mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 04:52:13 -07:00
b72d5a3f9a
fixed error "dll load failed" on some systems Expanded eyebrows line of face masks. It does not affect mask of FAN-x converter mode.
800 lines
28 KiB
Python
800 lines
28 KiB
Python
import os
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import sys
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import operator
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import numpy as np
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import cv2
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from shutil import copyfile
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from pathlib import Path
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from utils import Path_utils
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from utils.DFLPNG import DFLPNG
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from utils.DFLJPG import DFLJPG
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from utils.cv2_utils import *
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from facelib import LandmarksProcessor
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from joblib import Subprocessor
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import multiprocessing
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from interact import interact as io
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from imagelib import estimate_sharpness
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class BlurEstimatorSubprocessor(Subprocessor):
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class Cli(Subprocessor.Cli):
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#override
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def on_initialize(self, client_dict):
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self.log_info('Running on %s.' % (client_dict['device_name']) )
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#override
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def process_data(self, data):
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filepath = Path( data[0] )
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if filepath.suffix == '.png':
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dflimg = DFLPNG.load( str(filepath) )
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elif filepath.suffix == '.jpg':
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dflimg = DFLJPG.load ( str(filepath) )
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else:
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dflimg = None
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if dflimg is not None:
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image = cv2_imread( str(filepath) )
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return [ str(filepath), estimate_sharpness(image) ]
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else:
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self.log_err ("%s is not a dfl image file" % (filepath.name) )
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return [ str(filepath), 0 ]
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#override
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def get_data_name (self, data):
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#return string identificator of your data
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return data[0]
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#override
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def __init__(self, input_data ):
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self.input_data = input_data
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self.img_list = []
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self.trash_img_list = []
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super().__init__('BlurEstimator', BlurEstimatorSubprocessor.Cli, 60)
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#override
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def on_clients_initialized(self):
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io.progress_bar ("", len (self.input_data))
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#override
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def on_clients_finalized(self):
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io.progress_bar_close ()
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#override
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def process_info_generator(self):
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for i in range(0, multiprocessing.cpu_count() ):
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yield 'CPU%d' % (i), {}, {'device_idx': i,
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'device_name': 'CPU%d' % (i),
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}
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#override
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def get_data(self, host_dict):
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if len (self.input_data) > 0:
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return self.input_data.pop(0)
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return None
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#override
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def on_data_return (self, host_dict, data):
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self.input_data.insert(0, data)
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#override
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def on_result (self, host_dict, data, result):
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if result[1] == 0:
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self.trash_img_list.append ( result )
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else:
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self.img_list.append ( result )
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io.progress_bar_inc(1)
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#override
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def get_result(self):
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return self.img_list, self.trash_img_list
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def sort_by_blur(input_path):
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io.log_info ("Sorting by blur...")
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img_list = [ (filename,[]) for filename in Path_utils.get_image_paths(input_path) ]
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img_list, trash_img_list = BlurEstimatorSubprocessor (img_list).run()
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io.log_info ("Sorting...")
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img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True)
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return img_list, trash_img_list
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def sort_by_face(input_path):
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io.log_info ("Sorting by face similarity...")
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img_list = []
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trash_img_list = []
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for filepath in io.progress_bar_generator( Path_utils.get_image_paths(input_path), "Loading"):
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filepath = Path(filepath)
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if filepath.suffix == '.png':
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dflimg = DFLPNG.load( str(filepath) )
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elif filepath.suffix == '.jpg':
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dflimg = DFLJPG.load ( str(filepath) )
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else:
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dflimg = None
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if dflimg is None:
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io.log_err ("%s is not a dfl image file" % (filepath.name) )
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trash_img_list.append ( [str(filepath)] )
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continue
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img_list.append( [str(filepath), dflimg.get_landmarks()] )
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img_list_len = len(img_list)
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for i in io.progress_bar_generator ( range(0, img_list_len-1), "Sorting"):
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min_score = float("inf")
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j_min_score = i+1
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for j in range(i+1,len(img_list)):
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fl1 = img_list[i][1]
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fl2 = img_list[j][1]
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score = np.sum ( np.absolute ( (fl2 - fl1).flatten() ) )
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if score < min_score:
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min_score = score
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j_min_score = j
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img_list[i+1], img_list[j_min_score] = img_list[j_min_score], img_list[i+1]
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return img_list, trash_img_list
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def sort_by_face_dissim(input_path):
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io.log_info ("Sorting by face dissimilarity...")
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img_list = []
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trash_img_list = []
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for filepath in io.progress_bar_generator( Path_utils.get_image_paths(input_path), "Loading"):
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filepath = Path(filepath)
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if filepath.suffix == '.png':
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dflimg = DFLPNG.load( str(filepath) )
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elif filepath.suffix == '.jpg':
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dflimg = DFLJPG.load ( str(filepath) )
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else:
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dflimg = None
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if dflimg is None:
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io.log_err ("%s is not a dfl image file" % (filepath.name) )
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trash_img_list.append ( [str(filepath)] )
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continue
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img_list.append( [str(filepath), dflimg.get_landmarks(), 0 ] )
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img_list_len = len(img_list)
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for i in io.progress_bar_generator( range(img_list_len-1), "Sorting"):
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score_total = 0
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for j in range(i+1,len(img_list)):
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if i == j:
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continue
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fl1 = img_list[i][1]
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fl2 = img_list[j][1]
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score_total += np.sum ( np.absolute ( (fl2 - fl1).flatten() ) )
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img_list[i][2] = score_total
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io.log_info ("Sorting...")
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img_list = sorted(img_list, key=operator.itemgetter(2), reverse=True)
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return img_list, trash_img_list
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def sort_by_face_yaw(input_path):
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io.log_info ("Sorting by face yaw...")
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img_list = []
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trash_img_list = []
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for filepath in io.progress_bar_generator( Path_utils.get_image_paths(input_path), "Loading"):
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filepath = Path(filepath)
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if filepath.suffix == '.png':
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dflimg = DFLPNG.load( str(filepath) )
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elif filepath.suffix == '.jpg':
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dflimg = DFLJPG.load ( str(filepath) )
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else:
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dflimg = None
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if dflimg is None:
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io.log_err ("%s is not a dfl image file" % (filepath.name) )
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trash_img_list.append ( [str(filepath)] )
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continue
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pitch_yaw_roll = dflimg.get_pitch_yaw_roll()
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if pitch_yaw_roll is not None:
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pitch, yaw, roll = pitch_yaw_roll
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else:
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pitch, yaw, roll = LandmarksProcessor.estimate_pitch_yaw_roll ( dflimg.get_landmarks() )
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img_list.append( [str(filepath), yaw ] )
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io.log_info ("Sorting...")
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img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True)
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return img_list, trash_img_list
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def sort_by_face_pitch(input_path):
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io.log_info ("Sorting by face pitch...")
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img_list = []
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trash_img_list = []
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for filepath in io.progress_bar_generator( Path_utils.get_image_paths(input_path), "Loading"):
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filepath = Path(filepath)
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if filepath.suffix == '.png':
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dflimg = DFLPNG.load( str(filepath) )
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elif filepath.suffix == '.jpg':
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dflimg = DFLJPG.load ( str(filepath) )
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else:
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dflimg = None
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if dflimg is None:
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io.log_err ("%s is not a dfl image file" % (filepath.name) )
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trash_img_list.append ( [str(filepath)] )
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continue
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pitch_yaw_roll = dflimg.get_pitch_yaw_roll()
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if pitch_yaw_roll is not None:
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pitch, yaw, roll = pitch_yaw_roll
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else:
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pitch, yaw, roll = LandmarksProcessor.estimate_pitch_yaw_roll ( dflimg.get_landmarks() )
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img_list.append( [str(filepath), pitch ] )
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io.log_info ("Sorting...")
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img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True)
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return img_list, trash_img_list
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class HistSsimSubprocessor(Subprocessor):
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class Cli(Subprocessor.Cli):
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#override
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def on_initialize(self, client_dict):
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self.log_info ('Running on %s.' % (client_dict['device_name']) )
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#override
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def process_data(self, data):
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img_list = []
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for x in data:
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img = cv2_imread(x)
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img_list.append ([x, cv2.calcHist([img], [0], None, [256], [0, 256]),
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cv2.calcHist([img], [1], None, [256], [0, 256]),
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cv2.calcHist([img], [2], None, [256], [0, 256])
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])
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img_list_len = len(img_list)
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for i in range(img_list_len-1):
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min_score = float("inf")
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j_min_score = i+1
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for j in range(i+1,len(img_list)):
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score = cv2.compareHist(img_list[i][1], img_list[j][1], cv2.HISTCMP_BHATTACHARYYA) + \
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cv2.compareHist(img_list[i][2], img_list[j][2], cv2.HISTCMP_BHATTACHARYYA) + \
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cv2.compareHist(img_list[i][3], img_list[j][3], cv2.HISTCMP_BHATTACHARYYA)
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if score < min_score:
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min_score = score
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j_min_score = j
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img_list[i+1], img_list[j_min_score] = img_list[j_min_score], img_list[i+1]
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self.progress_bar_inc(1)
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return img_list
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#override
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def get_data_name (self, data):
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return "Bunch of images"
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#override
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def __init__(self, img_list ):
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self.img_list = img_list
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self.img_list_len = len(img_list)
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slice_count = 20000
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sliced_count = self.img_list_len // slice_count
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if sliced_count > 12:
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sliced_count = 11.9
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slice_count = int(self.img_list_len / sliced_count)
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sliced_count = self.img_list_len // slice_count
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self.img_chunks_list = [ self.img_list[i*slice_count : (i+1)*slice_count] for i in range(sliced_count) ] + \
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[ self.img_list[sliced_count*slice_count:] ]
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self.result = []
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super().__init__('HistSsim', HistSsimSubprocessor.Cli, 0)
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#override
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def process_info_generator(self):
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for i in range( len(self.img_chunks_list) ):
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yield 'CPU%d' % (i), {'i':i}, {'device_idx': i,
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'device_name': 'CPU%d' % (i)
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}
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#override
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def on_clients_initialized(self):
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io.progress_bar ("Sorting", len(self.img_list))
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io.progress_bar_inc(len(self.img_chunks_list))
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#override
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def on_clients_finalized(self):
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io.progress_bar_close()
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#override
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def get_data(self, host_dict):
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if len (self.img_chunks_list) > 0:
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return self.img_chunks_list.pop(0)
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return None
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#override
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def on_data_return (self, host_dict, data):
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raise Exception("Fail to process data. Decrease number of images and try again.")
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#override
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def on_result (self, host_dict, data, result):
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self.result += result
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return 0
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#override
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def get_result(self):
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return self.result
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def sort_by_hist(input_path):
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io.log_info ("Sorting by histogram similarity...")
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img_list = HistSsimSubprocessor(Path_utils.get_image_paths(input_path)).run()
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return img_list
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class HistDissimSubprocessor(Subprocessor):
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class Cli(Subprocessor.Cli):
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#override
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def on_initialize(self, client_dict):
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self.log_info ('Running on %s.' % (client_dict['device_name']) )
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self.img_list = client_dict['img_list']
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self.img_list_len = len(self.img_list)
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#override
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def process_data(self, data):
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i = data[0]
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score_total = 0
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for j in range( 0, self.img_list_len):
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if i == j:
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continue
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score_total += cv2.compareHist(self.img_list[i][1], self.img_list[j][1], cv2.HISTCMP_BHATTACHARYYA)
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return score_total
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#override
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def get_data_name (self, data):
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#return string identificator of your data
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return self.img_list[data[0]][0]
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#override
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def __init__(self, img_list ):
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self.img_list = img_list
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self.img_list_range = [i for i in range(0, len(img_list) )]
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self.result = []
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super().__init__('HistDissim', HistDissimSubprocessor.Cli, 60)
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#override
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def on_clients_initialized(self):
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io.progress_bar ("Sorting", len (self.img_list) )
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#override
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def on_clients_finalized(self):
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io.progress_bar_close()
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#override
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def process_info_generator(self):
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for i in range(0, min(multiprocessing.cpu_count(), 8) ):
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yield 'CPU%d' % (i), {}, {'device_idx': i,
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'device_name': 'CPU%d' % (i),
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'img_list' : self.img_list
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}
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#override
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def get_data(self, host_dict):
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if len (self.img_list_range) > 0:
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return [self.img_list_range.pop(0)]
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return None
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#override
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def on_data_return (self, host_dict, data):
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self.img_list_range.insert(0, data[0])
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#override
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def on_result (self, host_dict, data, result):
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self.img_list[data[0]][2] = result
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io.progress_bar_inc(1)
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#override
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def get_result(self):
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return self.img_list
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def sort_by_hist_dissim(input_path):
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io.log_info ("Sorting by histogram dissimilarity...")
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img_list = []
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trash_img_list = []
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for filepath in io.progress_bar_generator( Path_utils.get_image_paths(input_path), "Loading"):
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filepath = Path(filepath)
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if filepath.suffix == '.png':
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dflimg = DFLPNG.load( str(filepath) )
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elif filepath.suffix == '.jpg':
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dflimg = DFLJPG.load ( str(filepath) )
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else:
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dflimg = None
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image = cv2_imread(str(filepath))
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if dflimg is not None:
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face_mask = LandmarksProcessor.get_image_hull_mask (image.shape, dflimg.get_landmarks())
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image = (image*face_mask).astype(np.uint8)
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img_list.append ([str(filepath), cv2.calcHist([cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)], [0], None, [256], [0, 256]), 0 ])
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img_list = HistDissimSubprocessor(img_list).run()
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io.log_info ("Sorting...")
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img_list = sorted(img_list, key=operator.itemgetter(2), reverse=True)
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return img_list, trash_img_list
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def sort_by_brightness(input_path):
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io.log_info ("Sorting by brightness...")
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img_list = [ [x, np.mean ( cv2.cvtColor(cv2_imread(x), cv2.COLOR_BGR2HSV)[...,2].flatten() )] for x in io.progress_bar_generator( Path_utils.get_image_paths(input_path), "Loading") ]
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io.log_info ("Sorting...")
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img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True)
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return img_list
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def sort_by_hue(input_path):
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io.log_info ("Sorting by hue...")
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img_list = [ [x, np.mean ( cv2.cvtColor(cv2_imread(x), cv2.COLOR_BGR2HSV)[...,0].flatten() )] for x in io.progress_bar_generator( Path_utils.get_image_paths(input_path), "Loading") ]
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io.log_info ("Sorting...")
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img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True)
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return img_list
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def sort_by_black(input_path):
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io.log_info ("Sorting by amount of black pixels...")
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img_list = []
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for x in io.progress_bar_generator( Path_utils.get_image_paths(input_path), "Loading"):
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img = cv2_imread(x)
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img_list.append ([x, img[(img == 0)].size ])
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io.log_info ("Sorting...")
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img_list = sorted(img_list, key=operator.itemgetter(1), reverse=False)
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return img_list
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def sort_by_origname(input_path):
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io.log_info ("Sort by original filename...")
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img_list = []
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trash_img_list = []
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for filepath in io.progress_bar_generator( Path_utils.get_image_paths(input_path), "Loading"):
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filepath = Path(filepath)
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if filepath.suffix == '.png':
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dflimg = DFLPNG.load( str(filepath) )
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elif filepath.suffix == '.jpg':
|
|
dflimg = DFLJPG.load( str(filepath) )
|
|
else:
|
|
dflimg = None
|
|
|
|
if dflimg is None:
|
|
io.log_err ("%s is not a dfl image file" % (filepath.name) )
|
|
trash_img_list.append( [str(filepath)] )
|
|
continue
|
|
|
|
img_list.append( [str(filepath), dflimg.get_source_filename()] )
|
|
|
|
io.log_info ("Sorting...")
|
|
img_list = sorted(img_list, key=operator.itemgetter(1))
|
|
return img_list, trash_img_list
|
|
|
|
def sort_by_oneface_in_image(input_path):
|
|
io.log_info ("Sort by one face in images...")
|
|
image_paths = Path_utils.get_image_paths(input_path)
|
|
a = np.array ([ ( int(x[0]), int(x[1]) ) \
|
|
for x in [ Path(filepath).stem.split('_') for filepath in image_paths ] if len(x) == 2
|
|
])
|
|
if len(a) > 0:
|
|
idxs = np.ndarray.flatten ( np.argwhere ( a[:,1] != 0 ) )
|
|
idxs = np.unique ( a[idxs][:,0] )
|
|
idxs = np.ndarray.flatten ( np.argwhere ( np.array([ x[0] in idxs for x in a ]) == True ) )
|
|
if len(idxs) > 0:
|
|
io.log_info ("Found %d images." % (len(idxs)) )
|
|
img_list = [ (path,) for i,path in enumerate(image_paths) if i not in idxs ]
|
|
trash_img_list = [ (image_paths[x],) for x in idxs ]
|
|
return img_list, trash_img_list
|
|
return [], []
|
|
|
|
class FinalLoaderSubprocessor(Subprocessor):
|
|
class Cli(Subprocessor.Cli):
|
|
#override
|
|
def on_initialize(self, client_dict):
|
|
self.log_info ('Running on %s.' % (client_dict['device_name']) )
|
|
self.include_by_blur = client_dict['include_by_blur']
|
|
|
|
#override
|
|
def process_data(self, data):
|
|
filepath = Path(data[0])
|
|
|
|
try:
|
|
if filepath.suffix == '.png':
|
|
dflimg = DFLPNG.load( str(filepath) )
|
|
elif filepath.suffix == '.jpg':
|
|
dflimg = DFLJPG.load( str(filepath) )
|
|
else:
|
|
dflimg = None
|
|
|
|
if dflimg is None:
|
|
self.log_err("%s is not a dfl image file" % (filepath.name))
|
|
return [ 1, [str(filepath)] ]
|
|
|
|
bgr = cv2_imread(str(filepath))
|
|
if bgr is None:
|
|
raise Exception ("Unable to load %s" % (filepath.name) )
|
|
|
|
gray = cv2.cvtColor(bgr, cv2.COLOR_BGR2GRAY)
|
|
sharpness = estimate_sharpness(gray) if self.include_by_blur else 0
|
|
pitch, yaw, roll = LandmarksProcessor.estimate_pitch_yaw_roll ( dflimg.get_landmarks() )
|
|
|
|
hist = cv2.calcHist([gray], [0], None, [256], [0, 256])
|
|
except Exception as e:
|
|
self.log_err (e)
|
|
return [ 1, [str(filepath)] ]
|
|
|
|
return [ 0, [str(filepath), sharpness, hist, yaw ] ]
|
|
|
|
#override
|
|
def get_data_name (self, data):
|
|
#return string identificator of your data
|
|
return data[0]
|
|
|
|
#override
|
|
def __init__(self, img_list, include_by_blur ):
|
|
self.img_list = img_list
|
|
|
|
self.include_by_blur = include_by_blur
|
|
self.result = []
|
|
self.result_trash = []
|
|
|
|
super().__init__('FinalLoader', FinalLoaderSubprocessor.Cli, 60)
|
|
|
|
#override
|
|
def on_clients_initialized(self):
|
|
io.progress_bar ("Loading", len (self.img_list))
|
|
|
|
#override
|
|
def on_clients_finalized(self):
|
|
io.progress_bar_close()
|
|
|
|
#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),
|
|
'include_by_blur': self.include_by_blur
|
|
}
|
|
|
|
#override
|
|
def get_data(self, host_dict):
|
|
if len (self.img_list) > 0:
|
|
return [self.img_list.pop(0)]
|
|
|
|
return None
|
|
|
|
#override
|
|
def on_data_return (self, host_dict, data):
|
|
self.img_list.insert(0, data[0])
|
|
|
|
#override
|
|
def on_result (self, host_dict, data, result):
|
|
if result[0] == 0:
|
|
self.result.append (result[1])
|
|
else:
|
|
self.result_trash.append (result[1])
|
|
io.progress_bar_inc(1)
|
|
|
|
#override
|
|
def get_result(self):
|
|
return self.result, self.result_trash
|
|
|
|
class FinalHistDissimSubprocessor(Subprocessor):
|
|
class Cli(Subprocessor.Cli):
|
|
#override
|
|
def on_initialize(self, client_dict):
|
|
self.log_info ('Running on %s.' % (client_dict['device_name']) )
|
|
|
|
#override
|
|
def process_data(self, data):
|
|
idx, img_list = data
|
|
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
|
|
img_list = sorted(img_list, key=operator.itemgetter(3), reverse=True)
|
|
return idx, img_list
|
|
|
|
#override
|
|
def get_data_name (self, data):
|
|
return "Bunch of images"
|
|
|
|
#override
|
|
def __init__(self, yaws_sample_list ):
|
|
self.yaws_sample_list = yaws_sample_list
|
|
self.yaws_sample_list_len = len(yaws_sample_list)
|
|
|
|
self.yaws_sample_list_idxs = [ i for i in range(self.yaws_sample_list_len) if self.yaws_sample_list[i] is not None ]
|
|
self.result = [ None for _ in range(self.yaws_sample_list_len) ]
|
|
super().__init__('FinalHistDissimSubprocessor', FinalHistDissimSubprocessor.Cli)
|
|
|
|
#override
|
|
def process_info_generator(self):
|
|
for i in range(min(multiprocessing.cpu_count(), 8) ):
|
|
yield 'CPU%d' % (i), {'i':i}, {'device_idx': i,
|
|
'device_name': 'CPU%d' % (i)
|
|
}
|
|
#override
|
|
def on_clients_initialized(self):
|
|
io.progress_bar ("Sort by hist-dissim", self.yaws_sample_list_len)
|
|
|
|
#override
|
|
def on_clients_finalized(self):
|
|
io.progress_bar_close()
|
|
|
|
#override
|
|
def get_data(self, host_dict):
|
|
if len (self.yaws_sample_list_idxs) > 0:
|
|
idx = self.yaws_sample_list_idxs.pop(0)
|
|
|
|
return idx, self.yaws_sample_list[idx]
|
|
return None
|
|
|
|
#override
|
|
def on_data_return (self, host_dict, data):
|
|
self.yaws_sample_list_idxs.insert(0, data[0])
|
|
|
|
#override
|
|
def on_result (self, host_dict, data, result):
|
|
idx, yaws_sample_list = data
|
|
self.result[idx] = yaws_sample_list
|
|
io.progress_bar_inc(1)
|
|
|
|
#override
|
|
def get_result(self):
|
|
return self.result
|
|
|
|
def sort_final(input_path, include_by_blur=True):
|
|
io.log_info ("Performing final sort.")
|
|
|
|
target_count = io.input_int ("Target number of images? (default:2000) : ", 2000)
|
|
|
|
img_list, trash_img_list = FinalLoaderSubprocessor( Path_utils.get_image_paths(input_path), include_by_blur ).run()
|
|
final_img_list = []
|
|
|
|
grads = 128
|
|
imgs_per_grad = round (target_count / grads)
|
|
|
|
grads_space = np.linspace (-1.0,1.0,grads)
|
|
|
|
yaws_sample_list = [None]*grads
|
|
for g in io.progress_bar_generator ( range(grads), "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 io.progress_bar_generator ( range(grads), ""):
|
|
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
|
|
|
|
if include_by_blur:
|
|
sharpned_imgs_per_grad = imgs_per_grad*10
|
|
for g in io.progress_bar_generator ( range (grads), "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) > 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
|
|
|
|
yaws_sample_list = FinalHistDissimSubprocessor(yaws_sample_list).run()
|
|
|
|
for g in io.progress_bar_generator ( range (grads), "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 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)
|
|
|
|
io.log_info ("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 io.progress_bar_generator( range(len(trash_img_list)), "Moving trash", leave=False):
|
|
src = Path (trash_img_list[i][0])
|
|
dst = trash_path / src.name
|
|
try:
|
|
src.rename (dst)
|
|
except:
|
|
io.log_info ('fail to trashing %s' % (src.name) )
|
|
|
|
io.log_info ("")
|
|
|
|
if len(img_list) != 0:
|
|
for i in io.progress_bar_generator( [*range(len(img_list))], "Renaming", leave=False):
|
|
src = Path (img_list[i][0])
|
|
dst = input_path / ('%.5d_%s' % (i, src.name ))
|
|
try:
|
|
src.rename (dst)
|
|
except:
|
|
io.log_info ('fail to rename %s' % (src.name) )
|
|
|
|
for i in io.progress_bar_generator( [*range(len(img_list))], "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:
|
|
io.log_info ('fail to rename %s' % (src.name) )
|
|
|
|
|
|
|
|
def main (input_path, sort_by_method):
|
|
input_path = Path(input_path)
|
|
sort_by_method = sort_by_method.lower()
|
|
|
|
io.log_info ("Running sort tool.\r\n")
|
|
|
|
img_list = []
|
|
trash_img_list = []
|
|
if sort_by_method == 'blur': img_list, trash_img_list = sort_by_blur (input_path)
|
|
elif sort_by_method == 'face': img_list, trash_img_list = sort_by_face (input_path)
|
|
elif sort_by_method == 'face-dissim': img_list, trash_img_list = sort_by_face_dissim (input_path)
|
|
elif sort_by_method == 'face-yaw': img_list, trash_img_list = sort_by_face_yaw (input_path)
|
|
elif sort_by_method == 'face-pitch': img_list, trash_img_list = sort_by_face_pitch (input_path)
|
|
elif sort_by_method == 'hist': img_list = sort_by_hist (input_path)
|
|
elif sort_by_method == 'hist-dissim': img_list, trash_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, trash_img_list = sort_by_origname (input_path)
|
|
elif sort_by_method == 'oneface': img_list, trash_img_list = sort_by_oneface_in_image (input_path)
|
|
elif sort_by_method == 'final': img_list, trash_img_list = sort_final (input_path)
|
|
elif sort_by_method == 'final-no-blur': img_list, trash_img_list = sort_final (input_path, include_by_blur=False)
|
|
|
|
final_process (input_path, img_list, trash_img_list)
|