def segment_characters(image, rows, cols):
# Extract characters from image.
countCharacters = 0
coordinates = []
amOnCharacter = False
i = 0
for col in cols:
if 1 in col:
if not amOnCharacter:
coordinates.append(i)
countCharacters += 1
amOnCharacter = True
else:
if amOnCharacter:
coordinates.append(i)
amOnCharacter = False
i += 1
if (len(coordinates) % 2 != 0):
coordinates.append(i)
for i in range(0, len(coordinates), 2):
cropped_image = image.crop(
(coordinates[i], 0, image.size[0] - (image.size[0] - (coordinates[i + 1])), image.size[1]))
newfilepath = "CharacterDetection/" + str(i // 2 + 1) + ".png"
cropped_image.save(newfilepath)
return coordinates
def extractLinesFromImage(lines, image):
dir_name = "LineDetection/"
i = 1
for line in lines:
cropped_image = image.crop(
(0, line[0], image.size[0], line[1]))
newfilepath = dir_name + str(i) + ".png"
cropped_image.save(newfilepath)
i += 1
def dump_clothing_test_data():
for index, item in test_labels_clothes.iterrows():
print(item['filename'])
image = find_image(item['filename'])
print(os.getcwd())
#crop the image from the bounding boxes
t = image.crop((item['xmin'], item['ymin'], item['xmax'], item['ymax']))
os.chdir('data/transfer_learning/clothes/test_set ') #Incomplete make sure making test/train split
os.chdir(str(item['classes']))
t.save(fp = item['filename'])
os.chdir("../../../../..")
def dump_clothing_training_data():
for index, item in train_labels_clothes.iterrows():
print(item['filename'])
image = find_image(item['filename'])
print(os.getcwd())
#crop the image from the bounding boxes
t = image.crop((item['xmin'] + margin, item['ymin'] + marginHead , item['xmax'] - margin, item['ymax'] - margin))
os.chdir('data/transfer_learning/clothes/training_set ')
os.chdir(str(item['classes']))
t.save(fp = item['filename'])
os.chdir("../../../../..")
def dump_test_color_data():
for index, item in test_labels_colors.iterrows():
print(item['filename'])
image = find_image(item['filename'])
print(os.getcwd())
#crop the image from the bounding boxes
t = image.crop((item['xmin'] + margin, item['ymin'] + marginHead, item['xmax'] - margin, item['ymax'] - margin))
os.chdir('data/transfer_learning/colors/test_set ')
os.chdir(str(item['major_color']))
t.save(fp = item['filename'])
os.chdir("../../../../..")
def rescale_image(image):
size = np.asarray(image.size)
size = (size * IMAGE_SIZE / min(size)).astype(int)
image = image.resize(size, resample=Image.LANCZOS)
w, h = image.size
image = image.crop((
(w - IMAGE_SIZE) // 2,
(h - IMAGE_SIZE) // 2,
(w + IMAGE_SIZE) // 2,
(h + IMAGE_SIZE) // 2)
)
return image
def dump_test_pattern_data():
for index, item in test_labels_pattern.iterrows():
print(item['filename'])
image = find_image(item['filename'])
print(os.getcwd())
marginHead = 20
margin = 10
#crop the image from the bounding boxes
t = image.crop((item['xmin'] + margin, item['ymin'] + marginHead, item['xmax'] - margin, item['ymax'] - margin))
os.chdir('data/transfer_learning/pattern/test_set ')
os.chdir(str(item['clothing_pattern']))
t.save(fp = item['filename'])
os.chdir("../../../../..")
def processImage(imagePath):
image = Image.open(imagePath)
image = image.crop(bbox)
(currw, currh) = image.size
if currw >= currh:
image = image.resize((width, int(float(currh) * float(width) / float(currw))))
else:
image = image.resize((int(float(currw) * float(height) / float(currh)), height))
background = Image.new('RGB', (width, height), (0, 0, 0))
background.paste(
image, (int((width - image.size[0]) / 2), int((height - image.size[1]) / 2))
)
return background
def cropImage(image, x1, y1, x2, y2): ima=image.crop((x1,y1,x2,y2)) return ima
def threshold_slow(image):
#Transformar a imagem em array de bytes
image_process = np.array(image)
#Pasta onde fica alojados os arquivos cortados
path_crop_save = save_path + "/crop/"
print("Carregando imagem....")
#Nome padrao para os arquivos
file_name_save = "/imgpixel"
text = ""
#Obtendo a dimenssao da imagem escolhida
largura, altura = image.size
#Fazendo o calculo da proporcao
proporcao = largura / altura
print("Largura: " + str(largura))
print("Altura: " + str(altura))
print("Proporcao: " + str(proporcao))
#Obtendo o pixel da imagem
pixels = list(image.getdata())
numl1 = numl2 = numl3 = numl4 = numl5 = 0
#print("Pixels: "+str(pixels))
#Loop para percorrer a imagem
for y in range(0, altura - 50):
print('linha', y)
for x in range(0, largura - 50):
#Inicialmente e realizado o corte na dimensao de 50x50
#Crop (Esquerda, cima, direita, baixo)
new_image = image.crop((x, y, x + 50, y + 50)).copy()
#A imagem cortada e transformada em array pois o metodo de predicacao so aceita iamgem em array
crop_img_array = np.array(new_image)
#Essa nova imagem cortada esta em 3 canais(RGB), com isso e acionado o metodo load
#para transformar em array e em um canal (graysacle)
#img_gray = load(new_image)
img_gray = new_image.convert('L')
# img_gray.show()
time.sleep(0.5)
img_gray = (np.asarray(img_gray).reshape(1, 50, 50, 1)) / 255.0
#A ideia e marcar o pixel central, na imagem orignal,da que foi cortada para verificar a execucao
#com isso ha o metodo threshold no opencv para binarizar a iamgem e posteriormente
#calcular o momento da binarizacao da imagem, que isto e realizado pelo metodo moments
# ret,thresh = cv2.threshold(img_gray,127,255,0)
# M = cv2.moments(thresh)
image_class = loaded_model.predict(img_gray, verbose=0)
print(">>>>>>>" + str(image_class))
#Atribui o valor da classe classificada
# print(img_gray)
#exit()
pred = image_class.argmax(axis=-1)
''' Lembrando que o 0 e o numero 7 na regua, bem como
o 1 e o 8, 2 e o 13 e o 3 e 14, ja para finalizar
4 sao as negativas ou lixo
'''
if pred == 0:
numl1 = numl1 + 1
# time.sleep(0.5)
#Cada corte e salvo na pasta declarada abaixo para comparar se realmente esta fazendo correto
crop_write = ""
crop_write = path_crop_save + "L1/" + file_name_save + "{0}-{1}".format(
x, y) + ".jpeg"
cv2.imwrite(crop_write, crop_img_array)
#Aqui e localizado o centro da imagem, ou seja, do 50x50 e depois marca com a cor de cada camada
#declarada, neste caso a verde sao para identificar o numero 7 na regua.
cX = x + 25
cY = y + 25
image_process = cv2.circle(image_process, (cX, cY), 5, verde,
-1)
cv2.imwrite(save_path + "regua.jpg", image_process)
elif pred == 1:
numl2 = numl2 + 1
# time.sleep(0.5)
crop_write = ""
crop_write = path_crop_save + "L2/" + file_name_save + "{0}-{1}".format(
x, y) + ".jpeg"
cv2.imwrite(crop_write, crop_img_array)
cX = x + 25
cY = y + 25
image_process = cv2.circle(image_process, (cX, cY), 5, amarelo,
-1)
cv2.imwrite(save_path + "regua.jpg", image_process)
elif pred == 2:
numl3 = numl3 + 1
# time.sleep(0.5)
crop_write = ""
crop_write = path_crop_save + "L3/" + file_name_save + "{0}-{1}".format(
x, y) + ".jpeg"
cv2.imwrite(crop_write, crop_img_array)
cX = x + 25
cY = y + 25
image_process = cv2.circle(image_process, (cX, cY), 5, laranja,
-1)
cv2.imwrite(save_path + "regua.jpg", image_process)
elif pred == 3:
numl4 = numl4 + 1
# time.sleep(0.5)
crop_write = ""
crop_write = path_crop_save + "L4/" + file_name_save + "{0}-{1}".format(
x, y) + ".jpeg"
cv2.imwrite(crop_write, crop_img_array)
cX = x + 25
cY = y + 25
image_process = cv2.circle(image_process, (cX, cY), 5,
vermelho, -1)
cv2.imwrite(save_path + "regua.jpg", image_process)
else:
#print("Nernhum")
numl5 = numl5 + 1
print(str(numl5) + "ª vez")
#crop_write = ""
#crop_write = path_crop_save+"L5/"+file_name_save+"{0}-{1}".format(x,y)+".jpeg"
#cv2.imwrite(crop_write,crop_img_array)
#cX=x+25
#cY=y+25
#image_process = cv2.circle(image_process, (cX, cY), 5, azul, -1)
#cv2.imwrite(save_path+"regua.jpg",image_process)
print(numl1, numl2, numl3, numl4, numl5)
rec_channels = array(rec_channels, dtype=uint8)
pil_channels = [Image.fromarray(channel) for channel in rec_channels]
# image and compress image
# rec_image = Image.merge("YCbCr", channels).convert('RGB')
rec_pil_image = Image.merge("YCbCr", pil_channels).convert('RGB')
# compute file size
# rec_pil_image
# f2 = BytesIO()
# rec_pil_image.save(f2, format='JPEG', quality=75) # qtables=pil_qtables)
# rec_pil_image_size = len(f2.getvalue())
return rec_pil_image
if __name__ == '__main__':
image = Image.open(
'/home/imagenet-data/train//n03977966/n03977966_11273.JPEG')
image_size = min(image.size)
crop_image = image.crop((0, 0, image_size, image_size))
f1 = BytesIO()
crop_image.save(f1, format='JPEG', quality=75) # qtables=pil_qtables)
print("f1", len(f1.getvalue()))
deepn_image = DeepN(crop_image)
f2 = BytesIO()
deepn_image.save(f2, format='JPEG', quality=75) # qtables=pil_qtables)
print("f2", len(f2.getvalue()))
display_str = ''
if classes[j] in category_index.keys():
class_name = str(category_index[classes[j]]['name'])
else:
class_name = 'NA'
display_str = str(class_name)
if class_name in labels_whitelist:
display_str = '{}_{}'.format(display_str,
int(100 * scores[j]))
print('cropping box {0} ({1}, {2}, {3}, {4})'.format(
display_str, xmin_px, ymin_px, xmax_px, ymax_px))
cropped_img = image.crop(
(xmin_px, ymin_px, xmax_px, ymax_px))
with tf.gfile.Open(
os.path.join(
PATH_TO_TEST_IMAGES_CROP_RES_DIR,
'{0}_{1}_{2}.jpg'.format(
image_path_filename, display_str, j)),
'w') as fid:
cropped_img.save(fid, 'JPEG')
features = get_feature(cropped_img)
w.writerow({
"filename": image_path,
"object_class": class_name,
"score": scores[j],
"ymin": ymin,
"xmin": xmin,
def cropAndAddPadding(image, newfilepath):
pix_val = list(image.getdata())
i = 0
row = []
rows= []
el = 0
for element in pix_val:
if element[0] == 255:
el = 0
else:
el = 1
if i % image.size[0] == 0 and i != 0:#image.size returns a tuple (width, height)
rows.append(row)
row = []
if i % image.size[0] != 0:
row.append(el)
i += 1
col = []
cols= []
for i in range(0,len(rows[0])): #za sekoj chlen od prviot row
col = []
for j in range(0,len(rows)): #za sekoj row
col.append(rows[j][i])
cols.append(col)
# Cropping
cropFromTop = 0
cropFromBottom = 0
for row in rows:
if not(1 in row):
cropFromTop += 1
else:
break
reversed_rows = rows[::-1]
for row in reversed_rows:
if not(1 in row):
cropFromBottom += 1
else:
break
cropFromLeft = 0
cropFromRight = 0
for col in cols:
if not(1 in col):
cropFromLeft += 1
else:
break
for col in cols[::-1]:
if not(1 in col):
cropFromRight += 1
else:
break
cropped_image = image.crop((cropFromLeft, cropFromTop, image.size[0]-cropFromRight, image.size[1]-cropFromBottom))
cropped_image.save(newfilepath)
size = (50, 50)
resized_image = cropped_image
padding = 0
im = cv2.imread(newfilepath)
color = [255, 255, 255]
if not (resized_image.size[0] == resized_image.size[1]):
if resized_image.size[0] > resized_image.size[1]:
padding = (resized_image.size[0]-resized_image.size[1])//2
croppedThenPadded = cv2.copyMakeBorder(
im, padding, padding, 0, 0, cv2.BORDER_CONSTANT, value=color)
else:
padding = (resized_image.size[1]-resized_image.size[0])//2
croppedThenPadded = cv2.copyMakeBorder(
im, 0, 0, padding, padding, cv2.BORDER_CONSTANT, value=color)
sv = Image.fromarray(croppedThenPadded)
sv.save(newfilepath)
x = sv.resize((size[0], size[1]), Image.LANCZOS)
x.save(newfilepath)
def cropImage(image, x1, y1, x2, y2):
#utils.raiseNotDefined()
cropped_image = image.crop((x1, y1, x2, y2))
return cropped_image
def crop_image(image, enable = 0):
if enable:
return image.crop((151, 151, image.size[0] - 150, image.size[1] - 150))
else:
return image