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