]]) 
+    
+    @property
+    def cvformat_coords(self):
+        if self.angle == 0:
+            return [int(x) for x in [self.x, self.y, self.dx, self.dy]]
+        else:
+            raise Exception('cannot return [x,y,dx,dy] for a box with angle, use cvformat_result() instead')
+        
+    @property
+    def cvformat_result(self):
+        return ([int(x) for x in [self.x, self.y, self.dx, self.dy]], self.score, self.angle)
+    
+#    @property
+#    def rot_matrix(self):
+#        return array([[cos(math.radians(self.angle)), -sin(math.radians(self.angle))], 
+#                   [sin(math.radians(self.angle)),  cos(math.radians(self.angle))]])
+
+    @property
+    def top_left(self):
+        return tuple(self.points[0,].tolist()[0])
+    
+    @property
+    def top_right(self):
+        return tuple(self.points[1,].tolist()[0])
+
+    @property
+    def bottom_right(self):
+        return tuple(self.points[2,].tolist()[0])
+
+    @property
+    def bottom_left(self):
+        return tuple(self.points[3,].tolist()[0])
+    
+    @property
+    def polygon(self):
+        return Polygon([self.top_left, self.top_right, self.bottom_right, self.bottom_left])
+        
+    def overlap(self, otherRect):      
+        return float(self.polygon.intersection(otherRect.polygon).area)
+    
+    @property
+    def area(self):
+        return float(self.polygon.area)
+    
+    def __gt__(self,b):
+        return self.area>b.area
+    def __ge__(self,b):
+        return self.area>=b.area
+    def __lt__(self,b):
+        return self.area<b.area    
+    def __le__(self,b):
+        return self.area<=b.area
+            
+        

adience_align-master/adiencealign(old)/cascade_detection/cascade_detector.py.bak

@@ -0,0 +1,353 @@
+'''
+Created on May 7, 2014
+
+@author: eran
+'''
+import cv2
+import pickle
+import numpy as np
+from shapely.geometry.polygon import Polygon
+import math
+from adiencealign.common.files import make_path, expand_path
+from adiencealign.common.images import pad_image_for_rotation
+
+class CascadeDetector(object):
+    '''
+    This is a haar cascade classifier capable of detecting in multiple angles
+    '''
+    def __init__(self, cascade_file = './resources/haarcascade_frontalface_default.xml', 
+                 min_size = (10, 10),
+                 min_neighbors = 20,
+                 scale_factor = 1.04,
+                 angles = [0],
+                 thr = 0.4, 
+                 cascade_type = 'haar'):
+        '''
+        cascade_type - is a string defining the type of cascade
+        '''
+        print expand_path('.')
+        self.cascade_file = cascade_file.rsplit('/',1)[1]
+        self._cascade_classifier = cv2.CascadeClassifier(cascade_file)
+        self.scale_factor = scale_factor
+        self.min_neighbors = min_neighbors
+        self.min_size = min_size
+        self.cascade_type = cascade_type
+        self.angles = angles
+        self.thr = thr
+        
+    def __str__(self):
+        return ''.join([str(x) for x in ['cascade_file:',self.cascade_file,
+                          ',scale_factor:',self.scale_factor,
+                          ',min_neighbors:',self.min_neighbors,
+                          ',min_neighbors:',self.min_neighbors,
+                          ',cascade_type:',self.cascade_type
+                          ]])
+    
+    def save_configuration(self, target_file):
+        file_path = target_file.rsplit('/',1)[0]
+        make_path(file_path)
+        config = {'min_size':self.min_size, 'min_neighbours':self.min_neighbors, 'scale_factor':self.scale_factor, 'cascade_file':self.cascade_file}
+        pickle.dump(obj=config, file = open(target_file,'w'), protocol = 2)
+    
+    @staticmethod
+    def load_configuration(target_file):
+        return pickle.load(open(target_file,'r'))
+        
+    def detectMultiScaleWithScores(self, img, scaleFactor = None, minNeighbors = None, minSize = None, flags = 4):
+        scaleFactor = self.scale_factor if not scaleFactor else scaleFactor
+        minNeighbors = self.min_neighbors if not minNeighbors else minNeighbors
+        minSize = self.min_size if not minSize else minSize
+        return self._cascade_classifier.detectMultiScale(img, 
+                                                         scaleFactor = scaleFactor, 
+                                                         minNeighbors = minNeighbors, 
+                                                         minSize = minSize, 
+                                                         flags = flags)
+        
+    def detectWithAngles(self, img, angels = None, resolve = True, thr = None ):
+        '''
+        angles - a list of angles to test. If None, default to the value created at the constructor (which defaults to [0])
+        resolve - a boolean flag, whether or not to cluster the boxes, and resolve cluster by highest score.
+        thr - the maximum area covered with objects, before we break from the angles loop
+        
+        returns - a list of CascadeResult() objects
+        '''
+        
+        if thr == None:
+            thr = self.thr
+            
+        original_size = img.shape[0] * img.shape[0]
+        if angels == None:
+            angels = self.angles
+            
+        results = []
+        total_area = 0
+        for angle in angels:
+
+            # the diagonal of the image is the diameter of the rotated image, so the big_image needs to bound this circle
+            # by being that big
+
+            big_image, x_shift, y_shift, diag, rot_center = pad_image_for_rotation(img)
+            
+            # find the rotation and the inverse rotation matrix, to allow translations between old and new coordinates and vice versa
+            rot_mat = cv2.getRotationMatrix2D(rot_center, angle, scale = 1.0)
+            inv_rot_mat = cv2.invertAffineTransform(rot_mat)
+            
+            # rotate the image by the desired angle
+            rot_image = cv2.warpAffine(big_image, rot_mat, (big_image.shape[1],big_image.shape[0]), flags=cv2.INTER_CUBIC)
+            faces = self.detectMultiScaleWithScores(rot_image, scaleFactor = 1.03, minNeighbors = 20, minSize = (15,15), flags = 4)
+            for face in faces:
+                xp = face[0]
+                dx = face[2]
+                yp = face[1]
+                dy = face[3]
+                score = 1
+                dots = np.matrix([[xp,xp+dx,xp+dx,xp], [yp,yp,yp+dy,yp+dy], [1, 1, 1, 1]])
+                # these are the original coordinates in the "big_image"
+#                print dots
+                originals_in_big = inv_rot_mat * dots
+#                print originals_in_big
+                shifter = np.matrix([[x_shift]*4, [y_shift]*4])
+#                print shifter
+                # these are the original coordinate in the original image
+                originals = originals_in_big - shifter
+#                print originals
+                points = np.array(originals.transpose())
+                x = points[0,0]
+                y = points[0,1]
+                box_with_score = ([x,y,dx,dy], score)
+                
+                cascade_result = CascadeResult.from_polygon_points(points, score, self.cascade_type)
+#                print cascade_result
+                
+                results.append(cascade_result)
+            
+            #################
+            # test and see, if we found enough objects, break out and don't waste our time
+                total_area += cascade_result.area     
+            
+        if resolve:
+            return resolve_angles(results, width = img.shape[1], height = img.shape[0])
+        else:
+            return results
+
+class BoxInImage(object):
+    def __init__(self, originals, dx, dy, score = None, angle = 0):
+        self.originals = originals
+        self.dx = dx
+        self.dy = dy
+        self.score = score
+        self.angle = angle
+    
+    def __str__(self):
+        return ",".join([str(x) for x in [self.originals, self.dx, self.dy, self.score, self.angle]])
+
+def resolve_angles(list_of_results, width, height, thr = 0.3):
+    '''
+    we want to cluster the boxes into clusters, and then choose the best box in each cluster by score
+        * thr - decides what the maximum distance is for a box to join a cluster, in the sense of how much of it's area is covered by the best box in the cluster
+              note, that two squares, centered, with 45 degrees rotation, will overlap on 77% of their area (thr == 0.22)    
+    '''
+    clusters = []
+    for box in list_of_results:
+#        total_polygon = Polygon([(0,0), (width,0), (width,height), (0,height)])
+#        if box.polygon.intersection(total_polygon).area < box.area:
+#            # this means the box is outside the image somehow
+#            continue
+    
+        area = box.area
+        closest_cluster = None
+        dist_to_closest_cluster = 1.0
+        for n,cluster in enumerate(clusters):
+            dist = 1.0
+            for cluster_box in cluster:
+                local_dist = 1.0 - box.overlap(cluster_box)/area
+                dist = min(dist, local_dist)
+            if dist < dist_to_closest_cluster:
+                dist_to_closest_cluster = dist
+                closest_cluster = n
+        if closest_cluster == None or dist_to_closest_cluster > thr:
+            # no good cluster was found, open a new cluster
+            clusters.append([box])
+        else:
+            clusters[n].append(box)
+    
+    centroids = []
+    for cluster in clusters:
+        centroids.append(sorted(cluster,key=lambda x: x.score)[-1])
+    
+    return centroids
+            
+        
+            
+        
+    
+def resolve_boxes(dict_of_list_of_cascade_results, min_overlap = 0.7):
+    '''
+    Say you tried two different cascades to detect faces.
+    enter a dictionary (the key is a string describing a cascade type) of detected objects
+    This function returns a unified results list, where it resolves overlapping boxes, and chooses one of them.
+    
+    The bigger boxes are selected instead of smaller ones, whether they contain them, or enough of them, determined by min_overlap
+    
+    '''
+    final_faces = []
+    for cascade_str, faces in dict_of_list_of_cascade_results.iteritems():
+        # go through each cascade type
+        for face in faces:
+            if type(face) == CascadeResult:
+                new_res = face
+            else:
+                new_res = CascadeResult(face,cascade_type = cascade_str)
+            to_add = True
+            for old_index,old_res in enumerate(final_faces):
+                ratio = new_res.area / old_res.area
+                if ratio >1.0:
+                    # new_box is bigger
+                    if new_res.overlap(old_res)/old_res.area > min_overlap:
+                        # the new box contains the old one, we want to replace it:
+                        final_faces[old_index] = new_res
+                        to_add = False
+                        break
+                if ratio <=1.0:
+                    # the new_box is smaller
+                    if new_res.overlap(old_res)/new_res.area > min_overlap:
+                        # the old box contains the new one, we therefore dont need to add the new box:
+                        to_add = False
+                        break
+            if to_add:
+                # if there was no hit, this is a new face, we can add it
+                final_faces.append(new_res)
+    return final_faces
+
+def most_centered_box( cascade_results, ( rows, cols ) ):
+    best_err = 1e10
+    for i, cascade in enumerate( cascade_results ):
+        err = ( cascade.x + cascade.dx / 2 - cols / 2 ) ** 2 + ( cascade.y + cascade.dy / 2 - rows / 2 ) ** 2
+        if err < best_err:
+            index = i
+    return cascade_results[ index ]
+        
+class CascadeResult(object):
+    def __init__(self, box_with_score, cascade_type = None, angle = 0):
+        self.x = box_with_score[0][0]
+        self.y = box_with_score[0][1]
+        self.dx = box_with_score[0][2]
+        self.dy = box_with_score[0][3]
+        self.score = box_with_score[1]
+        self.cascade_type = cascade_type
+        self.angle = angle
+        
+    @staticmethod
+    def from_polygon_points(points, score, cascade_type = None):
+        '''
+        an alternative generator, allows giving the polygon points instead of [x,y,dx,dy]
+        '''
+        x = points[0,0]
+        y = points[0,1]
+        top = points[1,] - points[0,]
+        left = points[3,] - points[0,]
+        dx = math.sqrt(sum([i*i for i in top]))
+        dy = math.sqrt(sum([i*i for i in left]))
+        angle = math.atan(float(top[1])/top[0]) * 180 / math.pi if top[0] != 0 else (970 if top[1] >0 else -90)
+        return CascadeResult(([x,y,dx,dy],score), cascade_type, angle)
+        
+    
+    def __str__(self):
+        return ''.join([str(x) for x in ['center:',self.center,
+                                         ',\nx:',self.x,
+                                         ',\ny:',self.y,
+                                         ',\ndx:',self.dx,
+                                         ',\ndy:',self.dy,
+                                         ',\nscore:',self.score,
+                                         ',\nangle:',self.angle,
+                                         ',\ncascade_type:',self.cascade_type,
+                                         ',\npoints_int:\n',self.points_int
+                                         ]])
+    
+    @property
+    def points(self):
+        x = self.x
+        y = self.y
+        dx = self.dx
+        dy = self.dy
+        a = self.angle/180.0*math.pi
+        dots = np.matrix([[x,y,1],[x+dx,y,1],[x+dx,y+dy,1],[x,y+dy,1]])
+        dots = dots.transpose()
+        rot_mat = cv2.getRotationMatrix2D((dots[0,0],dots[1,0]), -self.angle, scale = 1.0)
+        points = rot_mat * dots
+        points = points.transpose() 
+        return points
+
+    @property
+    def center(self):
+        return tuple(int(x) for x in (self.points.sum(0)/4.0).tolist()[0])
+    
+    @property
+    def points_int(self):
+        return self.points.astype(int)
+        
+    @property
+    def score_with_type(self):
+        if self.cascade_type:
+            return self.cascade_type + ' ' + str(self.score)
+        else:
+            return str(self.score)
+        
+    @property
+    def filename_encode(self):
+        
+        return '_'.join([str(x) for x in ['loct'] + self.cvformat_result[0] + ['ang', int(self.angle),self.cascade_type, self.score]]) 
+    
+    @property
+    def cvformat_coords(self):
+        if self.angle == 0:
+            return [int(x) for x in [self.x, self.y, self.dx, self.dy]]
+        else:
+            raise Exception('cannot return [x,y,dx,dy] for a box with angle, use cvformat_result() instead')
+        
+    @property
+    def cvformat_result(self):
+        return ([int(x) for x in [self.x, self.y, self.dx, self.dy]], self.score, self.angle)
+    
+#    @property
+#    def rot_matrix(self):
+#        return array([[cos(math.radians(self.angle)), -sin(math.radians(self.angle))], 
+#                   [sin(math.radians(self.angle)),  cos(math.radians(self.angle))]])
+
+    @property
+    def top_left(self):
+        return tuple(self.points[0,].tolist()[0])
+    
+    @property
+    def top_right(self):
+        return tuple(self.points[1,].tolist()[0])
+
+    @property
+    def bottom_right(self):
+        return tuple(self.points[2,].tolist()[0])
+
+    @property
+    def bottom_left(self):
+        return tuple(self.points[3,].tolist()[0])
+    
+    @property
+    def polygon(self):
+        return Polygon([self.top_left, self.top_right, self.bottom_right, self.bottom_left])
+        
+    def overlap(self, otherRect):      
+        return float(self.polygon.intersection(otherRect.polygon).area)
+    
+    @property
+    def area(self):
+        return float(self.polygon.area)
+    
+    def __gt__(self,b):
+        return self.area>b.area
+    def __ge__(self,b):
+        return self.area>=b.area
+    def __lt__(self,b):
+        return self.area<b.area    
+    def __le__(self,b):
+        return self.area<=b.area
+            
+        

adience_align-master/adiencealign(old)/cascade_detection/cascade_face_finder.py

@@ -0,0 +1,153 @@
+'''
+Created on May 7, 2014
+
+@author: eran
+'''
+from adiencealign.common.images import extract_box
+import glob
+import os
+import time
+from adiencealign.cascade_detection.cascade_detector import CascadeDetector,\
+    resolve_boxes, CascadeResult
+import cv2
+import csv
+'''
+Created on Dec 18, 2013
+
+@author: eran
+'''
+'''
+Created on Nov 26, 2013
+
+@author: eran
+'''
+
+class CascadeFaceFinder(object):
+    
+    def __init__(self, 
+                 min_size = 32, 
+                 drawn_target_res = 360*360, 
+                 hangles = [0, -22, 22], 
+                 langles = [0,-45,-22,22,45], 
+                 haar_file = 'haarcascade_frontalface_default.xml',
+                 lbp_file = 'lbpcascade_frontalface.xml'):
+        '''
+        finder = CascadeFaceFinder(min_size = 32, drawn_target_res = 360*360, hangles = [0], langles = [0,-45,-22,22,45], parts_threshold = 0)
+        
+        finder.get_faces_in_folder(input_folder, output_dir, drawn_folder, is_small_drawn)
+        
+        or
+        
+        finder.get_faces_in_photo(full_file, output_dir, drawn_folder, is_small_drawn)
+        '''
+        self.min_size = (min_size,min_size)
+        self.drawn_target_res = drawn_target_res
+        self._hangles = hangles
+        self._langles = langles
+        self.recalc_detectors(haar_file, lbp_file)
+        
+#         self.funnel = FaceFunnel()
+        
+    @property
+    def hangles(self):
+        return self._hangles
+    
+    @hangles.setter
+    def hangles(self,hangles):
+        self._hangles = hangles
+        self.recalc_detectors()
+
+    @property
+    def langles(self):
+        return self._langles
+    
+    @langles.setter
+    def langles(self,langles):
+        self._langles = langles
+        self.recalc_detectors()
+        
+    def recalc_detectors(self, haar_file, lbp_file):
+        self.haar_dtct = CascadeDetector(cascade_file = haar_file,
+                                      min_size = self.min_size,
+                                      min_neighbors = 20,
+                                      scale_factor = 1.03,
+                                      cascade_type = 'haar',
+                                      thr = 0.4,
+                                      angles = self.hangles)
+    
+        self.lbp_dtct = CascadeDetector(cascade_file = lbp_file,
+                                   min_size = self.min_size,
+                                   min_neighbors = 15,
+                                   scale_factor = 1.04,
+                                   cascade_type = 'lbp',
+                                   thr = 0.4,
+                                   angles = self.langles)
+        
+        
+    def get_faces_list_in_photo(self, img):
+        if self.hangles:
+                haar_faces = self.haar_dtct.detectWithAngles(img, resolve = True)
+        else:
+            haar_faces = []
+        lbp_faces = self.lbp_dtct.detectWithAngles(img, resolve = True)
+        faces = resolve_boxes({'haar':haar_faces, 'lbp':lbp_faces}, min_overlap = 0.6)
+        
+        return faces
+    
+    def create_faces_file(self, fname, is_overwrite = False, target_file = None):
+        '''
+        Runs facial detection on fname (say a.jpg, or a.png), and creates a results file (a.faces.txt)
+        
+        target_file - override, and specify a specific target file
+        is_overwrite - allow overwriting an existing results file
+        '''
+        faces = self.get_faces_list_in_photo(cv2.imread(fname))
+        results_file = fname.rsplit('.',1)[0] + '.faces.txt' if target_file is None else target_file
+        
+        if os.path.exists(results_file) and not is_overwrite:
+            print("Warning, faces result file", results_file, "exists")
+        else:
+            with open(results_file,'w') as csvfile:
+                csv_writer = csv.writer(csvfile, delimiter=',')
+                header = ['x', 'y','dx','dy', 'score', 'angle', 'type']
+                csv_writer.writerow(header)
+                for face in faces:
+                    csv_writer.writerow([str(i) for i in [int(face.x), int(face.y), int(face.dx), int(face.dy), face.score, face.angle, face.cascade_type]])
+        return results_file
+    
+    def get_sub_images_from_file(self,original_image_file, faces_file):
+        '''
+        extracts all the face sub-images from an image file, based on the results in a faces file
+        
+        returns - the list of face images (numpy arrays)
+        '''
+        img = cv2.imread(original_image_file)
+        faces_reader = csv.reader(open(faces_file))
+        next(faces_reader)  # discard the headings
+        padded_face_images = []
+        for line in faces_reader:
+            x, y, dx, dy, score, angle, cascade_type = line
+            [x,y,dx,dy,score, angle] = [int(float(i)) for i in [x,y,dx,dy,score, angle]]
+            face = CascadeResult(([x,y,dx,dy], score), cascade_type, angle)
+            padded_face, bounding_box_in_padded_face, _, _ = extract_box(img, face, padding_factor = 0.25)
+            padded_face_images.append(padded_face)
+        return padded_face_images
+    
+    def create_sub_images_from_file(self, original_image_file, faces_file, target_folder = None, img_type = 'png'):
+        '''
+        reads a faces file, created by "self.create_faces_file" and extracts padded faces from the original image
+        The faces will be created in the same folder as the faces file, unless specified otherwise by "target_folder"
+        
+        returns - the list of face files (strings)
+        '''
+        target_folder = os.path.split(faces_file)[0] if target_folder is None else target_folder
+        padded_face_images = self.get_sub_images_from_file(original_image_file, faces_file)
+        
+        base_image_name = os.path.split(faces_file)[1].split('.')[0]
+        face_files = []
+        for n_face, face_img in enumerate(padded_face_images):
+            face_file = os.path.join(target_folder, base_image_name + '_face_%d.%s' %(n_face, img_type))
+            cv2.imwrite( face_file , face_img )
+            face_files.append(face_file)
+        return face_files
+        

adience_align-master/adiencealign(old)/cascade_detection/cascade_face_finder.py.bak

@@ -0,0 +1,153 @@
+'''
+Created on May 7, 2014
+
+@author: eran
+'''
+from adiencealign.common.images import extract_box
+import glob
+import os
+import time
+from adiencealign.cascade_detection.cascade_detector import CascadeDetector,\
+    resolve_boxes, CascadeResult
+import cv2
+import csv
+'''
+Created on Dec 18, 2013
+
+@author: eran
+'''
+'''
+Created on Nov 26, 2013
+
+@author: eran
+'''
+
+class CascadeFaceFinder(object):
+    
+    def __init__(self, 
+                 min_size = 32, 
+                 drawn_target_res = 360*360, 
+                 hangles = [0, -22, 22], 
+                 langles = [0,-45,-22,22,45], 
+                 haar_file = 'haarcascade_frontalface_default.xml',
+                 lbp_file = 'lbpcascade_frontalface.xml'):
+        '''
+        finder = CascadeFaceFinder(min_size = 32, drawn_target_res = 360*360, hangles = [0], langles = [0,-45,-22,22,45], parts_threshold = 0)
+        
+        finder.get_faces_in_folder(input_folder, output_dir, drawn_folder, is_small_drawn)
+        
+        or
+        
+        finder.get_faces_in_photo(full_file, output_dir, drawn_folder, is_small_drawn)
+        '''
+        self.min_size = (min_size,min_size)
+        self.drawn_target_res = drawn_target_res
+        self._hangles = hangles
+        self._langles = langles
+        self.recalc_detectors(haar_file, lbp_file)
+        
+#         self.funnel = FaceFunnel()
+        
+    @property
+    def hangles(self):
+        return self._hangles
+    
+    @hangles.setter
+    def hangles(self,hangles):
+        self._hangles = hangles
+        self.recalc_detectors()
+
+    @property
+    def langles(self):
+        return self._langles
+    
+    @langles.setter
+    def langles(self,langles):
+        self._langles = langles
+        self.recalc_detectors()
+        
+    def recalc_detectors(self, haar_file, lbp_file):
+        self.haar_dtct = CascadeDetector(cascade_file = haar_file,
+                                      min_size = self.min_size,
+                                      min_neighbors = 20,
+                                      scale_factor = 1.03,
+                                      cascade_type = 'haar',
+                                      thr = 0.4,
+                                      angles = self.hangles)
+    
+        self.lbp_dtct = CascadeDetector(cascade_file = lbp_file,
+                                   min_size = self.min_size,
+                                   min_neighbors = 15,
+                                   scale_factor = 1.04,
+                                   cascade_type = 'lbp',
+                                   thr = 0.4,
+                                   angles = self.langles)
+        
+        
+    def get_faces_list_in_photo(self, img):
+        if self.hangles:
+                haar_faces = self.haar_dtct.detectWithAngles(img, resolve = True)
+        else:
+            haar_faces = []
+        lbp_faces = self.lbp_dtct.detectWithAngles(img, resolve = True)
+        faces = resolve_boxes({'haar':haar_faces, 'lbp':lbp_faces}, min_overlap = 0.6)
+        
+        return faces
+    
+    def create_faces_file(self, fname, is_overwrite = False, target_file = None):
+        '''
+        Runs facial detection on fname (say a.jpg, or a.png), and creates a results file (a.faces.txt)
+        
+        target_file - override, and specify a specific target file
+        is_overwrite - allow overwriting an existing results file
+        '''
+        faces = self.get_faces_list_in_photo(cv2.imread(fname))
+        results_file = fname.rsplit('.',1)[0] + '.faces.txt' if target_file is None else target_file
+        
+        if os.path.exists(results_file) and not is_overwrite:
+            print "Warning, faces result file", results_file, "exists"
+        else:
+            with open(results_file,'w') as csvfile:
+                csv_writer = csv.writer(csvfile, delimiter=',')
+                header = ['x', 'y','dx','dy', 'score', 'angle', 'type']
+                csv_writer.writerow(header)
+                for face in faces:
+                    csv_writer.writerow([str(i) for i in [int(face.x), int(face.y), int(face.dx), int(face.dy), face.score, face.angle, face.cascade_type]])
+        return results_file
+    
+    def get_sub_images_from_file(self,original_image_file, faces_file):
+        '''
+        extracts all the face sub-images from an image file, based on the results in a faces file
+        
+        returns - the list of face images (numpy arrays)
+        '''
+        img = cv2.imread(original_image_file)
+        faces_reader = csv.reader(open(faces_file))
+        faces_reader.next()  # discard the headings
+        padded_face_images = []
+        for line in faces_reader:
+            x, y, dx, dy, score, angle, cascade_type = line
+            [x,y,dx,dy,score, angle] = [int(float(i)) for i in [x,y,dx,dy,score, angle]]
+            face = CascadeResult(([x,y,dx,dy], score), cascade_type, angle)
+            padded_face, bounding_box_in_padded_face, _, _ = extract_box(img, face, padding_factor = 0.25)
+            padded_face_images.append(padded_face)
+        return padded_face_images
+    
+    def create_sub_images_from_file(self, original_image_file, faces_file, target_folder = None, img_type = 'png'):
+        '''
+        reads a faces file, created by "self.create_faces_file" and extracts padded faces from the original image
+        The faces will be created in the same folder as the faces file, unless specified otherwise by "target_folder"
+        
+        returns - the list of face files (strings)
+        '''
+        target_folder = os.path.split(faces_file)[0] if target_folder is None else target_folder
+        padded_face_images = self.get_sub_images_from_file(original_image_file, faces_file)
+        
+        base_image_name = os.path.split(faces_file)[1].split('.')[0]
+        face_files = []
+        for n_face, face_img in enumerate(padded_face_images):
+            face_file = os.path.join(target_folder, base_image_name + '_face_%d.%s' %(n_face, img_type))
+            cv2.imwrite( face_file , face_img )
+            face_files.append(face_file)
+        return face_files
+        

adience_align-master/adiencealign(old)/common/drawing.py

@@ -0,0 +1,33 @@
+'''
+Created on May 7, 2014
+
+@author: eran
+'''
+import cv2
+from adiencealign.cascade_detection.cascade_detector import CascadeResult
+import numpy as np
+
+def draw_rect(img, r, angle = 0, color=(255,255,255), thickness = 4, alpha = 0.5):
+    '''
+    accepts:
+    1. a (x,y,dx,dy) list
+    4. a [(x,y,dx,dy),score] list, as returned by cv2.CascadeClassifier.detectMultiScaleWithScores()
+    5. a CascadeResult object
+    '''
+    if type(r) == CascadeResult:
+        color = tuple(list(color) + [alpha])
+        cv2.polylines(img, pts = [r.points_int], isClosed = True, color = color, thickness = thickness)
+        return
+    elif len(r)==4 or len(r)==2: # [x,y,dx,dy]
+        if len(r)==2:
+            if len(r[0]) == 4:
+                r = r[0]
+            else: 
+                raise Exception("bad input to draw_rect...")
+        pt1 = int(round(r[0])), int(round(r[1]))
+        pt2 = int(round(r[0]+r[2])), int(round(r[1]+r[3]))
+        color = tuple(list(color) + [alpha])
+        cv2.rectangle(img, pt1, pt2, color, thickness = thickness)
+    else:
+        raise Exception("bad input to draw_rect...")
+    return

adience_align-master/adiencealign(old)/common/files.py

@@ -0,0 +1,22 @@
+'''
+Created on May 7, 2014
+
+@author: eran
+'''
+import os
+import shutil
+from os.path import expanduser
+
+def make_path(path, delete_content_if_exists = False):
+    if not os.path.exists(path):
+        os.makedirs(path)
+    elif delete_content_if_exists:
+        shutil.rmtree(path)
+        
+def expand_path(file_or_path):
+    if file_or_path.startswith('~/'):
+        home = expanduser("~")
+        file_at = os.path.join(home,file_or_path[2:])
+        return file_at
+    else:
+        return file_or_path        

adience_align-master/adiencealign(old)/common/images.py

@@ -0,0 +1,87 @@
+'''
+Created on May 7, 2014
+
+@author: eran
+'''
+import math
+import numpy as np
+import cv2
+
+
+
+def pad_image_for_rotation(img):
+    # we pad the image so, when we rotate it, it would never be clipped
+    rot_y,rot_x = img.shape[:2]
+    rot_x = int(rot_x / 2.0)
+    rot_y = int(rot_y / 2.0)
+    diag = int(math.sqrt(sum([math.pow(x,2) for x in img.shape])))
+    diag = int(math.ceil(diag / 2.0) * 2.0) # make sure it is even
+    if len(img.shape) == 3:
+        big_image = np.zeros((diag, diag, 3), dtype=np.uint8)
+    else:
+        big_image = np.zeros((diag, diag), dtype=np.uint8)
+    x_shift = int(diag/2-rot_x)
+    y_shift = int(diag/2-rot_y)
+    # the shift of the old image within big_image
+    if len(img.shape) == 3:
+        big_image[y_shift :y_shift+img.shape[0], x_shift:x_shift+img.shape[1], :] = img
+    else:
+        big_image[y_shift :y_shift+img.shape[0], x_shift:x_shift+img.shape[1]] = img
+    
+    # the rotation center is no the radius (half the old image diagonal)
+    rot_center = diag/2, diag/2
+    return big_image, x_shift, y_shift, diag, rot_center
+
+
+def extract_rect(img, rect, factor = 0.2):
+    (x,y,dx,dy) = rect
+    new_x = max(0, int(x-dx*factor))
+    new_y = max(0, int(y-dy*factor))
+    new_dx = min(int(dx+2*factor*dx), img.shape[1] - new_x)
+    new_dy = min(int(dy+2*factor*dy), img.shape[0] - new_y)
+    Dx = x - new_x
+    Dy = y - new_y
+    #return [new_x, new_y, new_dx, new_dy]
+    return img[new_y:new_y+new_dy,new_x:new_x+new_dx,:], Dx, Dy
+
+
+def extract_box(img, box, padding_factor = 0.2):
+    '''
+    we can search for whatever we want in the rotated bordered image, 
+    
+    Any point found can be translated back to the original image by:
+    1. adding the origins of the bordered area,
+    2. rotating the point using the inverse rotation matrix    
+    
+    '''
+    
+    if box.angle != 0:
+        
+        b_w = max(img.shape)*2
+        b_h = b_w
+        dx_center = b_w / 2 - box.center[0]
+        dy_center = b_h / 2 - box.center[1]
+        new_img = np.zeros((b_w, b_h, 3), dtype = img.dtype)
+        new_img[dy_center:(dy_center + img.shape[0]), dx_center:(dx_center + img.shape[1]), :] = img
+        
+        box_in_big_image = box.points + np.c_[np.ones((4,1)) * dx_center, np.ones((4,1)) * dy_center]
+
+        rot_mat = cv2.getRotationMatrix2D((b_w/2, b_h/2), box.angle, scale = 1.0)
+        inv_rot_mat = cv2.invertAffineTransform(rot_mat)
+        rot_image = cv2.warpAffine(new_img, rot_mat, (new_img.shape[1],new_img.shape[0]), flags=cv2.INTER_CUBIC)
+        box_UL_in_rotated = (rot_mat * np.matrix([box_in_big_image[0,0], box_in_big_image[0,1], 1]).transpose()).transpose().tolist()[0] 
+        box_coords_in_rotated = np.matrix(np.c_[box_in_big_image, np.ones((4,1))]) * rot_mat.T
+        box_coords_in_rotated = box_coords_in_rotated[0,:].tolist()[0] + [box.dx, box.dy]
+    else:
+        rot_mat = cv2.getRotationMatrix2D(box.center, box.angle, scale = 1.0)
+        inv_rot_mat = cv2.invertAffineTransform(rot_mat)
+        # for efficiency
+        rot_image = img.copy()
+        box_UL_in_rotated = (rot_mat * np.matrix([box.points[0,0], box.points[0,1], 1]).transpose()).transpose().tolist()[0] 
+        box_coords_in_rotated = box_UL_in_rotated + [box.dx, box.dy]
+    
+    img_with_border, Dx, Dy = extract_rect(rot_image, box_coords_in_rotated, padding_factor)
+    box_coords_in_bordered = [Dx, Dy] + [box.dx, box.dy]
+    border_UL_in_rotated = [box_UL_in_rotated[0]-Dx, box_UL_in_rotated[1]-Dy]
+    
+    return img_with_border, box_coords_in_bordered, border_UL_in_rotated, inv_rot_mat

adience_align-master/adiencealign(old)/common/landmarks.py

@@ -0,0 +1,113 @@
+'''
+Created on May 7, 2014
+
+@author: eran
+'''
+
+import csv
+import cv2
+from numpy import linalg
+import numpy as np
+
+
+WEIGHTS3 = [11.1088851746,15.8721645013,12.3189439894,15.9467104922,13.9265119716,17.2447706133,11.4118267639,17.0728365324,12.7831886739,17.1908773151,9.6639887492,13.8443342456,8.76890470223,11.4441704453,7.52083144762,10.3245662427,6.35563072919,7.55739887985,6.42340544936,7.48786881875,10.8720924456,8.1349958353,12.3664410374,9.58137800608,6.29390307208,9.47697088783,8.49859202931,9.43946799727,7.92920023102,10.6126442536,10.2953809171,11.299323189,11.1181958685,12.9374719654,12.3764338392,14.7823225327,13.086272904,16.0571795811,15.079169884,17.5936174739,8.39112414861,7.68532826996,8.89386612449,8.70173923211,10.0826620269,8.70286074207,8.13121344224,9.80805203263,7.76044090777,9.2502084627,7.61334683331,10.4813589698,8.64831020289,11.0452512508,9.19528177019,13.0171747152,10.1204323102,14.0189765809,11.0232436734,14.7355286373,12.4881579947,15.4279914333,11.5785971474,16.7942051778,12.4916161829,17.57726411,14.3422306002,19.3015061859,16.3109851665,23.7227227093,17.7687071538,22.6848438204,14.9879312002,18.6763354368,12.927920123,17.7652660198,10.3584444834,15.5584775245,10.660322225,15.4351684107,11.6468441007,13.7962556973,12.9019472625,16.6407866045,13.1946878458,16.4137518526,9.86525395127,11.6687513083,10.4858060411,12.8407630953,9.24210197996,10.9728479778,9.37639005327,12.3418022852,12.2786533953,12.0629300205,14.8495857728,15.4667996708,14.7414922143,15.2761005039,8.5837102275,10.8010609515,6.55275411638,14.4240347981,10.4200283162,17.6888997346,11.4480670185,22.4669420211,13.1705102756,29.3073334802,16.9922725597,35.4031969543,18.7102372238,41.7466671473,21.7036998929,47.1495172267,24.4179633642,51.9023425203,26.6870471848,57.5921966087,7.71654443362,18.3796425232,9.84932443383,23.3915673615,15.7135746598,31.5768046636,18.159161567,39.0502675506,20.5154926286,44.6961338521,22.8541610324,50.9071591504,26.5569627651,54.4338495899,29.1062390164,61.5990210977]
+
+def read_fidu(fidu_name):
+    fidu_reader = csv.reader(open(fidu_name))
+    try:
+        line = next(fidu_reader)
+        score, yaw_angle = line
+        next(fidu_reader)
+        fidu_points = [[int(float(field)) for field in row[-2:]] for row in fidu_reader]
+        return int(score), int(yaw_angle), fidu_points
+    except:
+        if line[0] == 'nothing found':
+            return -1000, None, None
+        else:
+            raise Exception('Corrupt Fidu File ' + fidu_name)
+        
+        
+def draw_fidu(img, fidu_points, radius = 3, color = (255,0,0), thickness = 1, draw_numbers_color = None):
+    '''
+    set draw_numbers_color = (COLOR_BLUE), for example, to draw the point numbers
+    '''
+    for n,point in enumerate(fidu_points):
+        cv2.circle(img, tuple([int(x) for x in point]),radius,color, thickness)
+        if draw_numbers_color:
+            cv2.putText( img, 
+              '%d' %n,
+             tuple([int(point[0])-4, int(point[1])-4]), 
+             cv2.FONT_HERSHEY_COMPLEX, 
+             0.35, 
+             draw_numbers_color, 
+             thickness = 1 )
+            
+            
+
+def _compute_affine_transform_cvpy(refpoints, points, w = None): # Copied from the book
+    if w == None:
+        w = [1] * (len(points) * 2)
+    assert(len(w) == 2*len(points))
+    y = []
+    for n, p in enumerate(refpoints):
+        y += [p[0]/w[n*2], p[1]/w[n*2+1]]
+    A = []
+    for n, p in enumerate(points):
+        A.extend([ [p[0]/w[n*2], p[1]/w[n*2], 0, 0, 1/w[n*2], 0], [0, 0, p[0]/w[n*2+1], p[1]/w[n*2+1], 0, 1/w[n*2+1]] ])
+    
+    lstsq = linalg.lstsq(A,y)
+    h11, h12, h21, h22, dx, dy = lstsq[0]
+    err = lstsq[1]
+
+    R = np.array([[h11, h12, dx], [h21, h22, dy]])
+    return R, err
+
+
+def _compute_affine_transform_ocvlsq(refpoints, points, w = None):
+    if w == None:
+        w = [1] * (len(points) * 2)
+    assert(len(w) == 2*len(points))
+    y = []
+    for n, p in enumerate(refpoints):
+        y += [p[0]/w[n*2], p[1]/w[n*2+1]]
+    A = []
+    for n, p in enumerate(points):
+        A.extend([ [p[0]/w[n*2], p[1]/w[n*2], 0, 0, 1/w[n*2], 0], [0, 0, p[0]/w[n*2+1], p[1]/w[n*2+1], 0, 1/w[n*2+1]] ])
+    
+    lstsq = cv2.solve(np.array(A), np.array(y), flags=cv2.DECOMP_SVD)
+    h11, h12, h21, h22, dx, dy = lstsq[1]
+    err = 0#lstsq[1]
+
+    #R = np.array([[h11, h12, dx], [h21, h22, dy]])
+    # The row above works too - but creates a redundant dimension
+    R = np.array([[h11[0], h12[0], dx[0]], [h21[0], h22[0], dy[0]]])
+    return R, err
+            
+def fidu_transform(fidu_model, fidu_points, weights, img, shift=(0.0,0.0), use_ocvlsq=False):
+    FIDU_SIZE = 544
+    
+    SHIFT_Y = int(FIDU_SIZE * shift[0])
+    SHIFT_X = int(FIDU_SIZE * shift[1])
+    if not use_ocvlsq:
+        R, err = _compute_affine_transform_cvpy(fidu_model, fidu_points, weights)
+    else:
+        R, err = _compute_affine_transform_ocvlsq(fidu_model, fidu_points, weights)
+
+    funneled_img = cv2.warpAffine(img, R, (FIDU_SIZE + SHIFT_Y*2, FIDU_SIZE + SHIFT_X*2), flags=cv2.INTER_CUBIC)
+    return funneled_img, R
+
+
+def shift_vector(points, shift):
+    FIDU_SIZE = 544
+    
+    SHIFT_Y = int(FIDU_SIZE * shift[0])
+    SHIFT_X = int(FIDU_SIZE * shift[1])
+    SHIFT = (SHIFT_X, SHIFT_Y)
+    s_points = [(p[0] + SHIFT[0], p[1] + SHIFT[1]) for p in points]
+    return s_points
+
+def unwarp_fidu(orig_fidu_points, unwarp_mat):    
+    points = np.array(orig_fidu_points).T
+    points_h = np.r_[points, np.ones((1,points.shape[1]))]
+    orig_fidu_points_in_aligned = np.dot(unwarp_mat,points_h).T.astype(np.int16)
+    return orig_fidu_points_in_aligned 

adience_align-master/adiencealign(old)/common/landmarks.py.bak

@@ -0,0 +1,113 @@
+'''
+Created on May 7, 2014
+
+@author: eran
+'''
+
+import csv
+import cv2
+from numpy import linalg
+import numpy as np
+
+
+WEIGHTS3 = [11.1088851746,15.8721645013,12.3189439894,15.9467104922,13.9265119716,17.2447706133,11.4118267639,17.0728365324,12.7831886739,17.1908773151,9.6639887492,13.8443342456,8.76890470223,11.4441704453,7.52083144762,10.3245662427,6.35563072919,7.55739887985,6.42340544936,7.48786881875,10.8720924456,8.1349958353,12.3664410374,9.58137800608,6.29390307208,9.47697088783,8.49859202931,9.43946799727,7.92920023102,10.6126442536,10.2953809171,11.299323189,11.1181958685,12.9374719654,12.3764338392,14.7823225327,13.086272904,16.0571795811,15.079169884,17.5936174739,8.39112414861,7.68532826996,8.89386612449,8.70173923211,10.0826620269,8.70286074207,8.13121344224,9.80805203263,7.76044090777,9.2502084627,7.61334683331,10.4813589698,8.64831020289,11.0452512508,9.19528177019,13.0171747152,10.1204323102,14.0189765809,11.0232436734,14.7355286373,12.4881579947,15.4279914333,11.5785971474,16.7942051778,12.4916161829,17.57726411,14.3422306002,19.3015061859,16.3109851665,23.7227227093,17.7687071538,22.6848438204,14.9879312002,18.6763354368,12.927920123,17.7652660198,10.3584444834,15.5584775245,10.660322225,15.4351684107,11.6468441007,13.7962556973,12.9019472625,16.6407866045,13.1946878458,16.4137518526,9.86525395127,11.6687513083,10.4858060411,12.8407630953,9.24210197996,10.9728479778,9.37639005327,12.3418022852,12.2786533953,12.0629300205,14.8495857728,15.4667996708,14.7414922143,15.2761005039,8.5837102275,10.8010609515,6.55275411638,14.4240347981,10.4200283162,17.6888997346,11.4480670185,22.4669420211,13.1705102756,29.3073334802,16.9922725597,35.4031969543,18.7102372238,41.7466671473,21.7036998929,47.1495172267,24.4179633642,51.9023425203,26.6870471848,57.5921966087,7.71654443362,18.3796425232,9.84932443383,23.3915673615,15.7135746598,31.5768046636,18.159161567,39.0502675506,20.5154926286,44.6961338521,22.8541610324,50.9071591504,26.5569627651,54.4338495899,29.1062390164,61.5990210977]
+
+def read_fidu(fidu_name):
+    fidu_reader = csv.reader(open(fidu_name))
+    try:
+        line = fidu_reader.next()
+        score, yaw_angle = line
+        fidu_reader.next()
+        fidu_points = [[int(float(field)) for field in row[-2:]] for row in fidu_reader]
+        return int(score), int(yaw_angle), fidu_points
+    except:
+        if line[0] == 'nothing found':
+            return -1000, None, None
+        else:
+            raise Exception('Corrupt Fidu File ' + fidu_name)
+        
+        
+def draw_fidu(img, fidu_points, radius = 3, color = (255,0,0), thickness = 1, draw_numbers_color = None):
+    '''
+    set draw_numbers_color = (COLOR_BLUE), for example, to draw the point numbers
+    '''
+    for n,point in enumerate(fidu_points):
+        cv2.circle(img, tuple([int(x) for x in point]),radius,color, thickness)
+        if draw_numbers_color:
+            cv2.putText( img, 
+              '%d' %n,
+             tuple([int(point[0])-4, int(point[1])-4]), 
+             cv2.FONT_HERSHEY_COMPLEX, 
+             0.35, 
+             draw_numbers_color, 
+             thickness = 1 )
+            
+            
+
+def _compute_affine_transform_cvpy(refpoints, points, w = None): # Copied from the book
+    if w == None:
+        w = [1] * (len(points) * 2)
+    assert(len(w) == 2*len(points))
+    y = []
+    for n, p in enumerate(refpoints):
+        y += [p[0]/w[n*2], p[1]/w[n*2+1]]
+    A = []
+    for n, p in enumerate(points):
+        A.extend([ [p[0]/w[n*2], p[1]/w[n*2], 0, 0, 1/w[n*2], 0], [0, 0, p[0]/w[n*2+1], p[1]/w[n*2+1], 0, 1/w[n*2+1]] ])
+    
+    lstsq = linalg.lstsq(A,y)
+    h11, h12, h21, h22, dx, dy = lstsq[0]
+    err = lstsq[1]
+
+    R = np.array([[h11, h12, dx], [h21, h22, dy]])
+    return R, err
+
+
+def _compute_affine_transform_ocvlsq(refpoints, points, w = None):
+    if w == None:
+        w = [1] * (len(points) * 2)
+    assert(len(w) == 2*len(points))
+    y = []
+    for n, p in enumerate(refpoints):
+        y += [p[0]/w[n*2], p[1]/w[n*2+1]]
+    A = []
+    for n, p in enumerate(points):
+        A.extend([ [p[0]/w[n*2], p[1]/w[n*2], 0, 0, 1/w[n*2], 0], [0, 0, p[0]/w[n*2+1], p[1]/w[n*2+1], 0, 1/w[n*2+1]] ])
+    
+    lstsq = cv2.solve(np.array(A), np.array(y), flags=cv2.DECOMP_SVD)
+    h11, h12, h21, h22, dx, dy = lstsq[1]
+    err = 0#lstsq[1]
+
+    #R = np.array([[h11, h12, dx], [h21, h22, dy]])
+    # The row above works too - but creates a redundant dimension
+    R = np.array([[h11[0], h12[0], dx[0]], [h21[0], h22[0], dy[0]]])
+    return R, err
+            
+def fidu_transform(fidu_model, fidu_points, weights, img, shift=(0.0,0.0), use_ocvlsq=False):
+    FIDU_SIZE = 544
+    
+    SHIFT_Y = int(FIDU_SIZE * shift[0])
+    SHIFT_X = int(FIDU_SIZE * shift[1])
+    if not use_ocvlsq:
+        R, err = _compute_affine_transform_cvpy(fidu_model, fidu_points, weights)
+    else:
+        R, err = _compute_affine_transform_ocvlsq(fidu_model, fidu_points, weights)
+
+    funneled_img = cv2.warpAffine(img, R, (FIDU_SIZE + SHIFT_Y*2, FIDU_SIZE + SHIFT_X*2), flags=cv2.INTER_CUBIC)
+    return funneled_img, R
+
+
+def shift_vector(points, shift):
+    FIDU_SIZE = 544
+    
+    SHIFT_Y = int(FIDU_SIZE * shift[0])
+    SHIFT_X = int(FIDU_SIZE * shift[1])
+    SHIFT = (SHIFT_X, SHIFT_Y)
+    s_points = [(p[0] + SHIFT[0], p[1] + SHIFT[1]) for p in points]
+    return s_points
+
+def unwarp_fidu(orig_fidu_points, unwarp_mat):    
+    points = np.array(orig_fidu_points).T
+    points_h = np.r_[points, np.ones((1,points.shape[1]))]
+    orig_fidu_points_in_aligned = np.dot(unwarp_mat,points_h).T.astype(np.int16)
+    return orig_fidu_points_in_aligned 

adience_align-master/adiencealign(old)/landmarks_detection/landmarks_detector.py

@@ -0,0 +1,20 @@
+'''
+Created on May 7, 2014
+
+@author: eran
+'''
+import subprocess
+import os
+
+def detect_landmarks(fname, max_size = 400*400, min_size = 50*50, fidu_exec_dir = os.path.abspath('../resources/')):
+    '''
+    optional flags:
+    max_size - If exceeds this pixel size, image is resized to that before landmark detection
+    min_size - If below this pixel size, image is resized to that before landmark detection
+    '''
+    max_size = str(max_size) if max_size is not None else ''
+    min_size = str(min_size) if min_size is not None else ''
+    fidu_cmd = './FiducialFaceDetector.sh Face_small_146filters_-0.65thr.xml %s %s %s' %(fname, min_size, max_size)    
+    print(fidu_cmd)
+    x = subprocess.call(fidu_cmd, shell=True, cwd = fidu_exec_dir)
+    return

adience_align-master/adiencealign(old)/landmarks_detection/landmarks_detector.py.bak

@@ -0,0 +1,20 @@
+'''
+Created on May 7, 2014
+
+@author: eran
+'''
+import subprocess
+import os
+
+def detect_landmarks(fname, max_size = 400*400, min_size = 50*50, fidu_exec_dir = os.path.abspath('../resources/')):
+    '''
+    optional flags:
+    max_size - If exceeds this pixel size, image is resized to that before landmark detection
+    min_size - If below this pixel size, image is resized to that before landmark detection
+    '''
+    max_size = str(max_size) if max_size is not None else ''
+    min_size = str(min_size) if min_size is not None else ''
+    fidu_cmd = './FiducialFaceDetector.sh Face_small_146filters_-0.65thr.xml %s %s %s' %(fname, min_size, max_size)    
+    print fidu_cmd
+    x = subprocess.call(fidu_cmd, shell=True, cwd = fidu_exec_dir)
+    return

adience_align-master/adiencealign(old)/pipeline/CascadeFaceAligner.py

@@ -0,0 +1,148 @@
+'''
+Created on May 8, 2014
+
+@author: eran
+'''
+from adiencealign.cascade_detection.cascade_face_finder import CascadeFaceFinder
+from adiencealign.affine_alignment.affine_aligner import AffineAligner
+import glob
+import os
+from adiencealign.landmarks_detection.landmarks_detector import detect_landmarks
+from adiencealign.common.landmarks import read_fidu, unwarp_fidu, draw_fidu
+import cv2
+
+class CascadeFaceAligner(object):
+    '''
+    classdocs
+    '''
+
+
+    def __init__(self, haar_file = '../resources/haarcascade_frontalface_default.xml', 
+                 lbp_file = '../resources/lbpcascade_frontalface.xml', 
+                 fidu_model_file = '../resources/model_ang_0.txt',
+                 fidu_exec_dir = '../resources/'):
+        '''
+        Constructor
+        '''
+        self.face_finder = CascadeFaceFinder(haar_file = haar_file,
+                                            lbp_file = lbp_file)
+        
+        self.aligner = AffineAligner(fidu_model_file = fidu_model_file)
+        self.fidu_exec_dir = fidu_exec_dir
+        
+        self.valid_angles = [-45,-30,-15,0,15,30,45]
+        
+    def detect_faces(self, input_folder, output_folder, mark_dones = True):
+        '''
+        mark_dones - if True, will create a hidden file, marking this file as done with a hidden file, starting with '.done.'
+        '''        
+        input_files1 = glob.glob(os.path.join(input_folder, '*.jpg'))
+        input_files2 = glob.glob(os.path.join(input_folder, '*.png'))
+        input_files = input_files1 + input_files2
+        N = len(input_files)
+        
+        for n_file, input_file in enumerate(input_files):
+            a,b = os.path.split(input_file)
+            done_file = os.path.join(a, '.done.' + b.rsplit('.',1)[0])
+            
+            if os.path.exists(done_file):
+                continue
+            
+            print("... processing", input_file)
+
+            target_faces_file = os.path.join( output_folder, os.path.split(input_file)[1].rsplit('.',1)[0] + '.faces.txt')
+            
+            faces_file = self.face_finder.create_faces_file( input_file, is_overwrite = False, target_file = target_faces_file )
+            sub_images_files = self.face_finder.create_sub_images_from_file( original_image_file = input_file, 
+                                                    faces_file = faces_file, 
+                                                    target_folder = output_folder,
+                                                    img_type = 'jpg')
+            #touch
+            open(done_file,'w').close()
+            print("Detected on %d / %d files" %(n_file, N))
+        
+    def align_faces(self, input_images, output_path, fidu_max_size = None, fidu_min_size = None, is_align = True, is_draw_fidu = False, delete_no_fidu = False):
+        '''
+        input_images - can be either a folder (all *.jpgs in it) or a list of filenames
+        
+        , fidu_max_size = None, fidu_min_size = None):
+        '''
+        if type(input_images) == type(''):
+            input_images = glob.glob(os.path.join(input_images, '*.jpg'))
+        
+        for input_image in input_images:
+            detect_landmarks(fname = os.path.abspath(input_image),
+                                 max_size = fidu_max_size,
+                                 min_size = fidu_min_size,
+                                 fidu_exec_dir = self.fidu_exec_dir)
+            
+            fidu_file = input_image.rsplit('.',1)[0] + '.cfidu'
+            fidu_score, yaw_angle, fidu_points = read_fidu(fidu_file)
+            
+            if not (fidu_score is not None and yaw_angle in self.valid_angles):
+                # skip face
+                if delete_no_fidu:
+                    os.remove(fidu_file)
+                    os.remove(input_image)
+                continue
+            
+            if is_align:
+                # save the aligned image
+                sub_img = cv2.imread(input_image)
+                _, base_fname = os.path.split(input_image) 
+                aligned_img, R = self.aligner.align(sub_img, fidu_points)
+                aligned_img_file = os.path.join(output_path, base_fname.rsplit('.',1)[0] + '.aligned.png')
+                cv2.imwrite(aligned_img_file, aligned_img)
+                
+                # save a copy of the aligned image, with the landmarks drawn
+                if is_draw_fidu:
+                    aligned_img_file = os.path.join(output_path, base_fname.rsplit('.',1)[0] + '.aligned.withpoints.png') 
+                    fidu_points_in_aligned = unwarp_fidu(orig_fidu_points = fidu_points, unwarp_mat = R)
+                    draw_fidu(aligned_img, fidu_points_in_aligned, radius = 9, color = (255,0,0), thickness = 3)
+                    cv2.imwrite(aligned_img_file, aligned_img)
+            
+#             
+#     def align_faces2(self, input_folder, output_folder, detect_landmarks = True, delete_no_fidu = True, is_align = True, is_draw_fidu = False, fidu_max_size = None, fidu_min_size = None):
+#         '''
+#         delete_no_fidu - deletes the .cfidu and sub_img if no fidu was found
+#         is_draw_fidu - creates a copy of the aligned images with the original fiducial points on it
+#         '''
+#         input_files = glob.glob(os.path.join(input_folder, '*'))
+#         for input_file in input_files:
+#             print "... processing", input_file
+#             target_faces_file = os.path.join( output_folder, os.path.split(input_file)[1].rsplit('.',1)[0] + '.faces.txt')
+#             
+#             faces_file = self.face_finder.create_faces_file( input_file, is_overwrite = False, target_file = target_faces_file )
+#             sub_images_files = self.face_finder.create_sub_images_from_file( original_image_file = input_file, 
+#                                                     faces_file = faces_file, 
+#                                                     target_folder = output_folder,
+#                                                     img_type = 'jpg')
+#             
+#             for sub_image_file in sub_images_files:
+#                 detect_landmarks(fname = os.path.abspath(sub_image_file),
+#                                  max_size = fidu_max_size,
+#                                  min_size = fidu_min_size,
+#                                  fidu_exec_dir = self.fidu_exec_dir)
+#                 
+#                 fidu_file = sub_image_file.rsplit('.',1)[0] + '.cfidu'
+#                 fidu_score, yaw_angle, fidu_points = read_fidu(fidu_file)
+#                 
+#                 if not (fidu_score is not None and yaw_angle in self.valid_angles):
+#                     # skip face
+#                     os.remove(fidu_file)
+#                     os.remove(sub_image_file)
+#                     continue
+#                 
+#                 if is_align:
+#                     # save the aligned image
+#                     sub_img = cv2.imread(sub_image_file)
+#                     aligned_img, R = self.aligner.align(sub_img, fidu_points)
+#                     aligned_img_file = sub_image_file.rsplit('.',1)[0] + '.aligned.png'
+#                     cv2.imwrite(aligned_img_file, aligned_img)
+#                     
+#                     # save a copy of the aligned image, with the landmarks drawn
+#                     if is_draw_fidu:
+#                         aligned_img_file = sub_image_file.rsplit('.',1)[0] + '.aligned.withpoints.png'
+#                         fidu_points_in_aligned = unwarp_fidu(orig_fidu_points = fidu_points, unwarp_mat = R)
+#                         draw_fidu(aligned_img, fidu_points_in_aligned, radius = 9, color = (255,0,0), thickness = 3)
+#                         cv2.imwrite(aligned_img_file, aligned_img)

adience_align-master/adiencealign(old)/pipeline/CascadeFaceAligner.py.bak

@@ -0,0 +1,148 @@
+'''
+Created on May 8, 2014
+
+@author: eran
+'''
+from adiencealign.cascade_detection.cascade_face_finder import CascadeFaceFinder
+from adiencealign.affine_alignment.affine_aligner import AffineAligner
+import glob
+import os
+from adiencealign.landmarks_detection.landmarks_detector import detect_landmarks
+from adiencealign.common.landmarks import read_fidu, unwarp_fidu, draw_fidu
+import cv2
+
+class CascadeFaceAligner(object):
+    '''
+    classdocs
+    '''
+
+
+    def __init__(self, haar_file = '../resources/haarcascade_frontalface_default.xml', 
+                 lbp_file = '../resources/lbpcascade_frontalface.xml', 
+                 fidu_model_file = '../resources/model_ang_0.txt',
+                 fidu_exec_dir = '../resources/'):
+        '''
+        Constructor
+        '''
+        self.face_finder = CascadeFaceFinder(haar_file = haar_file,
+                                            lbp_file = lbp_file)
+        
+        self.aligner = AffineAligner(fidu_model_file = fidu_model_file)
+        self.fidu_exec_dir = fidu_exec_dir
+        
+        self.valid_angles = [-45,-30,-15,0,15,30,45]
+        
+    def detect_faces(self, input_folder, output_folder, mark_dones = True):
+        '''
+        mark_dones - if True, will create a hidden file, marking this file as done with a hidden file, starting with '.done.'
+        '''        
+        input_files1 = glob.glob(os.path.join(input_folder, '*.jpg'))
+        input_files2 = glob.glob(os.path.join(input_folder, '*.png'))
+        input_files = input_files1 + input_files2
+        N = len(input_files)
+        
+        for n_file, input_file in enumerate(input_files):
+            a,b = os.path.split(input_file)
+            done_file = os.path.join(a, '.done.' + b.rsplit('.',1)[0])
+            
+            if os.path.exists(done_file):
+                continue
+            
+            print "... processing", input_file
+
+            target_faces_file = os.path.join( output_folder, os.path.split(input_file)[1].rsplit('.',1)[0] + '.faces.txt')
+            
+            faces_file = self.face_finder.create_faces_file( input_file, is_overwrite = False, target_file = target_faces_file )
+            sub_images_files = self.face_finder.create_sub_images_from_file( original_image_file = input_file, 
+                                                    faces_file = faces_file, 
+                                                    target_folder = output_folder,
+                                                    img_type = 'jpg')
+            #touch
+            open(done_file,'w').close()
+            print "Detected on %d / %d files" %(n_file, N)
+        
+    def align_faces(self, input_images, output_path, fidu_max_size = None, fidu_min_size = None, is_align = True, is_draw_fidu = False, delete_no_fidu = False):
+        '''
+        input_images - can be either a folder (all *.jpgs in it) or a list of filenames
+        
+        , fidu_max_size = None, fidu_min_size = None):
+        '''
+        if type(input_images) == type(''):
+            input_images = glob.glob(os.path.join(input_images, '*.jpg'))
+        
+        for input_image in input_images:
+            detect_landmarks(fname = os.path.abspath(input_image),
+                                 max_size = fidu_max_size,
+                                 min_size = fidu_min_size,
+                                 fidu_exec_dir = self.fidu_exec_dir)
+            
+            fidu_file = input_image.rsplit('.',1)[0] + '.cfidu'
+            fidu_score, yaw_angle, fidu_points = read_fidu(fidu_file)
+            
+            if not (fidu_score is not None and yaw_angle in self.valid_angles):
+                # skip face
+                if delete_no_fidu:
+                    os.remove(fidu_file)
+                    os.remove(input_image)
+                continue
+            
+            if is_align:
+                # save the aligned image
+                sub_img = cv2.imread(input_image)
+                _, base_fname = os.path.split(input_image) 
+                aligned_img, R = self.aligner.align(sub_img, fidu_points)
+                aligned_img_file = os.path.join(output_path, base_fname.rsplit('.',1)[0] + '.aligned.png')
+                cv2.imwrite(aligned_img_file, aligned_img)
+                
+                # save a copy of the aligned image, with the landmarks drawn
+                if is_draw_fidu:
+                    aligned_img_file = os.path.join(output_path, base_fname.rsplit('.',1)[0] + '.aligned.withpoints.png') 
+                    fidu_points_in_aligned = unwarp_fidu(orig_fidu_points = fidu_points, unwarp_mat = R)
+                    draw_fidu(aligned_img, fidu_points_in_aligned, radius = 9, color = (255,0,0), thickness = 3)
+                    cv2.imwrite(aligned_img_file, aligned_img)
+            
+#             
+#     def align_faces2(self, input_folder, output_folder, detect_landmarks = True, delete_no_fidu = True, is_align = True, is_draw_fidu = False, fidu_max_size = None, fidu_min_size = None):
+#         '''
+#         delete_no_fidu - deletes the .cfidu and sub_img if no fidu was found
+#         is_draw_fidu - creates a copy of the aligned images with the original fiducial points on it
+#         '''
+#         input_files = glob.glob(os.path.join(input_folder, '*'))
+#         for input_file in input_files:
+#             print "... processing", input_file
+#             target_faces_file = os.path.join( output_folder, os.path.split(input_file)[1].rsplit('.',1)[0] + '.faces.txt')
+#             
+#             faces_file = self.face_finder.create_faces_file( input_file, is_overwrite = False, target_file = target_faces_file )
+#             sub_images_files = self.face_finder.create_sub_images_from_file( original_image_file = input_file, 
+#                                                     faces_file = faces_file, 
+#                                                     target_folder = output_folder,
+#                                                     img_type = 'jpg')
+#             
+#             for sub_image_file in sub_images_files:
+#                 detect_landmarks(fname = os.path.abspath(sub_image_file),
+#                                  max_size = fidu_max_size,
+#                                  min_size = fidu_min_size,
+#                                  fidu_exec_dir = self.fidu_exec_dir)
+#                 
+#                 fidu_file = sub_image_file.rsplit('.',1)[0] + '.cfidu'
+#                 fidu_score, yaw_angle, fidu_points = read_fidu(fidu_file)
+#                 
+#                 if not (fidu_score is not None and yaw_angle in self.valid_angles):
+#                     # skip face
+#                     os.remove(fidu_file)
+#                     os.remove(sub_image_file)
+#                     continue
+#                 
+#                 if is_align:
+#                     # save the aligned image
+#                     sub_img = cv2.imread(sub_image_file)
+#                     aligned_img, R = self.aligner.align(sub_img, fidu_points)
+#                     aligned_img_file = sub_image_file.rsplit('.',1)[0] + '.aligned.png'
+#                     cv2.imwrite(aligned_img_file, aligned_img)
+#                     
+#                     # save a copy of the aligned image, with the landmarks drawn
+#                     if is_draw_fidu:
+#                         aligned_img_file = sub_image_file.rsplit('.',1)[0] + '.aligned.withpoints.png'
+#                         fidu_points_in_aligned = unwarp_fidu(orig_fidu_points = fidu_points, unwarp_mat = R)
+#                         draw_fidu(aligned_img, fidu_points_in_aligned, radius = 9, color = (255,0,0), thickness = 3)
+#                         cv2.imwrite(aligned_img_file, aligned_img)

adience_align-master/adiencealign(old)/resources/FiducialFaceDetector.sh

@@ -0,0 +1,3 @@
+#!/bin/sh
+export LD_LIBRARY_PATH=/usr/local/lib:.
+"./FiducialFaceDetector" $1 $2 $3 $4

adience_align-master/adiencealign(old)/resources/model_ang_0.txt

@@ -0,0 +1,68 @@
+1,272,272
+2,252,272
+3,232,272
+4,292,272
+5,312,272
+6,272,232
+7,272,192
+8,272,152
+9,272,112
+10,212,112
+11,192,112
+12,172,112
+13,192,92
+14,172,92
+15,152,112
+16,112,72
+17,132,52
+18,172,52
+19,212,52
+20,252,52
+21,332,112
+22,352,112
+23,372,112
+24,352,92
+25,372,92
+26,392,112
+27,432,72
+28,412,52
+29,372,52
+30,332,52
+31,292,52
+32,272,312
+33,252,312
+34,232,332
+35,212,352
+36,232,352
+37,252,332
+38,272,332
+39,312,312
+40,332,332
+41,352,352
+42,332,352
+43,312,332
+44,332,372
+45,312,352
+46,312,372
+47,272,352
+48,252,372
+49,252,352
+50,232,372
+51,272,372
+52,272,492
+53,212,492
+54,172,472
+55,132,432
+56,92,392
+57,72,332
+58,52,272
+59,52,212
+60,52,152
+61,332,472
+62,372,452
+63,412,412
+64,452,372
+65,472,312
+66,472,252
+67,472,192
+68,472,132

adience_align-master/adiencealign(old)/tests/clear_test.sh

@@ -0,0 +1,6 @@
+rm outputs/pipeline/faces/*
+rm outputs/pipeline/aligned/*
+rm outputs/cascade/1/*
+rm outputs/cascade/2/*
+
+rm resources/pipeline/.done*

adience_align-master/adiencealign(old)/tests/clear_test.sh~

@@ -0,0 +1,4 @@
+rm outputs/pipeline/faces/*
+rm outputs/pipeline/aligned/*
+rm outputs/cascade/1/*
+rm outputs/cascade/2/*

adience_align-master/adiencealign(old)/tests/outputs/cascade/2/Fayssal_Mekdad_0002.jpg.faces.txt

@@ -0,0 +1,2 @@
+x,y,dx,dy,score,angle,type
+61,62,132,132,344,0.0,haar

adience_align-master/adiencealign(old)/tests/outputs/cascade/2/family-home.png.faces.txt

@@ -0,0 +1,5 @@
+x,y,dx,dy,score,angle,type
+327,101,121,121,154,0.0,lbp
+237,106,113,113,139,0.0,lbp
+164,49,91,91,49,0.0,lbp
+434,86,94,94,95,0.0,lbp

adience_align-master/adiencealign(old)/tests/outputs/pipeline/faces/Fayssal_Mekdad_0002.faces.txt

@@ -0,0 +1,2 @@
+x,y,dx,dy,score,angle,type
+61,62,132,132,344,0.0,haar

adience_align-master/adiencealign(old)/tests/outputs/pipeline/faces/Fayssal_Mekdad_0002_face_0.cfidu

@@ -0,0 +1,70 @@
+233,0
+x,y,dx,dy,x_c,y_c
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