forked from 170010011/fr
66 lines
1.5 KiB
Python
66 lines
1.5 KiB
Python
from skimage.io import imread, imshow
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from skimage.transform import resize
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from skimage.feature import hog
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from skimage import exposure
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import matplotlib.pyplot as plt
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import sys
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import cv2
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import os
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import numpy as np
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import ntpath
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CASCADE_PATH = "fr_env/lib/python3.8/site-packages/cv2/data/haarcascade_frontalface_default.xml"
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def display_image(img):
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plt.imshow(img)
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plt.show()
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def path_leaf(path):
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head, tail = ntpath.split(path)
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return tail or ntpath.basename(head)
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if len(sys.argv) < 2:
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print("no input image")
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exit(1)
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if len(sys.argv) < 3:
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print("no output folder")
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exit(1)
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op_path = sys.argv[2]
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print(sys.argv[1])
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raw_img = cv2.imread(sys.argv[1])
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print(raw_img.shape)
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img = cv2.cvtColor(raw_img, cv2.COLOR_BGR2GRAY)
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print(img.shape)
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# resized_img = resize(img,(1000,1000))
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resized_img =img
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print(resized_img.shape)
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# display_image(resized_img)
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face_classifier = cv2.CascadeClassifier(CASCADE_PATH)
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# resized_img = np.array(resized_img, dtype='uint8')
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faces = face_classifier.detectMultiScale(resized_img, 1.3,3) ### (inp_img, scaling_factor, minNeighbours )
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print(len(faces))
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if len(faces) == 0:
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print("No faces found")
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ind = 0
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for (x,y,w,h) in faces:
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cv2.rectangle(resized_img, (x,y), (x+w,y+h), (127,0,255), 2 )
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cropped = resized_img[y:y+h, x:x+w]
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# cv2.imshow('Face', cropped)
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cv2.imwrite( op_path + "/faceof_"+str(ind)+path_leaf(sys.argv[1]),cropped)
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# print("***", op_path, op_path + "/faceof_"+str(ind)+path_leaf(sys.argv[1]))
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ind+=1
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# cv2.waitKey(0)
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cv2.destroyAllWindows() |