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forcing all tqdm's to ascii in order to work properly on Chinese windows

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iperov 2019-01-24 21:32:07 +04:00
parent 1e7a0836f7
commit 72646becd1
5 changed files with 24 additions and 25 deletions
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2
mainscripts/Converter.py
View file

@ -259,7 +259,7 @@ def main (input_dir, output_dir, model_dir, model_name, aligned_dir=None, **in_o
alignments = {} alignments = {}
aligned_path_image_paths = Path_utils.get_image_paths(aligned_path) aligned_path_image_paths = Path_utils.get_image_paths(aligned_path)
for filename in tqdm(aligned_path_image_paths, desc= "Collecting alignments" ): for filename in tqdm(aligned_path_image_paths, desc="Collecting alignments", ascii=True ):
dflpng = DFLPNG.load( str(filename), print_on_no_embedded_data=True ) dflpng = DFLPNG.load( str(filename), print_on_no_embedded_data=True )
if dflpng is None: if dflpng is None:
continue continue

1
mainscripts/Extractor.py
View file

@ -3,7 +3,6 @@ import os
import sys import sys
import time import time
import multiprocessing import multiprocessing
from tqdm import tqdm
from pathlib import Path from pathlib import Path
import numpy as np import numpy as np
import cv2 import cv2

36
mainscripts/Sorter.py
View file

@ -131,14 +131,14 @@ def sort_by_blur(input_path):
def sort_by_brightness(input_path): def sort_by_brightness(input_path):
print ("Sorting by brightness...") 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") ] 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", ascii=True) ]
print ("Sorting...") print ("Sorting...")
img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True) img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True)
return img_list return img_list
def sort_by_hue(input_path): def sort_by_hue(input_path):
print ("Sorting by hue...") 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") ] 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", ascii=True) ]
print ("Sorting...") print ("Sorting...")
img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True) img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True)
return img_list return img_list
@ -148,7 +148,7 @@ def sort_by_face(input_path):
print ("Sorting by face similarity...") print ("Sorting by face similarity...")
img_list = [] img_list = []
for filepath in tqdm( Path_utils.get_image_paths(input_path), desc="Loading"): for filepath in tqdm( Path_utils.get_image_paths(input_path), desc="Loading", ascii=True):
filepath = Path(filepath) filepath = Path(filepath)
if filepath.suffix != '.png': if filepath.suffix != '.png':
@ -163,7 +163,7 @@ def sort_by_face(input_path):
img_list_len = len(img_list) img_list_len = len(img_list)
for i in tqdm ( range(0, img_list_len-1), desc="Sorting"): for i in tqdm ( range(0, img_list_len-1), desc="Sorting", ascii=True):
min_score = float("inf") min_score = float("inf")
j_min_score = i+1 j_min_score = i+1
for j in range(i+1,len(img_list)): for j in range(i+1,len(img_list)):
@ -184,7 +184,7 @@ def sort_by_face_dissim(input_path):
print ("Sorting by face dissimilarity...") print ("Sorting by face dissimilarity...")
img_list = [] img_list = []
for filepath in tqdm( Path_utils.get_image_paths(input_path), desc="Loading"): for filepath in tqdm( Path_utils.get_image_paths(input_path), desc="Loading", ascii=True):
filepath = Path(filepath) filepath = Path(filepath)
if filepath.suffix != '.png': if filepath.suffix != '.png':
@ -198,7 +198,7 @@ def sort_by_face_dissim(input_path):
img_list.append( [str(filepath), dflpng.get_landmarks(), 0 ] ) img_list.append( [str(filepath), dflpng.get_landmarks(), 0 ] )
img_list_len = len(img_list) img_list_len = len(img_list)
for i in tqdm( range(0, img_list_len-1), desc="Sorting"): for i in tqdm( range(0, img_list_len-1), desc="Sorting", ascii=True):
score_total = 0 score_total = 0
for j in range(i+1,len(img_list)): for j in range(i+1,len(img_list)):
if i == j: if i == j:
@ -217,7 +217,7 @@ def sort_by_face_dissim(input_path):
def sort_by_face_yaw(input_path): def sort_by_face_yaw(input_path):
print ("Sorting by face yaw...") print ("Sorting by face yaw...")
img_list = [] img_list = []
for filepath in tqdm( Path_utils.get_image_paths(input_path), desc="Loading"): for filepath in tqdm( Path_utils.get_image_paths(input_path), desc="Loading", ascii=True):
filepath = Path(filepath) filepath = Path(filepath)
if filepath.suffix != '.png': if filepath.suffix != '.png':
@ -436,7 +436,7 @@ def sort_by_hist_dissim(input_path):
print ("Sorting by histogram dissimilarity...") print ("Sorting by histogram dissimilarity...")
img_list = [] img_list = []
for filename_path in tqdm( Path_utils.get_image_paths(input_path), desc="Loading"): for filename_path in tqdm( Path_utils.get_image_paths(input_path), desc="Loading", ascii=True):
image = cv2.imread(filename_path) image = cv2.imread(filename_path)
dflpng = DFLPNG.load( str(filename_path) ) dflpng = DFLPNG.load( str(filename_path) )
@ -563,7 +563,7 @@ def sort_final(input_path):
grads_space = np.linspace (-255,255,grads) grads_space = np.linspace (-255,255,grads)
yaws_sample_list = [None]*grads yaws_sample_list = [None]*grads
for g in tqdm ( range(grads), desc="Sort by yaw" ): for g in tqdm ( range(grads), desc="Sort by yaw", ascii=True ):
yaw = grads_space[g] yaw = grads_space[g]
next_yaw = grads_space[g+1] if g < grads-1 else yaw next_yaw = grads_space[g+1] if g < grads-1 else yaw
@ -578,7 +578,7 @@ def sort_final(input_path):
yaws_sample_list[g] = yaw_samples yaws_sample_list[g] = yaw_samples
total_lack = 0 total_lack = 0
for g in tqdm ( range (grads), desc="" ): for g in tqdm ( range (grads), desc="", ascii=True ):
img_list = yaws_sample_list[g] img_list = yaws_sample_list[g]
img_list_len = len(img_list) if img_list is not None else 0 img_list_len = len(img_list) if img_list is not None else 0
@ -588,7 +588,7 @@ def sort_final(input_path):
imgs_per_grad += total_lack // grads imgs_per_grad += total_lack // grads
sharpned_imgs_per_grad = imgs_per_grad*10 sharpned_imgs_per_grad = imgs_per_grad*10
for g in tqdm ( range (grads), desc="Sort by blur" ): for g in tqdm ( range (grads), desc="Sort by blur", ascii=True ):
img_list = yaws_sample_list[g] img_list = yaws_sample_list[g]
if img_list is None: if img_list is None:
continue continue
@ -605,7 +605,7 @@ def sort_final(input_path):
yaws_sample_list[g] = img_list yaws_sample_list[g] = img_list
for g in tqdm ( range (grads), desc="Sort by hist" ): for g in tqdm ( range (grads), desc="Sort by hist", ascii=True ):
img_list = yaws_sample_list[g] img_list = yaws_sample_list[g]
if img_list is None: if img_list is None:
continue continue
@ -620,7 +620,7 @@ def sort_final(input_path):
yaws_sample_list[g] = sorted(img_list, key=operator.itemgetter(3), reverse=True) yaws_sample_list[g] = sorted(img_list, key=operator.itemgetter(3), reverse=True)
for g in tqdm ( range (grads), desc="Fetching best" ): for g in tqdm ( range (grads), desc="Fetching best", ascii=True ):
img_list = yaws_sample_list[g] img_list = yaws_sample_list[g]
if img_list is None: if img_list is None:
continue continue
@ -634,7 +634,7 @@ def sort_by_black(input_path):
print ("Sorting by amount of black pixels...") print ("Sorting by amount of black pixels...")
img_list = [] img_list = []
for x in tqdm( Path_utils.get_image_paths(input_path), desc="Loading"): for x in tqdm( Path_utils.get_image_paths(input_path), desc="Loading", ascii=True):
img = cv2.imread(x) img = cv2.imread(x)
img_list.append ([x, img[(img == 0)].size ]) img_list.append ([x, img[(img == 0)].size ])
@ -654,7 +654,7 @@ def final_process(input_path, img_list, trash_img_list):
for filename in Path_utils.get_image_paths(trash_path): for filename in Path_utils.get_image_paths(trash_path):
Path(filename).unlink() Path(filename).unlink()
for i in tqdm( range(len(trash_img_list)), desc="Moving trash" , leave=False): for i in tqdm( range(len(trash_img_list)), desc="Moving trash" , leave=False, ascii=True):
src = Path (trash_img_list[i][0]) src = Path (trash_img_list[i][0])
dst = trash_path / src.name dst = trash_path / src.name
try: try:
@ -664,7 +664,7 @@ def final_process(input_path, img_list, trash_img_list):
print ("") print ("")
for i in tqdm( range(len(img_list)), desc="Renaming" , leave=False): for i in tqdm( range(len(img_list)), desc="Renaming" , leave=False, ascii=True):
src = Path (img_list[i][0]) src = Path (img_list[i][0])
dst = input_path / ('%.5d_%s' % (i, src.name )) dst = input_path / ('%.5d_%s' % (i, src.name ))
try: try:
@ -672,7 +672,7 @@ def final_process(input_path, img_list, trash_img_list):
except: except:
print ('fail to rename %s' % (src.name) ) print ('fail to rename %s' % (src.name) )
for i in tqdm( range(len(img_list)) , desc="Renaming" ): for i in tqdm( range(len(img_list)) , desc="Renaming", ascii=True ):
src = Path (img_list[i][0]) src = Path (img_list[i][0])
src = input_path / ('%.5d_%s' % (i, src.name)) src = input_path / ('%.5d_%s' % (i, src.name))
@ -686,7 +686,7 @@ def sort_by_origname(input_path):
print ("Sort by original filename...") print ("Sort by original filename...")
img_list = [] img_list = []
for filepath in tqdm( Path_utils.get_image_paths(input_path), desc="Loading"): for filepath in tqdm( Path_utils.get_image_paths(input_path), desc="Loading", ascii=True):
filepath = Path(filepath) filepath = Path(filepath)
if filepath.suffix != '.png': if filepath.suffix != '.png':

8
samples/SampleLoader.py
View file

@ -27,7 +27,7 @@ class SampleLoader:
if sample_type == SampleType.IMAGE: if sample_type == SampleType.IMAGE:
if datas[sample_type] is None: if datas[sample_type] is None:
datas[sample_type] = [ Sample(filename=filename) for filename in tqdm( Path_utils.get_image_paths(samples_path), desc="Loading" ) ] datas[sample_type] = [ Sample(filename=filename) for filename in tqdm( Path_utils.get_image_paths(samples_path), desc="Loading", ascii=True ) ]
elif sample_type == SampleType.FACE: elif sample_type == SampleType.FACE:
if datas[sample_type] is None: if datas[sample_type] is None:
@ -53,7 +53,7 @@ class SampleLoader:
def upgradeToFaceSamples ( samples ): def upgradeToFaceSamples ( samples ):
sample_list = [] sample_list = []
for s in tqdm( samples, desc="Loading" ): for s in tqdm( samples, desc="Loading", ascii=True ):
s_filename_path = Path(s.filename) s_filename_path = Path(s.filename)
if s_filename_path.suffix != '.png': if s_filename_path.suffix != '.png':
@ -78,7 +78,7 @@ class SampleLoader:
yaw_samples_len = len(yaw_samples) yaw_samples_len = len(yaw_samples)
sample_list = [] sample_list = []
for i in tqdm( range(yaw_samples_len), desc="Sorting" ): for i in tqdm( range(yaw_samples_len), desc="Sorting", ascii=True ):
if yaw_samples[i] is not None: if yaw_samples[i] is not None:
for s in yaw_samples[i]: for s in yaw_samples[i]:
s_t = [] s_t = []
@ -114,7 +114,7 @@ class SampleLoader:
yaws_sample_list = [None]*gradations yaws_sample_list = [None]*gradations
for i in tqdm( range(0, gradations), desc="Sorting" ): for i in tqdm( range(0, gradations), desc="Sorting", ascii=True ):
yaw = lowest_yaw + i*diff_rot_per_grad yaw = lowest_yaw + i*diff_rot_per_grad
next_yaw = lowest_yaw + (i+1)*diff_rot_per_grad next_yaw = lowest_yaw + (i+1)*diff_rot_per_grad

2
utils/SubprocessorBase.py
View file

@ -134,7 +134,7 @@ class SubprocessorBase(object):
print ( self.get_no_process_started_message() ) print ( self.get_no_process_started_message() )
return None return None
self.progress_bar = tqdm( total=self.onHostGetProgressBarLen(), desc=self.onHostGetProgressBarDesc() ) self.progress_bar = tqdm( total=self.onHostGetProgressBarLen(), desc=self.onHostGetProgressBarDesc(), ascii=True )
self.onHostClientsInitialized() self.onHostClientsInitialized()
try: try: