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removed sort by hist-blur

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iperov 2018-12-20 12:57:16 +04:00
parent 9926dc626a
commit bc5ca1ab59
2 changed files with 1 additions and 69 deletions
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2
main.py
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@ -58,7 +58,7 @@ if __name__ == "__main__":
sort_parser = subparsers.add_parser( "sort", help="Sort faces in a directory.")
sort_parser.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input directory. A directory containing the files you wish to process.")
sort_parser.add_argument('--by', required=True, dest="sort_by_method", choices=("blur", "face", "face-dissim", "face-yaw", "hist", "hist-dissim", "hist-blur", "brightness", "hue", "black", "origname"), help="Method of sorting. 'origname' sort by original filename to recover original sequence." )
sort_parser.add_argument('--by', required=True, dest="sort_by_method", choices=("blur", "face", "face-dissim", "face-yaw", "hist", "hist-dissim", "brightness", "hue", "black", "origname"), help="Method of sorting. 'origname' sort by original filename to recover original sequence." )
sort_parser.set_defaults (func=process_sort)
def process_train(arguments):

68
mainscripts/Sorter.py
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@ -239,73 +239,6 @@ def sort_by_face_yaw(input_path):
return img_list
def sort_by_hist_blur(input_path):
print ("Sorting by histogram similarity and blur...")
img_list = []
for x in tqdm( Path_utils.get_image_paths(input_path), desc="Loading"):
img = cv2.imread(x)
img_list.append ([x, cv2.calcHist([img], [0], None, [256], [0, 256]),
cv2.calcHist([img], [1], None, [256], [0, 256]),
cv2.calcHist([img], [2], None, [256], [0, 256]),
estimate_sharpness(img)
])
img_list_len = len(img_list)
for i in tqdm( range(0, img_list_len-1), desc="Sorting"):
min_score = float("inf")
j_min_score = i+1
for j in range(i+1,len(img_list)):
score = cv2.compareHist(img_list[i][1], img_list[j][1], cv2.HISTCMP_BHATTACHARYYA) + \
cv2.compareHist(img_list[i][2], img_list[j][2], cv2.HISTCMP_BHATTACHARYYA) + \
cv2.compareHist(img_list[i][3], img_list[j][3], cv2.HISTCMP_BHATTACHARYYA)
if score < min_score:
min_score = score
j_min_score = j
img_list[i+1], img_list[j_min_score] = img_list[j_min_score], img_list[i+1]
l = []
for i in range(0, img_list_len-1):
score = cv2.compareHist(img_list[i][1], img_list[i+1][1], cv2.HISTCMP_BHATTACHARYYA) + \
cv2.compareHist(img_list[i][2], img_list[i+1][2], cv2.HISTCMP_BHATTACHARYYA) + \
cv2.compareHist(img_list[i][3], img_list[i+1][3], cv2.HISTCMP_BHATTACHARYYA)
l += [score]
l = np.array(l)
v = np.mean(l)
if v*2 < np.max(l):
v *= 2
new_img_list = []
start_group_i = 0
odd_counter = 0
for i in tqdm( range(0, img_list_len), desc="Sorting"):
end_group_i = -1
if i < img_list_len-1:
score = cv2.compareHist(img_list[i][1], img_list[i+1][1], cv2.HISTCMP_BHATTACHARYYA) + \
cv2.compareHist(img_list[i][2], img_list[i+1][2], cv2.HISTCMP_BHATTACHARYYA) + \
cv2.compareHist(img_list[i][3], img_list[i+1][3], cv2.HISTCMP_BHATTACHARYYA)
if score >= v:
end_group_i = i
elif i == img_list_len-1:
end_group_i = i
if end_group_i >= start_group_i:
odd_counter += 1
s = sorted(img_list[start_group_i:end_group_i+1] , key=operator.itemgetter(4), reverse=True)
if odd_counter % 2 == 0:
new_img_list = new_img_list + s
else:
new_img_list = s + new_img_list
start_group_i = i + 1
return new_img_list
def sort_by_hist(input_path):
print ("Sorting by histogram similarity...")
@ -507,7 +440,6 @@ def main (input_path, sort_by_method):
elif sort_by_method == 'face-yaw': img_list = sort_by_face_yaw (input_path)
elif sort_by_method == 'hist': img_list = sort_by_hist (input_path)
elif sort_by_method == 'hist-dissim': img_list = sort_by_hist_dissim (input_path)
elif sort_by_method == 'hist-blur': img_list = sort_by_hist_blur (input_path)
elif sort_by_method == 'brightness': img_list = sort_by_brightness (input_path)
elif sort_by_method == 'hue': img_list = sort_by_hue (input_path)
elif sort_by_method == 'black': img_list = sort_by_black (input_path)