import math
import multiprocessing
import operator
import os
import sys
import tempfile
from functools import cmp_to_key
from pathlib import Path
import cv2
import numpy as np
from numpy import linalg as npla
from core import imagelib, mathlib, pathex
from core.cv2ex import *
from core.imagelib import estimate_sharpness
from core.interact import interact as io
from core.joblib import Subprocessor
from core.leras import nn
from DFLIMG import *
from facelib import LandmarksProcessor
class BlurEstimatorSubprocessor(Subprocessor):
class Cli(Subprocessor.Cli):
def on_initialize(self, client_dict):
self.estimate_motion_blur = client_dict['estimate_motion_blur']
def process_data(self, data):
filepath = Path( data[0] )
dflimg = DFLIMG.load (filepath)
if dflimg is None or not dflimg.has_data():
self.log_err (f"{filepath.name} is not a dfl image file")
return [ str(filepath), 0 ]
else:
image = cv2_imread( str(filepath) )
face_mask = LandmarksProcessor.get_image_hull_mask (image.shape, dflimg.get_landmarks())
image = (image*face_mask).astype(np.uint8)
if self.estimate_motion_blur:
value = cv2.Laplacian(image, cv2.CV_64F, ksize=11).var()
else:
value = estimate_sharpness(image)
return [ str(filepath), value ]
def get_data_name (self, data):
return data[0]
def __init__(self, input_data, estimate_motion_blur=False ):
self.input_data = input_data
self.estimate_motion_blur = estimate_motion_blur
self.img_list = []
self.trash_img_list = []
super().__init__('BlurEstimator', BlurEstimatorSubprocessor.Cli, 60)
def on_clients_initialized(self):
io.progress_bar ("", len (self.input_data))
def on_clients_finalized(self):
io.progress_bar_close ()
def process_info_generator(self):
cpu_count = multiprocessing.cpu_count()
io.log_info(f'Running on {cpu_count} CPUs')
for i in range(cpu_count):
yield 'CPU%d' % (i), {}, {'estimate_motion_blur':self.estimate_motion_blur}
def get_data(self, host_dict):
if len (self.input_data) > 0:
return self.input_data.pop(0)
return None
def on_data_return (self, host_dict, data):
self.input_data.insert(0, data)
def on_result (self, host_dict, data, result):
if result[1] == 0:
self.trash_img_list.append ( result )
else:
self.img_list.append ( result )
io.progress_bar_inc(1)
def get_result(self):
return self.img_list, self.trash_img_list
def sort_by_blur(input_path):
io.log_info ("Sorting by blur...")
img_list = [ (filename,[]) for filename in pathex.get_image_paths(input_path) ]
img_list, trash_img_list = BlurEstimatorSubprocessor (img_list).run()
io.log_info ("Sorting...")
img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True)
return img_list, trash_img_list
def sort_by_motion_blur(input_path):
io.log_info ("Sorting by motion blur...")
img_list = [ (filename,[]) for filename in pathex.get_image_paths(input_path) ]
img_list, trash_img_list = BlurEstimatorSubprocessor (img_list, estimate_motion_blur=True).run()
io.log_info ("Sorting...")
img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True)
return img_list, trash_img_list
def sort_by_face_yaw(input_path):
io.log_info ("Sorting by face yaw...")
img_list = []
trash_img_list = []
for filepath in io.progress_bar_generator( pathex.get_image_paths(input_path), "Loading"):
filepath = Path(filepath)
dflimg = DFLIMG.load (filepath)
if dflimg is None or not dflimg.has_data():
io.log_err (f"{filepath.name} is not a dfl image file")
trash_img_list.append ( [str(filepath)] )
continue
pitch, yaw, roll = LandmarksProcessor.estimate_pitch_yaw_roll ( dflimg.get_landmarks(), size=dflimg.get_shape()[1] )
img_list.append( [str(filepath), yaw ] )
io.log_info ("Sorting...")
img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True)
return img_list, trash_img_list
def sort_by_face_pitch(input_path):
io.log_info ("Sorting by face pitch...")
img_list = []
trash_img_list = []
for filepath in io.progress_bar_generator( pathex.get_image_paths(input_path), "Loading"):
filepath = Path(filepath)
dflimg = DFLIMG.load (filepath)
if dflimg is None or not dflimg.has_data():
io.log_err (f"{filepath.name} is not a dfl image file")
trash_img_list.append ( [str(filepath)] )
continue
pitch, yaw, roll = LandmarksProcessor.estimate_pitch_yaw_roll ( dflimg.get_landmarks(), size=dflimg.get_shape()[1] )
img_list.append( [str(filepath), pitch ] )
io.log_info ("Sorting...")
img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True)
return img_list, trash_img_list
def sort_by_face_source_rect_size(input_path):
io.log_info ("Sorting by face rect size...")
img_list = []
trash_img_list = []
for filepath in io.progress_bar_generator( pathex.get_image_paths(input_path), "Loading"):
filepath = Path(filepath)
dflimg = DFLIMG.load (filepath)
if dflimg is None or not dflimg.has_data():
io.log_err (f"{filepath.name} is not a dfl image file")
trash_img_list.append ( [str(filepath)] )
continue
source_rect = dflimg.get_source_rect()
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))
img_list.append( [str(filepath), rect_area ] )
io.log_info ("Sorting...")
img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True)
return img_list, trash_img_list
class HistSsimSubprocessor(Subprocessor):
class Cli(Subprocessor.Cli):
def process_data(self, data):
img_list = []
for x in data:
img = cv2_imread(x)
img_list.append ([x, cv2.calcHist([img], [0], None, [256], [0, 256]),
cv2.calcHist([img], [1], None, [256], [0, 256]),
cv2.calcHist([img], [2], None, [256], [0, 256])
])
img_list_len = len(img_list)
for i in range(img_list_len-1):
min_score = float("inf")
j_min_score = i+1
for j in range(i+1,len(img_list)):
score = cv2.compareHist(img_list[i][1], img_list[j][1], cv2.HISTCMP_BHATTACHARYYA) + \
cv2.compareHist(img_list[i][2], img_list[j][2], cv2.HISTCMP_BHATTACHARYYA) + \
cv2.compareHist(img_list[i][3], img_list[j][3], cv2.HISTCMP_BHATTACHARYYA)
if score < min_score:
min_score = score
j_min_score = j
img_list[i+1], img_list[j_min_score] = img_list[j_min_score], img_list[i+1]
self.progress_bar_inc(1)
return img_list
def get_data_name (self, data):
return "Bunch of images"
def __init__(self, img_list ):
self.img_list = img_list
self.img_list_len = len(img_list)
slice_count = 20000
sliced_count = self.img_list_len // slice_count
if sliced_count > 12:
sliced_count = 11.9
slice_count = int(self.img_list_len / sliced_count)
sliced_count = self.img_list_len // slice_count
self.img_chunks_list = [ self.img_list[i*slice_count : (i+1)*slice_count] for i in range(sliced_count) ] + \
[ self.img_list[sliced_count*slice_count:] ]
self.result = []
super().__init__('HistSsim', HistSsimSubprocessor.Cli, 0)
def process_info_generator(self):
cpu_count = len(self.img_chunks_list)
io.log_info(f'Running on {cpu_count} threads')
for i in range(cpu_count):
yield 'CPU%d' % (i), {'i':i}, {}
def on_clients_initialized(self):
io.progress_bar ("Sorting", len(self.img_list))
io.progress_bar_inc(len(self.img_chunks_list))
def on_clients_finalized(self):
io.progress_bar_close()
def get_data(self, host_dict):
if len (self.img_chunks_list) > 0:
return self.img_chunks_list.pop(0)
return None
def on_data_return (self, host_dict, data):
raise Exception("Fail to process data. Decrease number of images and try again.")
def on_result (self, host_dict, data, result):
self.result += result
return 0
def get_result(self):
return self.result
def sort_by_hist(input_path):
io.log_info ("Sorting by histogram similarity...")
img_list = HistSsimSubprocessor(pathex.get_image_paths(input_path)).run()
return img_list, []
class HistDissimSubprocessor(Subprocessor):
class Cli(Subprocessor.Cli):
def on_initialize(self, client_dict):
self.img_list = client_dict['img_list']
self.img_list_len = len(self.img_list)
def process_data(self, data):
i = data[0]
score_total = 0
for j in range( 0, self.img_list_len):
if i == j:
continue
score_total += cv2.compareHist(self.img_list[i][1], self.img_list[j][1], cv2.HISTCMP_BHATTACHARYYA)
return score_total
def get_data_name (self, data):
return self.img_list[data[0]][0]
def __init__(self, img_list ):
self.img_list = img_list
self.img_list_range = [i for i in range(0, len(img_list) )]
self.result = []
super().__init__('HistDissim', HistDissimSubprocessor.Cli, 60)
def on_clients_initialized(self):
io.progress_bar ("Sorting", len (self.img_list) )
def on_clients_finalized(self):
io.progress_bar_close()
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), {}, {'img_list' : self.img_list}
def get_data(self, host_dict):
if len (self.img_list_range) > 0:
return [self.img_list_range.pop(0)]
return None
def on_data_return (self, host_dict, data):
self.img_list_range.insert(0, data[0])
def on_result (self, host_dict, data, result):
self.img_list[data[0]][2] = result
io.progress_bar_inc(1)
def get_result(self):
return self.img_list
def sort_by_hist_dissim(input_path):
io.log_info ("Sorting by histogram dissimilarity...")
img_list = []
trash_img_list = []
for filepath in io.progress_bar_generator( pathex.get_image_paths(input_path), "Loading"):
filepath = Path(filepath)
dflimg = DFLIMG.load (filepath)
image = cv2_imread(str(filepath))
if dflimg is not None and dflimg.has_data():
face_mask = LandmarksProcessor.get_image_hull_mask (image.shape, dflimg.get_landmarks())
image = (image*face_mask).astype(np.uint8)
img_list.append ([str(filepath), cv2.calcHist([cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)], [0], None, [256], [0, 256]), 0 ])
img_list = HistDissimSubprocessor(img_list).run()
io.log_info ("Sorting...")
img_list = sorted(img_list, key=operator.itemgetter(2), reverse=True)
return img_list, trash_img_list
def sort_by_brightness(input_path):
io.log_info ("Sorting by brightness...")
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") ]
io.log_info ("Sorting...")
img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True)
return img_list, []
def sort_by_hue(input_path):
io.log_info ("Sorting by hue...")
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") ]
io.log_info ("Sorting...")
img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True)
return img_list, []
def sort_by_black(input_path):
io.log_info ("Sorting by amount of black pixels...")
img_list = []
for x in io.progress_bar_generator( pathex.get_image_paths(input_path), "Loading"):
img = cv2_imread(x)
img_list.append ([x, img[(img == 0)].size ])
io.log_info ("Sorting...")
img_list = sorted(img_list, key=operator.itemgetter(1), reverse=False)
return img_list, []
def sort_by_origname(input_path):
io.log_info ("Sort by original filename...")
img_list = []
trash_img_list = []
for filepath in io.progress_bar_generator( pathex.get_image_paths(input_path), "Loading"):
filepath = Path(filepath)
dflimg = DFLIMG.load (filepath)
if dflimg is None or not dflimg.has_data():
io.log_err (f"{filepath.name} is not a dfl image file")
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 = pathex.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
io.log_info ("Nothing found. Possible recover original filenames first.")
return [], []
class FinalLoaderSubprocessor(Subprocessor):
class Cli(Subprocessor.Cli):
def on_initialize(self, client_dict):
self.faster = client_dict['faster']
def process_data(self, data):
filepath = Path(data[0])
try:
dflimg = DFLIMG.load (filepath)
if dflimg is None or not dflimg.has_data():
self.log_err (f"{filepath.name} is not a dfl image file")
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)
if self.faster:
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 ] ]
def get_data_name (self, data):
return data[0]
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)
def on_clients_initialized(self):
io.progress_bar ("Loading", len (self.img_list))
def on_clients_finalized(self):
io.progress_bar_close()
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}
def get_data(self, host_dict):
if len (self.img_list) > 0:
return [self.img_list.pop(0)]
return None
def on_data_return (self, host_dict, data):
self.img_list.insert(0, data[0])
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)
def get_result(self):
return self.result, self.result_trash
class FinalHistDissimSubprocessor(Subprocessor):
class Cli(Subprocessor.Cli):
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
def get_data_name (self, data):
return "Bunch of images"
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)
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), {}, {}
def on_clients_initialized(self):
io.progress_bar ("Sort by hist-dissim", len(self.pitch_yaw_sample_list_idxs) )
def on_clients_finalized(self):
io.progress_bar_close()
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
def on_data_return (self, host_dict, data):
self.pitch_yaw_sample_list_idxs.insert(0, data[0])
def on_result (self, host_dict, data, result):
idx, yaws_sample_list = data
self.result[idx] = yaws_sample_list
io.progress_bar_inc(1)
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)
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)
final_process (input_path, img_list, trash_img_list)
