mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 04:52:13 -07:00
726 lines
24 KiB
Python
726 lines
24 KiB
Python
import os
|
|
import sys
|
|
import operator
|
|
import numpy as np
|
|
import cv2
|
|
from tqdm import tqdm
|
|
from shutil import copyfile
|
|
|
|
from pathlib import Path
|
|
from utils import Path_utils
|
|
from utils import image_utils
|
|
from utils.DFLPNG import DFLPNG
|
|
from facelib import LandmarksProcessor
|
|
from utils.SubprocessorBase import SubprocessorBase
|
|
import multiprocessing
|
|
|
|
|
|
def estimate_sharpness(image):
|
|
height, width = image.shape[:2]
|
|
|
|
if image.ndim == 3:
|
|
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
|
|
|
|
sharpness = 0
|
|
for y in range(height):
|
|
for x in range(width-1):
|
|
sharpness += abs( int(image[y, x]) - int(image[y, x+1]) )
|
|
|
|
for x in range(width):
|
|
for y in range(height-1):
|
|
sharpness += abs( int(image[y, x]) - int(image[y+1, x]) )
|
|
|
|
return sharpness
|
|
|
|
|
|
class BlurEstimatorSubprocessor(SubprocessorBase):
|
|
#override
|
|
def __init__(self, input_data ):
|
|
self.input_data = input_data
|
|
self.result = []
|
|
|
|
super().__init__('BlurEstimator', 60)
|
|
|
|
#override
|
|
def onHostClientsInitialized(self):
|
|
pass
|
|
|
|
#override
|
|
def process_info_generator(self):
|
|
for i in range(0, multiprocessing.cpu_count() ):
|
|
yield 'CPU%d' % (i), {}, {'device_idx': i,
|
|
'device_name': 'CPU%d' % (i),
|
|
}
|
|
|
|
#override
|
|
def get_no_process_started_message(self):
|
|
print ( 'Unable to start CPU processes.')
|
|
|
|
#override
|
|
def onHostGetProgressBarDesc(self):
|
|
return None
|
|
|
|
#override
|
|
def onHostGetProgressBarLen(self):
|
|
return len (self.input_data)
|
|
|
|
#override
|
|
def onHostGetData(self, host_dict):
|
|
if len (self.input_data) > 0:
|
|
return self.input_data.pop(0)
|
|
|
|
return None
|
|
|
|
#override
|
|
def onHostDataReturn (self, host_dict, data):
|
|
self.input_data.insert(0, data)
|
|
|
|
#override
|
|
def onClientInitialize(self, client_dict):
|
|
self.safe_print ('Running on %s.' % (client_dict['device_name']) )
|
|
return None
|
|
|
|
#override
|
|
def onClientFinalize(self):
|
|
pass
|
|
|
|
#override
|
|
def onClientProcessData(self, data):
|
|
filename_path = Path( data[0] )
|
|
|
|
dflpng = DFLPNG.load( str(filename_path), print_on_no_embedded_data=True )
|
|
if dflpng is not None:
|
|
image = cv2.imread( str(filename_path) )
|
|
image = ( image * \
|
|
LandmarksProcessor.get_image_hull_mask (image, dflpng.get_landmarks()) \
|
|
).astype(np.uint8)
|
|
return [ str(filename_path), estimate_sharpness( image ) ]
|
|
else:
|
|
return [ str(filename_path), 0 ]
|
|
|
|
#override
|
|
def onClientGetDataName (self, data):
|
|
#return string identificator of your data
|
|
return data[0]
|
|
|
|
#override
|
|
def onHostResult (self, host_dict, data, result):
|
|
if result[1] == 0:
|
|
filename_path = Path( data[0] )
|
|
print ( "{0} - invalid image, renaming to {0}_invalid.".format(str(filename_path)) )
|
|
filename_path.rename ( str(filename_path) + '_invalid' )
|
|
else:
|
|
self.result.append ( result )
|
|
return 1
|
|
|
|
#override
|
|
def onFinalizeAndGetResult(self):
|
|
return self.result
|
|
|
|
|
|
def sort_by_blur(input_path):
|
|
print ("Sorting by blur...")
|
|
|
|
img_list = [ (filename,[]) for filename in Path_utils.get_image_paths(input_path) ]
|
|
img_list = BlurEstimatorSubprocessor (img_list).process()
|
|
|
|
print ("Sorting...")
|
|
img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True)
|
|
|
|
return img_list
|
|
|
|
def sort_by_brightness(input_path):
|
|
print ("Sorting by brightness...")
|
|
img_list = [ [x, np.mean ( cv2.cvtColor(cv2.imread(x), cv2.COLOR_BGR2HSV)[...,2].flatten() )] for x in tqdm( Path_utils.get_image_paths(input_path), desc="Loading") ]
|
|
print ("Sorting...")
|
|
img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True)
|
|
return img_list
|
|
|
|
def sort_by_hue(input_path):
|
|
print ("Sorting by hue...")
|
|
img_list = [ [x, np.mean ( cv2.cvtColor(cv2.imread(x), cv2.COLOR_BGR2HSV)[...,0].flatten() )] for x in tqdm( Path_utils.get_image_paths(input_path), desc="Loading") ]
|
|
print ("Sorting...")
|
|
img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True)
|
|
return img_list
|
|
|
|
def sort_by_face(input_path):
|
|
|
|
print ("Sorting by face similarity...")
|
|
|
|
img_list = []
|
|
for filepath in tqdm( Path_utils.get_image_paths(input_path), desc="Loading"):
|
|
filepath = Path(filepath)
|
|
|
|
if filepath.suffix != '.png':
|
|
print ("%s is not a png file required for sort_by_face" % (filepath.name) )
|
|
continue
|
|
|
|
dflpng = DFLPNG.load (str(filepath), print_on_no_embedded_data=True)
|
|
if dflpng is None:
|
|
continue
|
|
|
|
img_list.append( [str(filepath), dflpng.get_landmarks()] )
|
|
|
|
|
|
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)):
|
|
|
|
fl1 = img_list[i][1]
|
|
fl2 = img_list[j][1]
|
|
score = np.sum ( np.absolute ( (fl2 - fl1).flatten() ) )
|
|
|
|
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]
|
|
|
|
return img_list
|
|
|
|
def sort_by_face_dissim(input_path):
|
|
|
|
print ("Sorting by face dissimilarity...")
|
|
|
|
img_list = []
|
|
for filepath in tqdm( Path_utils.get_image_paths(input_path), desc="Loading"):
|
|
filepath = Path(filepath)
|
|
|
|
if filepath.suffix != '.png':
|
|
print ("%s is not a png file required for sort_by_face_dissim" % (filepath.name) )
|
|
continue
|
|
|
|
dflpng = DFLPNG.load (str(filepath), print_on_no_embedded_data=True)
|
|
if dflpng is None:
|
|
continue
|
|
|
|
img_list.append( [str(filepath), dflpng.get_landmarks(), 0 ] )
|
|
|
|
img_list_len = len(img_list)
|
|
for i in tqdm( range(0, img_list_len-1), desc="Sorting"):
|
|
score_total = 0
|
|
for j in range(i+1,len(img_list)):
|
|
if i == j:
|
|
continue
|
|
fl1 = img_list[i][1]
|
|
fl2 = img_list[j][1]
|
|
score_total += np.sum ( np.absolute ( (fl2 - fl1).flatten() ) )
|
|
|
|
img_list[i][2] = score_total
|
|
|
|
print ("Sorting...")
|
|
img_list = sorted(img_list, key=operator.itemgetter(2), reverse=True)
|
|
|
|
return img_list
|
|
|
|
def sort_by_face_yaw(input_path):
|
|
print ("Sorting by face yaw...")
|
|
img_list = []
|
|
for filepath in tqdm( Path_utils.get_image_paths(input_path), desc="Loading"):
|
|
filepath = Path(filepath)
|
|
|
|
if filepath.suffix != '.png':
|
|
print ("%s is not a png file required for sort_by_face_dissim" % (filepath.name) )
|
|
continue
|
|
|
|
dflpng = DFLPNG.load (str(filepath), print_on_no_embedded_data=True)
|
|
if dflpng is None:
|
|
continue
|
|
|
|
img_list.append( [str(filepath), np.array( dflpng.get_yaw_value() ) ] )
|
|
|
|
print ("Sorting...")
|
|
img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True)
|
|
|
|
return img_list
|
|
|
|
class HistSsimSubprocessor(SubprocessorBase):
|
|
#override
|
|
def __init__(self, img_list ):
|
|
self.img_list = img_list
|
|
self.img_list_len = len(img_list)
|
|
|
|
slice_count = 20000
|
|
sliced_count = self.img_list_len // slice_count
|
|
|
|
if sliced_count > 12:
|
|
sliced_count = 11.9
|
|
slice_count = int(self.img_list_len / sliced_count)
|
|
sliced_count = self.img_list_len // slice_count
|
|
|
|
self.img_chunks_list = [ self.img_list[i*slice_count : (i+1)*slice_count] for i in range(sliced_count) ] + \
|
|
[ self.img_list[sliced_count*slice_count:] ]
|
|
|
|
self.result = []
|
|
|
|
super().__init__('HistSsim', 0)
|
|
|
|
#override
|
|
def onHostClientsInitialized(self):
|
|
pass
|
|
|
|
#override
|
|
def process_info_generator(self):
|
|
for i in range( len(self.img_chunks_list) ):
|
|
yield 'CPU%d' % (i), {'i':i}, {'device_idx': i,
|
|
'device_name': 'CPU%d' % (i)
|
|
}
|
|
|
|
#override
|
|
def get_no_process_started_message(self):
|
|
print ( 'Unable to start CPU processes.')
|
|
|
|
#override
|
|
def onHostGetProgressBarDesc(self):
|
|
return "Sorting"
|
|
|
|
#override
|
|
def onHostGetProgressBarLen(self):
|
|
return len(self.img_list)
|
|
|
|
#override
|
|
def onHostClientsInitialized(self):
|
|
self.inc_progress_bar(len(self.img_chunks_list))
|
|
|
|
#override
|
|
def onHostGetData(self, host_dict):
|
|
if len (self.img_chunks_list) > 0:
|
|
return self.img_chunks_list.pop(0)
|
|
|
|
return None
|
|
|
|
#override
|
|
def onHostDataReturn (self, host_dict, data):
|
|
raise Exception("Fail to process data. Decrease number of images and try again.")
|
|
|
|
#override
|
|
def onClientInitialize(self, client_dict):
|
|
self.safe_print ('Running on %s.' % (client_dict['device_name']) )
|
|
return None
|
|
|
|
#override
|
|
def onClientFinalize(self):
|
|
pass
|
|
|
|
#override
|
|
def onClientProcessData(self, data):
|
|
|
|
img_list = []
|
|
for x in data:
|
|
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])
|
|
])
|
|
|
|
img_list_len = len(img_list)
|
|
for i in range(img_list_len-1):
|
|
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]
|
|
|
|
self.inc_progress_bar(1)
|
|
|
|
return img_list
|
|
|
|
#override
|
|
def onClientGetDataName (self, data):
|
|
#return string identificator of your data
|
|
return "Bunch of images"
|
|
|
|
#override
|
|
def onHostResult (self, host_dict, data, result):
|
|
self.result += result
|
|
return 0
|
|
|
|
#override
|
|
def onFinalizeAndGetResult(self):
|
|
return self.result
|
|
|
|
def sort_by_hist(input_path):
|
|
print ("Sorting by histogram similarity...")
|
|
img_list = HistSsimSubprocessor(Path_utils.get_image_paths(input_path)).process()
|
|
return img_list
|
|
|
|
class HistDissimSubprocessor(SubprocessorBase):
|
|
#override
|
|
def __init__(self, img_list ):
|
|
self.img_list = img_list
|
|
self.img_list_range = [i for i in range(0, len(img_list) )]
|
|
|
|
self.result = []
|
|
|
|
super().__init__('HistDissim', 60)
|
|
|
|
#override
|
|
def onHostClientsInitialized(self):
|
|
pass
|
|
|
|
#override
|
|
def process_info_generator(self):
|
|
for i in range(0, min(multiprocessing.cpu_count(), 8) ):
|
|
yield 'CPU%d' % (i), {}, {'device_idx': i,
|
|
'device_name': 'CPU%d' % (i),
|
|
'img_list' : self.img_list
|
|
}
|
|
|
|
#override
|
|
def get_no_process_started_message(self):
|
|
print ( 'Unable to start CPU processes.')
|
|
|
|
#override
|
|
def onHostGetProgressBarDesc(self):
|
|
return "Sorting"
|
|
|
|
#override
|
|
def onHostGetProgressBarLen(self):
|
|
return len (self.img_list)
|
|
|
|
#override
|
|
def onHostGetData(self, host_dict):
|
|
if len (self.img_list_range) > 0:
|
|
return [self.img_list_range.pop(0)]
|
|
|
|
return None
|
|
|
|
#override
|
|
def onHostDataReturn (self, host_dict, data):
|
|
self.img_list_range.insert(0, data[0])
|
|
|
|
#override
|
|
def onClientInitialize(self, client_dict):
|
|
self.img_list = client_dict['img_list']
|
|
self.img_list_len = len(self.img_list)
|
|
|
|
self.safe_print ('Running on %s.' % (client_dict['device_name']) )
|
|
return None
|
|
|
|
#override
|
|
def onClientFinalize(self):
|
|
pass
|
|
|
|
#override
|
|
def onClientProcessData(self, data):
|
|
i = data[0]
|
|
score_total = 0
|
|
for j in range( 0, self.img_list_len):
|
|
if i == j:
|
|
continue
|
|
score_total += cv2.compareHist(self.img_list[i][1], self.img_list[j][1], cv2.HISTCMP_BHATTACHARYYA)
|
|
|
|
return score_total
|
|
|
|
#override
|
|
def onClientGetDataName (self, data):
|
|
#return string identificator of your data
|
|
return self.img_list[data[0]][0]
|
|
|
|
#override
|
|
def onHostResult (self, host_dict, data, result):
|
|
self.img_list[data[0]][2] = result
|
|
return 1
|
|
|
|
#override
|
|
def onFinalizeAndGetResult(self):
|
|
return self.img_list
|
|
|
|
def sort_by_hist_dissim(input_path):
|
|
print ("Sorting by histogram dissimilarity...")
|
|
|
|
img_list = []
|
|
for filename_path in tqdm( Path_utils.get_image_paths(input_path), desc="Loading"):
|
|
image = cv2.imread(filename_path)
|
|
|
|
dflpng = DFLPNG.load( str(filename_path) )
|
|
if dflpng is not None:
|
|
face_mask = LandmarksProcessor.get_image_hull_mask (image, dflpng.get_landmarks())
|
|
image = (image*face_mask).astype(np.uint8)
|
|
|
|
img_list.append ([filename_path, cv2.calcHist([cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)], [0], None, [256], [0, 256]), 0 ])
|
|
|
|
img_list = HistDissimSubprocessor(img_list).process()
|
|
|
|
print ("Sorting...")
|
|
img_list = sorted(img_list, key=operator.itemgetter(2), reverse=True)
|
|
|
|
return img_list
|
|
|
|
|
|
class FinalLoaderSubprocessor(SubprocessorBase):
|
|
#override
|
|
def __init__(self, img_list ):
|
|
self.img_list = img_list
|
|
|
|
self.result = []
|
|
self.result_trash = []
|
|
|
|
super().__init__('FinalLoader', 60)
|
|
|
|
#override
|
|
def onHostClientsInitialized(self):
|
|
pass
|
|
|
|
#override
|
|
def process_info_generator(self):
|
|
for i in range(0, min(multiprocessing.cpu_count(), 8) ):
|
|
yield 'CPU%d' % (i), {}, {'device_idx': i,
|
|
'device_name': 'CPU%d' % (i)
|
|
}
|
|
|
|
#override
|
|
def get_no_process_started_message(self):
|
|
print ( 'Unable to start CPU processes.')
|
|
|
|
#override
|
|
def onHostGetProgressBarDesc(self):
|
|
return "Loading"
|
|
|
|
#override
|
|
def onHostGetProgressBarLen(self):
|
|
return len (self.img_list)
|
|
|
|
#override
|
|
def onHostGetData(self, host_dict):
|
|
if len (self.img_list) > 0:
|
|
return [self.img_list.pop(0)]
|
|
|
|
return None
|
|
|
|
#override
|
|
def onHostDataReturn (self, host_dict, data):
|
|
self.img_list.insert(0, data[0])
|
|
|
|
#override
|
|
def onClientInitialize(self, client_dict):
|
|
self.safe_print ('Running on %s.' % (client_dict['device_name']) )
|
|
return None
|
|
|
|
#override
|
|
def onClientFinalize(self):
|
|
pass
|
|
|
|
#override
|
|
def onClientProcessData(self, data):
|
|
filepath = Path(data[0])
|
|
|
|
try:
|
|
if filepath.suffix != '.png':
|
|
raise Exception ("%s is not a png file required for sort_final" % (filepath.name) )
|
|
|
|
dflpng = DFLPNG.load (str(filepath), print_on_no_embedded_data=True)
|
|
if dflpng is None:
|
|
raise Exception ("")
|
|
|
|
bgr = cv2.imread(str(filepath))
|
|
if bgr is None:
|
|
raise Exception ("Unable to load %s" % (filepath.name) )
|
|
|
|
gray = cv2.cvtColor(bgr, cv2.COLOR_BGR2GRAY)
|
|
gray_masked = ( gray * LandmarksProcessor.get_image_hull_mask (bgr, dflpng.get_landmarks() )[:,:,0] ).astype(np.uint8)
|
|
sharpness = estimate_sharpness(gray_masked)
|
|
hist = cv2.calcHist([gray], [0], None, [256], [0, 256])
|
|
except Exception as e:
|
|
print (e)
|
|
return [ 1, [str(filepath)] ]
|
|
|
|
return [ 0, [str(filepath), sharpness, hist, dflpng.get_yaw_value() ] ]
|
|
|
|
|
|
#override
|
|
def onClientGetDataName (self, data):
|
|
#return string identificator of your data
|
|
return data[0]
|
|
|
|
#override
|
|
def onHostResult (self, host_dict, data, result):
|
|
if result[0] == 0:
|
|
self.result.append (result[1])
|
|
else:
|
|
self.result_trash.append (result[1])
|
|
return 1
|
|
|
|
#override
|
|
def onFinalizeAndGetResult(self):
|
|
return self.result, self.result_trash
|
|
|
|
def sort_final(input_path):
|
|
print ("Performing final sort.")
|
|
|
|
img_list, trash_img_list = FinalLoaderSubprocessor( Path_utils.get_image_paths(input_path) ).process()
|
|
final_img_list = []
|
|
|
|
grads = 128
|
|
imgs_per_grad = 15
|
|
|
|
grads_space = np.linspace (-255,255,grads)
|
|
|
|
yaws_sample_list = [None]*grads
|
|
for g in tqdm ( range(grads), desc="Sort by yaw" ):
|
|
yaw = grads_space[g]
|
|
next_yaw = grads_space[g+1] if g < grads-1 else yaw
|
|
|
|
yaw_samples = []
|
|
for img in img_list:
|
|
s_yaw = -img[3]
|
|
if (g == 0 and s_yaw < next_yaw) or \
|
|
(g < grads-1 and s_yaw >= yaw and s_yaw < next_yaw) or \
|
|
(g == grads-1 and s_yaw >= yaw):
|
|
yaw_samples += [ img ]
|
|
if len(yaw_samples) > 0:
|
|
yaws_sample_list[g] = yaw_samples
|
|
|
|
total_lack = 0
|
|
for g in tqdm ( range (grads), desc="" ):
|
|
img_list = yaws_sample_list[g]
|
|
img_list_len = len(img_list) if img_list is not None else 0
|
|
|
|
lack = imgs_per_grad - img_list_len
|
|
total_lack += max(lack, 0)
|
|
|
|
imgs_per_grad += total_lack // grads
|
|
sharpned_imgs_per_grad = imgs_per_grad*10
|
|
|
|
for g in tqdm ( range (grads), desc="Sort by blur" ):
|
|
img_list = yaws_sample_list[g]
|
|
if img_list is None:
|
|
continue
|
|
|
|
img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True)
|
|
|
|
if len(img_list) > imgs_per_grad*2:
|
|
trash_img_list += img_list[len(img_list) // 2:]
|
|
img_list = img_list[0: len(img_list) // 2]
|
|
|
|
if len(img_list) > sharpned_imgs_per_grad:
|
|
trash_img_list += img_list[sharpned_imgs_per_grad:]
|
|
img_list = img_list[0:sharpned_imgs_per_grad]
|
|
|
|
yaws_sample_list[g] = img_list
|
|
|
|
for g in tqdm ( range (grads), desc="Sort by hist" ):
|
|
img_list = yaws_sample_list[g]
|
|
if img_list is None:
|
|
continue
|
|
|
|
for i in range( len(img_list) ):
|
|
score_total = 0
|
|
for j in range( len(img_list) ):
|
|
if i == j:
|
|
continue
|
|
score_total += cv2.compareHist(img_list[i][2], img_list[j][2], cv2.HISTCMP_BHATTACHARYYA)
|
|
img_list[i][3] = score_total
|
|
|
|
yaws_sample_list[g] = sorted(img_list, key=operator.itemgetter(3), reverse=True)
|
|
|
|
for g in tqdm ( range (grads), desc="Fetching best" ):
|
|
img_list = yaws_sample_list[g]
|
|
if img_list is None:
|
|
continue
|
|
|
|
final_img_list += img_list[0:imgs_per_grad]
|
|
trash_img_list += img_list[imgs_per_grad:]
|
|
|
|
return final_img_list, trash_img_list
|
|
|
|
def sort_by_black(input_path):
|
|
print ("Sorting by amount of black pixels...")
|
|
|
|
img_list = []
|
|
for x in tqdm( Path_utils.get_image_paths(input_path), desc="Loading"):
|
|
img = cv2.imread(x)
|
|
img_list.append ([x, img[(img == 0)].size ])
|
|
|
|
print ("Sorting...")
|
|
img_list = sorted(img_list, key=operator.itemgetter(1), reverse=False)
|
|
|
|
return img_list
|
|
|
|
def final_process(input_path, img_list, trash_img_list):
|
|
if len(trash_img_list) != 0:
|
|
parent_input_path = input_path.parent
|
|
trash_path = parent_input_path / (input_path.stem + '_trash')
|
|
trash_path.mkdir (exist_ok=True)
|
|
|
|
print ("Trashing %d items to %s" % ( len(trash_img_list), str(trash_path) ) )
|
|
|
|
for filename in Path_utils.get_image_paths(trash_path):
|
|
Path(filename).unlink()
|
|
|
|
for i in tqdm( range(len(trash_img_list)), desc="Moving trash" , leave=False):
|
|
src = Path (trash_img_list[i][0])
|
|
dst = trash_path / src.name
|
|
try:
|
|
src.rename (dst)
|
|
except:
|
|
print ('fail to trashing %s' % (src.name) )
|
|
|
|
print ("")
|
|
|
|
for i in tqdm( range(len(img_list)), desc="Renaming" , leave=False):
|
|
src = Path (img_list[i][0])
|
|
dst = input_path / ('%.5d_%s' % (i, src.name ))
|
|
try:
|
|
src.rename (dst)
|
|
except:
|
|
print ('fail to rename %s' % (src.name) )
|
|
|
|
for i in tqdm( range(len(img_list)) , desc="Renaming" ):
|
|
src = Path (img_list[i][0])
|
|
|
|
src = input_path / ('%.5d_%s' % (i, src.name))
|
|
dst = input_path / ('%.5d%s' % (i, src.suffix))
|
|
try:
|
|
src.rename (dst)
|
|
except:
|
|
print ('fail to rename %s' % (src.name) )
|
|
|
|
def sort_by_origname(input_path):
|
|
print ("Sort by original filename...")
|
|
|
|
img_list = []
|
|
for filepath in tqdm( Path_utils.get_image_paths(input_path), desc="Loading"):
|
|
filepath = Path(filepath)
|
|
|
|
if filepath.suffix != '.png':
|
|
print ("%s is not a png file required for sort_by_origname" % (filepath.name) )
|
|
continue
|
|
|
|
dflpng = DFLPNG.load (str(filepath), print_on_no_embedded_data=True)
|
|
if dflpng is None:
|
|
continue
|
|
|
|
img_list.append( [str(filepath), dflpng.get_source_filename()] )
|
|
|
|
print ("Sorting...")
|
|
img_list = sorted(img_list, key=operator.itemgetter(1))
|
|
return img_list
|
|
|
|
def main (input_path, sort_by_method):
|
|
input_path = Path(input_path)
|
|
sort_by_method = sort_by_method.lower()
|
|
|
|
print ("Running sort tool.\r\n")
|
|
|
|
img_list = []
|
|
trash_img_list = []
|
|
if sort_by_method == 'blur': img_list = sort_by_blur (input_path)
|
|
elif sort_by_method == 'face': img_list = sort_by_face (input_path)
|
|
elif sort_by_method == 'face-dissim': img_list = sort_by_face_dissim (input_path)
|
|
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 == '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)
|
|
elif sort_by_method == 'origname': img_list = sort_by_origname (input_path)
|
|
elif sort_by_method == 'final': img_list, trash_img_list = sort_final (input_path)
|
|
|
|
final_process (input_path, img_list, trash_img_list)
|