def change_button_img(self):
# run in Thread
brk = 0
count = 0
while 'playlistOfTheDay' not in self.yandex.play_lists_info:
# wite loging to yandex
time.sleep(.1)
if brk > 100:
break
brk += 1
else:
count = self.yandex.play_lists_info[
'playlistOfTheDay'].play_counter.value
if not count:
return
imgPPM = utils.Image(file=self.path + 'img/PlaylistOftheDay100Num.ppm')
imgPPM.addCount(count, 10, 83, [(77, 200, 73), (82, 201, 79)])
self.bPhotos['playlistOfTheDay'] = imgPPM()
self.lBttns['playlistOfTheDay']['image'] = self.bPhotos[
'playlistOfTheDay']
imgPPM.darker(44, 57, 33)
self.bPhotos['playlistOfTheDayCancel'] = imgPPM()
def parents_parts(num):
offspring = []
for i in range(num):
parent1_img = random.choice(self.current_population)["img"]
parent2_img = random.choice(self.current_population)["img"]
child = {
"img":
utils.Image(imgs=self.imgs,
imgs_shape=self.small_img_shape),
"score":
-1
}
# threshold = np.random.uniform(0,1)
child_indexes = parent1_img.get_index().copy()
# old_child_indexes = child_indexes.copy()
total_num_rc = parent1_img.get_index().shape
num_rows_selected = int(total_num_rc[0] *
self.crossover_percentage[0])
num_cols_selected = int(total_num_rc[1] *
self.crossover_percentage[1])
parent1_parts = tuple([
parent1_img.get_index()
[:num_rows_selected, :num_cols_selected],
parent1_img.get_index()[num_rows_selected:,
num_cols_selected:]
])
parent2_parts = tuple([
parent2_img.get_index()
[:num_rows_selected, :num_cols_selected],
parent2_img.get_index()[num_rows_selected:,
num_cols_selected:]
])
parts_1p = tuple([parent1_parts[0], parent2_parts[0]])
parts_2p = tuple([parent1_parts[1], parent2_parts[1]])
# all parts can swapped together
if (num_rows_selected == total_num_rc[0] - num_rows_selected
and num_cols_selected
== total_num_rc[1] - num_cols_selected):
parts = tuple(
[parts_1p[0], parts_1p[1], parts_2p[0], parts_2p[1]])
child_indexes[:num_rows_selected, :
num_cols_selected] = random.choice(parts)
child_indexes[num_rows_selected:,
num_cols_selected:] = random.choice(parts)
else:
child_indexes[:num_rows_selected, :
num_cols_selected] = random.choice(parts_1p)
child_indexes[num_rows_selected:,
num_cols_selected:] = random.choice(parts_2p)
child["img"].set_index(child_indexes)
child["score"] = self._fitness_single(
child["img"].construct_img())
offspring.append(child)
return offspring
def greedy_parts_v2(gen_num, parent_num=None):
if (parent_num is None):
parent_num = len(self.current_population)
initial_indexes = self.current_population[0]["img"].get_index()
child = {
"img": utils.Image(imgs=self.imgs,
imgs_shape=self.small_img_shape),
"score": -1
}
child_indexes = initial_indexes.copy()
# child["img"].set_index(child_indexes)
# child["score"] = self._fitness_single(child["img"].construct_img())
# print(child["score"])
# input("~~~INPUT DEBUG")
parent_num = min(parent_num, len(self.current_population))
parents = random.choices(self.current_population, k=parent_num)
for index_row, row in tqdm(enumerate(child_indexes)):
for index_col, _ in tqdm(enumerate(row)):
mn_error = 10000000000000000
mn_index = 0
for i in tqdm(range(parent_num)):
parent_indexes = parents[i]["img"].get_index()
for parent_index_row, parent_row in enumerate(
parent_indexes):
for parent_index_col, _ in enumerate(parent_row):
parent_gene_index = parent_indexes[
parent_index_row][parent_index_col]
parent_img_gene = self.imgs[parent_gene_index]
rlim, clim = self.small_img_shape[:2]
input_img_gene = self.input_img[
rlim * index_row:rlim * index_row + rlim,
clim * index_col:clim * index_col + clim]
error = self.calc_error(input_img_gene,
parent_img_gene,
mn_error=mn_error)
if (error < mn_error):
mn_error = error
mn_index = parent_gene_index
child_indexes[index_row][index_col] = mn_index
child["img"].set_index(child_indexes)
child["score"] = self._fitness_single(child["img"].construct_img())
# print(child["score"])
# input("~~~INPUT DEBUG")
offspring = [child for _ in range(gen_num)]
return offspring
def init(images, ri):
print "inside init"
global ReprojectedImage
global TargetImageSize
aEnergy = GetApperanceEnergy(images[ReferenceLabel], images)
for i in range(len(images)):
image = images[i]
klDivergenceEnergy = aEnergy[i]
Images.append(
utils.Image(image=image,
cameraPose=None,
kLDivergenceEnergy=klDivergenceEnergy))
TargetImageSize = len(ri), len(ri[1])
ReprojectedImage = [[{} for _ in range(TargetImageSize[1])]
for _ in range(TargetImageSize[0])]
for i, pixelList in enumerate(ri):
for j, pixelBucket in enumerate(pixelList):
ReprojectedImage[i][j] = CompressPixelList(pixelBucket)
def _generate_population(self, parent=None):
# Totally random population
population = []
if (parent is None):
for _ in range(self.population_size):
chromosome_indexes = np.random.randint(
self.max_index_imgs, size=self.small_imgs_num_rc)
chromosome_img = utils.Image(imgs=self.imgs,
index=chromosome_indexes,
imgs_shape=self.small_img_shape)
# img = chromosome_img.construct_img()
img = chromosome_img
population.append({"img": img, "score": -1})
# According to the parent
else:
population = None
return population
def uniform(gen_num, parent_num=None):
if (gen_num == 0):
return 0
if (parent_num is None):
parent_num = len(self.current_population)
offspring = []
for _ in range(gen_num):
initial_indexes = self.current_population[0]["img"].get_index()
child = {
"img":
utils.Image(imgs=self.imgs,
imgs_shape=self.small_img_shape),
"score":
-1
}
child_indexes = initial_indexes.copy()
parent_num = min(parent_num, len(self.current_population))
parents = random.choices(self.current_population, k=parent_num)
for index_row, row in enumerate(child_indexes):
for index_col, _ in enumerate(row):
random_parent_index = np.random.randint(parent_num)
random_parent_index_row = np.random.randint(
self.small_imgs_num_rc[0])
random_parent_index_col = np.random.randint(
self.small_imgs_num_rc[1])
child_indexes[index_row][index_col] = parents[
random_parent_index]["img"].get_index(
)[random_parent_index_row][random_parent_index_col]
child["img"].set_index(child_indexes)
child["score"] = self._fitness_single(
child["img"].construct_img())
offspring.append(child)
return offspring
def aug_function(inpath, outputpath, incsvfilepath, outcsvfilepath, augtype,
t_factor, r_factor, s_factor):
#print("\n")
#print(augtype)
#print("\n")
file1 = open(incsvfilepath,
'r') # 2. open file containing bounding box coordinates
lines = file1.readlines()
randlist = lines # selecting k random images to augment
for i in tqdm_gui(randlist):
x = i.strip().split(",")
#print("\nx: ")
#print(x)
#print("\n")
mylist = [
int(float(x[1])),
int(float(x[2])),
int(float(x[3])),
int(float(x[4]))
] #extract coordinates from file
cord = []
for i in mylist:
a = int(i)
cord.append(a)
a = x[0] # image filename
imageFolderPath = inpath # 3. input image folder path
loc = os.path.join(imageFolderPath, a) #location of image
# # Brightness and Contrast
if (augtype == 'Brightness and Contrast'):
image = cv.imread(loc)
alpha = random.triangular(1, 2)
beta = random.randint(20, 50)
result = cv.addWeighted(image, alpha,
np.zeros(image.shape, image.dtype), 0,
beta)
oname = "brightness_" + a
csvr = [oname] + mylist
with open(outcsvfilepath, 'a',
newline='') as file: # 4. write to csv file
writer = csv.writer(file)
writer.writerow(csvr)
#print(mylist)
#print(oname)
drawRectangle(result, outputpath, cord, output_name=oname)
else:
img_class = utils.Image(path=loc) # Create image class
img = img_class.getImage()
coord = cord
if (augtype == 'Scale'):
# # Test Scaling
output = img_class.transform('scale', coord, s_factor)
oname = "scaled_" + a # name of output image
ocord = output[1] # augmented coordinates
csvr = [oname
] + output[1] # line to be written to output csv file
with open(outcsvfilepath, 'a',
newline='') as file: # 4. write to csv file
writer = csv.writer(file)
writer.writerow(csvr)
drawRectangle(output[0],
outputpath,
output[1],
output_name=oname) # for saving output image
elif (augtype == 'Rotate'):
#print("\nrotate")
##Test Rotation
output = img_class.transform('rotate', coord, r_factor)
oname = "rotated_" + a
# ##print("ONAME",oname)
# ##print("New Bounding Boxes rotation: ", output[1])
ocord = output[1]
csvr = [oname] + output[1]
# ##print("ocord",ocord)
with open(outcsvfilepath, 'a', newline='') as file:
writer = csv.writer(file)
writer.writerow(csvr)
drawRectangle(output[0],
outputpath,
output[1],
output_name=oname)
#drawRectangle(img,outputpath, coord, output_name = "output_original.jpeg")
elif (augtype == 'Translate'):
#print("\ntranslate")
# # Test Translation - Horizontal
output = img_class.transform('translate', coord, t_factor)
oname = "translated_" + a # name of output image
ocord = output[1] # augmented coordinates
csvr = [oname
] + output[1] # line to be written to output csv file
with open(outcsvfilepath, 'a',
newline='') as file: #write to csv file
writer = csv.writer(file)
writer.writerow(csvr)
drawRectangle(output[0],
outputpath,
output[1],
output_name=oname) # for saving output image
elif (augtype == 'Flip'):
#print("\nflip")
# # Test Flipping
output = img_class.transform('flip', coord)
oname = "flipped_" + a # name of output image
ocord = ' '.join(str(e)
for e in output[1]) # augmented coordinates
csvr = oname + ' ' + ocord # line to be written to output csv file
listcsvr = csvr.split(" ")
#print("\noutcsvfilepath: ")
#print(outcsvfilepath)
#print("\ncsvr: ")
#print(csvr)
with open(outcsvfilepath, 'a',
newline='') as file: #write to csv file
writer = csv.writer(file)
writer.writerow(listcsvr)
drawRectangle(output[0],
outputpath,
output[1],
output_name=oname) # for saving output image
elif (augtype == 'Shear'):
# # Test Shear
#print("\nshear")
output = img_class.transform('shear', coord)
oname = "shear_" + a # name of output image
ocord = output[1] # augmented coordinates
csvr = [oname
] + output[1] # line to be written to output csv file
with open(outcsvfilepath, 'a',
newline='') as file: #write to csv file
writer = csv.writer(file)
writer.writerow(csvr)
drawRectangle(output[0],
outputpath,
output[1],
output_name=oname) # for saving output image
elif (augtype == 'Saturation'):
#print("\nhsv")
HSV_output_name = "Saturated_" + a
img_HSV, bboxes_HSV = utils.RandomHSV(hue=None,
saturation=100,
brightness=None)(
img.copy(),
cord.copy())
drawRectangle(
img_HSV,
outputpath,
bboxes_HSV,
output_name=HSV_output_name) # for saving output image
csvr = [HSV_output_name
] + bboxes_HSV # line to be written to output csv file
with open(outcsvfilepath, 'a',
newline='') as file: #write to csv file
writer = csv.writer(file)
writer.writerow(csvr)
