mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2024-11-20 23:10:08 -08:00
938 lines
31 KiB
Python
938 lines
31 KiB
Python
import math
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import multiprocessing
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import operator
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import os
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import sys
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import tempfile
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from functools import cmp_to_key
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from pathlib import Path
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import cv2
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import numpy as np
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from numpy import linalg as npla
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from core import imagelib, mathlib, pathex
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from core.cv2ex import *
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from core.imagelib import estimate_sharpness
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from core.interact import interact as io
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from core.joblib import Subprocessor
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from core.leras import nn
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from DFLIMG import *
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from facelib import LandmarksProcessor
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class BlurEstimatorSubprocessor(Subprocessor):
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class Cli(Subprocessor.Cli):
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def on_initialize(self, client_dict):
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self.estimate_motion_blur = client_dict['estimate_motion_blur']
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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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dflimg = DFLIMG.load (filepath)
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if dflimg is None or not dflimg.has_data():
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self.log_err (f"{filepath.name} is not a dfl image file")
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return [ str(filepath), 0 ]
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else:
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image = cv2_imread( str(filepath) )
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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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if self.estimate_motion_blur:
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value = cv2.Laplacian(image, cv2.CV_64F, ksize=11).var()
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else:
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value = estimate_sharpness(image)
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return [ str(filepath), value ]
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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, estimate_motion_blur=False ):
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self.input_data = input_data
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self.estimate_motion_blur = estimate_motion_blur
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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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cpu_count = multiprocessing.cpu_count()
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io.log_info(f'Running on {cpu_count} CPUs')
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for i in range(cpu_count):
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yield 'CPU%d' % (i), {}, {'estimate_motion_blur':self.estimate_motion_blur}
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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 pathex.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_motion_blur(input_path):
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io.log_info ("Sorting by motion blur...")
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img_list = [ (filename,[]) for filename in pathex.get_image_paths(input_path) ]
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img_list, trash_img_list = BlurEstimatorSubprocessor (img_list, estimate_motion_blur=True).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_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( pathex.get_image_paths(input_path), "Loading"):
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filepath = Path(filepath)
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dflimg = DFLIMG.load (filepath)
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if dflimg is None or not dflimg.has_data():
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io.log_err (f"{filepath.name} is not a dfl image file")
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trash_img_list.append ( [str(filepath)] )
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continue
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pitch, yaw, roll = LandmarksProcessor.estimate_pitch_yaw_roll ( dflimg.get_landmarks(), size=dflimg.get_shape()[1] )
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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( pathex.get_image_paths(input_path), "Loading"):
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filepath = Path(filepath)
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dflimg = DFLIMG.load (filepath)
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if dflimg is None or not dflimg.has_data():
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io.log_err (f"{filepath.name} is not a dfl image file")
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trash_img_list.append ( [str(filepath)] )
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continue
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pitch, yaw, roll = LandmarksProcessor.estimate_pitch_yaw_roll ( dflimg.get_landmarks(), size=dflimg.get_shape()[1] )
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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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def sort_by_face_source_rect_size(input_path):
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io.log_info ("Sorting by face rect size...")
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img_list = []
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trash_img_list = []
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for filepath in io.progress_bar_generator( pathex.get_image_paths(input_path), "Loading"):
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filepath = Path(filepath)
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dflimg = DFLIMG.load (filepath)
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if dflimg is None or not dflimg.has_data():
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io.log_err (f"{filepath.name} is not a dfl image file")
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trash_img_list.append ( [str(filepath)] )
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continue
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source_rect = dflimg.get_source_rect()
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rect_area = mathlib.polygon_area(np.array(source_rect[[0,2,2,0]]).astype(np.float32), np.array(source_rect[[1,1,3,3]]).astype(np.float32))
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img_list.append( [str(filepath), rect_area ] )
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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 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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cpu_count = len(self.img_chunks_list)
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io.log_info(f'Running on {cpu_count} threads')
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for i in range(cpu_count):
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yield 'CPU%d' % (i), {'i':i}, {}
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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(pathex.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.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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cpu_count = min(multiprocessing.cpu_count(), 8)
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io.log_info(f'Running on {cpu_count} CPUs')
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for i in range(cpu_count):
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yield 'CPU%d' % (i), {}, {'img_list' : self.img_list}
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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( pathex.get_image_paths(input_path), "Loading"):
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filepath = Path(filepath)
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dflimg = DFLIMG.load (filepath)
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image = cv2_imread(str(filepath))
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if dflimg is not None and dflimg.has_data():
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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( pathex.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( pathex.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( pathex.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( pathex.get_image_paths(input_path), "Loading"):
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filepath = Path(filepath)
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dflimg = DFLIMG.load (filepath)
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if dflimg is None or not dflimg.has_data():
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io.log_err (f"{filepath.name} is not a dfl image file")
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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_source_filename()] )
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io.log_info ("Sorting...")
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img_list = sorted(img_list, key=operator.itemgetter(1))
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return img_list, trash_img_list
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def sort_by_oneface_in_image(input_path):
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io.log_info ("Sort by one face in images...")
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image_paths = pathex.get_image_paths(input_path)
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a = np.array ([ ( int(x[0]), int(x[1]) ) \
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for x in [ Path(filepath).stem.split('_') for filepath in image_paths ] if len(x) == 2
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])
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if len(a) > 0:
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idxs = np.ndarray.flatten ( np.argwhere ( a[:,1] != 0 ) )
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idxs = np.unique ( a[idxs][:,0] )
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idxs = np.ndarray.flatten ( np.argwhere ( np.array([ x[0] in idxs for x in a ]) == True ) )
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if len(idxs) > 0:
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io.log_info ("Found %d images." % (len(idxs)) )
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img_list = [ (path,) for i,path in enumerate(image_paths) if i not in idxs ]
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trash_img_list = [ (image_paths[x],) for x in idxs ]
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return img_list, trash_img_list
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io.log_info ("Nothing found. Possible recover original filenames first.")
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return [], []
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class FinalLoaderSubprocessor(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.faster = client_dict['faster']
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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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try:
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dflimg = DFLIMG.load (filepath)
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if dflimg is None or not dflimg.has_data():
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self.log_err (f"{filepath.name} is not a dfl image file")
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return [ 1, [str(filepath)] ]
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bgr = cv2_imread(str(filepath))
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if bgr is None:
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raise Exception ("Unable to load %s" % (filepath.name) )
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gray = cv2.cvtColor(bgr, cv2.COLOR_BGR2GRAY)
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if self.faster:
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|
source_rect = dflimg.get_source_rect()
|
|
sharpness = mathlib.polygon_area(np.array(source_rect[[0,2,2,0]]).astype(np.float32), np.array(source_rect[[1,1,3,3]]).astype(np.float32))
|
|
else:
|
|
face_mask = LandmarksProcessor.get_image_hull_mask (gray.shape, dflimg.get_landmarks())
|
|
sharpness = estimate_sharpness( (gray[...,None]*face_mask).astype(np.uint8) )
|
|
|
|
pitch, yaw, roll = LandmarksProcessor.estimate_pitch_yaw_roll ( dflimg.get_landmarks(), size=dflimg.get_shape()[1] )
|
|
|
|
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, pitch ] ]
|
|
|
|
#override
|
|
def get_data_name (self, data):
|
|
#return string identificator of your data
|
|
return data[0]
|
|
|
|
#override
|
|
def __init__(self, img_list, faster ):
|
|
self.img_list = img_list
|
|
|
|
self.faster = faster
|
|
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):
|
|
cpu_count = min(multiprocessing.cpu_count(), 8)
|
|
io.log_info(f'Running on {cpu_count} CPUs')
|
|
|
|
for i in range(cpu_count):
|
|
yield 'CPU%d' % (i), {}, {'faster': self.faster}
|
|
|
|
#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 process_data(self, data):
|
|
idx, pitch_yaw_img_list = data
|
|
|
|
for p in range ( len(pitch_yaw_img_list) ):
|
|
|
|
img_list = pitch_yaw_img_list[p]
|
|
if img_list is not None:
|
|
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
|
|
|
|
pitch_yaw_img_list[p] = sorted(img_list, key=operator.itemgetter(3), reverse=True)
|
|
|
|
return idx, pitch_yaw_img_list
|
|
|
|
#override
|
|
def get_data_name (self, data):
|
|
return "Bunch of images"
|
|
|
|
#override
|
|
def __init__(self, pitch_yaw_sample_list ):
|
|
self.pitch_yaw_sample_list = pitch_yaw_sample_list
|
|
self.pitch_yaw_sample_list_len = len(pitch_yaw_sample_list)
|
|
|
|
self.pitch_yaw_sample_list_idxs = [ i for i in range(self.pitch_yaw_sample_list_len) if self.pitch_yaw_sample_list[i] is not None ]
|
|
self.result = [ None for _ in range(self.pitch_yaw_sample_list_len) ]
|
|
super().__init__('FinalHistDissimSubprocessor', FinalHistDissimSubprocessor.Cli)
|
|
|
|
#override
|
|
def process_info_generator(self):
|
|
cpu_count = min(multiprocessing.cpu_count(), 8)
|
|
io.log_info(f'Running on {cpu_count} CPUs')
|
|
for i in range(cpu_count):
|
|
yield 'CPU%d' % (i), {}, {}
|
|
|
|
#override
|
|
def on_clients_initialized(self):
|
|
io.progress_bar ("Sort by hist-dissim", len(self.pitch_yaw_sample_list_idxs) )
|
|
|
|
#override
|
|
def on_clients_finalized(self):
|
|
io.progress_bar_close()
|
|
|
|
#override
|
|
def get_data(self, host_dict):
|
|
if len (self.pitch_yaw_sample_list_idxs) > 0:
|
|
idx = self.pitch_yaw_sample_list_idxs.pop(0)
|
|
|
|
return idx, self.pitch_yaw_sample_list[idx]
|
|
return None
|
|
|
|
#override
|
|
def on_data_return (self, host_dict, data):
|
|
self.pitch_yaw_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_best_faster(input_path):
|
|
return sort_best(input_path, faster=True)
|
|
|
|
def sort_best(input_path, faster=False):
|
|
target_count = io.input_int ("Target number of faces?", 2000)
|
|
|
|
io.log_info ("Performing sort by best faces.")
|
|
if faster:
|
|
io.log_info("Using faster algorithm. Faces will be sorted by source-rect-area instead of blur.")
|
|
|
|
img_list, trash_img_list = FinalLoaderSubprocessor( pathex.get_image_paths(input_path), faster ).run()
|
|
final_img_list = []
|
|
|
|
grads = 128
|
|
imgs_per_grad = round (target_count / grads)
|
|
|
|
#instead of math.pi / 2, using -1.2,+1.2 because actually maximum yaw for 2DFAN landmarks are -1.2+1.2
|
|
grads_space = np.linspace (-1.2, 1.2,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
|
|
|
|
|
|
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
|
|
|
|
|
|
yaw_pitch_sample_list = [None]*grads
|
|
pitch_grads = imgs_per_grad
|
|
|
|
for g in io.progress_bar_generator ( range (grads), "Sort by pitch"):
|
|
img_list = yaws_sample_list[g]
|
|
if img_list is None:
|
|
continue
|
|
|
|
pitch_sample_list = [None]*pitch_grads
|
|
|
|
grads_space = np.linspace (-math.pi / 2,math.pi / 2, pitch_grads )
|
|
|
|
for pg in range (pitch_grads):
|
|
|
|
pitch = grads_space[pg]
|
|
next_pitch = grads_space[pg+1] if pg < pitch_grads-1 else pitch
|
|
|
|
pitch_samples = []
|
|
for img in img_list:
|
|
s_pitch = img[4]
|
|
if (pg == 0 and s_pitch < next_pitch) or \
|
|
(pg < pitch_grads-1 and s_pitch >= pitch and s_pitch < next_pitch) or \
|
|
(pg == pitch_grads-1 and s_pitch >= pitch):
|
|
pitch_samples += [ img ]
|
|
|
|
if len(pitch_samples) > 0:
|
|
pitch_sample_list[pg] = pitch_samples
|
|
yaw_pitch_sample_list[g] = pitch_sample_list
|
|
|
|
yaw_pitch_sample_list = FinalHistDissimSubprocessor(yaw_pitch_sample_list).run()
|
|
|
|
for g in io.progress_bar_generator (range (grads), "Fetching the best"):
|
|
pitch_sample_list = yaw_pitch_sample_list[g]
|
|
if pitch_sample_list is None:
|
|
continue
|
|
|
|
n = imgs_per_grad
|
|
|
|
while n > 0:
|
|
n_prev = n
|
|
for pg in range(pitch_grads):
|
|
img_list = pitch_sample_list[pg]
|
|
if img_list is None:
|
|
continue
|
|
final_img_list += [ img_list.pop(0) ]
|
|
if len(img_list) == 0:
|
|
pitch_sample_list[pg] = None
|
|
n -= 1
|
|
if n == 0:
|
|
break
|
|
if n_prev == n:
|
|
break
|
|
|
|
for pg in range(pitch_grads):
|
|
img_list = pitch_sample_list[pg]
|
|
if img_list is None:
|
|
continue
|
|
trash_img_list += img_list
|
|
|
|
return final_img_list, trash_img_list
|
|
|
|
"""
|
|
def sort_by_vggface(input_path):
|
|
io.log_info ("Sorting by face similarity using VGGFace model...")
|
|
|
|
model = VGGFace()
|
|
|
|
final_img_list = []
|
|
trash_img_list = []
|
|
|
|
image_paths = pathex.get_image_paths(input_path)
|
|
img_list = [ (x,) for x in image_paths ]
|
|
img_list_len = len(img_list)
|
|
img_list_range = [*range(img_list_len)]
|
|
|
|
feats = [None]*img_list_len
|
|
for i in io.progress_bar_generator(img_list_range, "Loading"):
|
|
img = cv2_imread( img_list[i][0] ).astype(np.float32)
|
|
img = imagelib.normalize_channels (img, 3)
|
|
img = cv2.resize (img, (224,224) )
|
|
img = img[..., ::-1]
|
|
img[..., 0] -= 93.5940
|
|
img[..., 1] -= 104.7624
|
|
img[..., 2] -= 129.1863
|
|
feats[i] = model.predict( img[None,...] )[0]
|
|
|
|
tmp = np.zeros( (img_list_len,) )
|
|
float_inf = float("inf")
|
|
for i in io.progress_bar_generator ( range(img_list_len-1), "Sorting" ):
|
|
i_feat = feats[i]
|
|
|
|
for j in img_list_range:
|
|
tmp[j] = npla.norm(i_feat-feats[j]) if j >= i+1 else float_inf
|
|
|
|
idx = np.argmin(tmp)
|
|
|
|
img_list[i+1], img_list[idx] = img_list[idx], img_list[i+1]
|
|
feats[i+1], feats[idx] = feats[idx], feats[i+1]
|
|
|
|
return img_list, trash_img_list
|
|
"""
|
|
|
|
def sort_by_absdiff(input_path):
|
|
io.log_info ("Sorting by absolute difference...")
|
|
|
|
is_sim = io.input_bool ("Sort by similar?", True, help_message="Otherwise sort by dissimilar.")
|
|
|
|
from core.leras import nn
|
|
|
|
device_config = nn.DeviceConfig.ask_choose_device(choose_only_one=True)
|
|
nn.initialize( device_config=device_config, data_format="NHWC" )
|
|
tf = nn.tf
|
|
|
|
image_paths = pathex.get_image_paths(input_path)
|
|
image_paths_len = len(image_paths)
|
|
|
|
batch_size = 512
|
|
batch_size_remain = image_paths_len % batch_size
|
|
|
|
i_t = tf.placeholder (tf.float32, (None,None,None,None) )
|
|
j_t = tf.placeholder (tf.float32, (None,None,None,None) )
|
|
|
|
outputs_full = []
|
|
outputs_remain = []
|
|
|
|
for i in range(batch_size):
|
|
diff_t = tf.reduce_sum( tf.abs(i_t-j_t[i]), axis=[1,2,3] )
|
|
outputs_full.append(diff_t)
|
|
if i < batch_size_remain:
|
|
outputs_remain.append(diff_t)
|
|
|
|
def func_bs_full(i,j):
|
|
return nn.tf_sess.run (outputs_full, feed_dict={i_t:i,j_t:j})
|
|
|
|
def func_bs_remain(i,j):
|
|
return nn.tf_sess.run (outputs_remain, feed_dict={i_t:i,j_t:j})
|
|
|
|
import h5py
|
|
db_file_path = Path(tempfile.gettempdir()) / 'sort_cache.hdf5'
|
|
db_file = h5py.File( str(db_file_path), "w")
|
|
db = db_file.create_dataset("results", (image_paths_len,image_paths_len), compression="gzip")
|
|
|
|
pg_len = image_paths_len // batch_size
|
|
if batch_size_remain != 0:
|
|
pg_len += 1
|
|
|
|
pg_len = int( ( pg_len*pg_len - pg_len ) / 2 + pg_len )
|
|
|
|
io.progress_bar ("Computing", pg_len)
|
|
j=0
|
|
while j < image_paths_len:
|
|
j_images = [ cv2_imread(x) for x in image_paths[j:j+batch_size] ]
|
|
j_images_len = len(j_images)
|
|
|
|
func = func_bs_remain if image_paths_len-j < batch_size else func_bs_full
|
|
|
|
i=0
|
|
while i < image_paths_len:
|
|
if i >= j:
|
|
i_images = [ cv2_imread(x) for x in image_paths[i:i+batch_size] ]
|
|
i_images_len = len(i_images)
|
|
result = func (i_images,j_images)
|
|
db[j:j+j_images_len,i:i+i_images_len] = np.array(result)
|
|
io.progress_bar_inc(1)
|
|
|
|
i += batch_size
|
|
db_file.flush()
|
|
j += batch_size
|
|
|
|
io.progress_bar_close()
|
|
|
|
next_id = 0
|
|
sorted = [next_id]
|
|
for i in io.progress_bar_generator ( range(image_paths_len-1), "Sorting" ):
|
|
id_ar = np.concatenate ( [ db[:next_id,next_id], db[next_id,next_id:] ] )
|
|
id_ar = np.argsort(id_ar)
|
|
|
|
|
|
next_id = np.setdiff1d(id_ar, sorted, True)[ 0 if is_sim else -1]
|
|
sorted += [next_id]
|
|
db_file.close()
|
|
db_file_path.unlink()
|
|
|
|
img_list = [ (image_paths[x],) for x in sorted]
|
|
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)
|
|
|
|
io.log_info ("Trashing %d items to %s" % ( len(trash_img_list), str(trash_path) ) )
|
|
|
|
for filename in pathex.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) )
|
|
|
|
sort_func_methods = {
|
|
'blur': ("blur", sort_by_blur),
|
|
'motion-blur': ("motion_blur", sort_by_motion_blur),
|
|
'face-yaw': ("face yaw direction", sort_by_face_yaw),
|
|
'face-pitch': ("face pitch direction", sort_by_face_pitch),
|
|
'face-source-rect-size' : ("face rect size in source image", sort_by_face_source_rect_size),
|
|
'hist': ("histogram similarity", sort_by_hist),
|
|
'hist-dissim': ("histogram dissimilarity", sort_by_hist_dissim),
|
|
'brightness': ("brightness", sort_by_brightness),
|
|
'hue': ("hue", sort_by_hue),
|
|
'black': ("amount of black pixels", sort_by_black),
|
|
'origname': ("original filename", sort_by_origname),
|
|
'oneface': ("one face in image", sort_by_oneface_in_image),
|
|
'absdiff': ("absolute pixel difference", sort_by_absdiff),
|
|
'final': ("best faces", sort_best),
|
|
'final-fast': ("best faces faster", sort_best_faster),
|
|
}
|
|
|
|
def main (input_path, sort_by_method=None):
|
|
io.log_info ("Running sort tool.\r\n")
|
|
|
|
if sort_by_method is None:
|
|
io.log_info(f"Choose sorting method:")
|
|
|
|
key_list = list(sort_func_methods.keys())
|
|
for i, key in enumerate(key_list):
|
|
desc, func = sort_func_methods[key]
|
|
io.log_info(f"[{i}] {desc}")
|
|
|
|
io.log_info("")
|
|
id = io.input_int("", 5, valid_list=[*range(len(key_list))] )
|
|
|
|
sort_by_method = key_list[id]
|
|
else:
|
|
sort_by_method = sort_by_method.lower()
|
|
|
|
desc, func = sort_func_methods[sort_by_method]
|
|
img_list, trash_img_list = func(input_path)
|
|
|
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final_process (input_path, img_list, trash_img_list)
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