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Finish the TODO in function "preprocess_face"

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Resize the detected face image proportionally then add black pixels to resize it to target_size, which avoids the transformation on the detected faces resulting from resizing directly.
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Libo-Xu 2021-07-01 10:40:11 +08:00 committed by GitHub
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1 changed files with 15 additions and 2 deletions
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deepface/commons/functions.py
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@ -124,9 +124,22 @@ def preprocess_face(img, target_size=(224, 224), grayscale = False, enforce_dete
if grayscale == True: if grayscale == True:
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = cv2.resize(img, target_size) # img = cv2.resize(img, target_size)
#TODO: resize causes transformation on base image, you should add black pixels to rezie it to target_size
# First resize the longer side to the target size
factor = target_size[0] / max(img.shape) # Suppose target_size[0] == target_size[1]
dsize = (int(img.shape[1] * factor), int(img.shape[0] * factor))
img = cv2.resize(img, dsize)
# Then pad the other side to the target size by adding black pixels
diff_0 = target_size[0] - img.shape[0]
diff_1 = target_size[1] - img.shape[1]
if grayscale == False:
# Put the base image in the middle of the padded image
img = np.pad(img, ((diff_0 // 2, diff_0 - diff_0 // 2), (diff_1 // 2, diff_1 - diff_1 // 2), (0, 0)), 'constant')
else:
img = np.pad(img, ((diff_0 // 2, diff_0 - diff_0 // 2), (diff_1 // 2, diff_1 - diff_1 // 2)), 'constant')
img_pixels = image.img_to_array(img) img_pixels = image.img_to_array(img)
img_pixels = np.expand_dims(img_pixels, axis = 0) img_pixels = np.expand_dims(img_pixels, axis = 0)
img_pixels /= 255 #normalize input in [0, 1] img_pixels /= 255 #normalize input in [0, 1]