def getRowsFromImage(image):
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
# image.size returns a tuple (width, height)
if i % image.size[0] == 0 and i != 0:
rows.append(row)
row = []
if i % image.size[0] != 0:
row.append(el)
i += 1
return rows
def load_image_into_numpy_array(self, image):
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape(
(im_height, im_width, 3)).astype(np.uint8)
data = pd.DataFrame(data=content, columns=new_cols)
data.class_name = data.class_name.map({'trafficlight': 1, 'speedlimit': 2, 'crosswalk': 3, 'stop': 4})
print(data.shape)
# data.head()
print("Waiting. . .")
data1 = []
i = 0
for a in data.path_name.values:
image = Image.open("..\\resources\\" + a).convert("RGB")
# Image resizing is needed to upgrade the resolution
image = image.resize((224, 224), Image.ANTIALIAS)
image = np.array(image.getdata()).reshape(224, 224, 3)
data1.append(image)
print("---Done---")
X = np.array(data1)
y = np.array(data.iloc[:, -1], dtype=int)
c = to_categorical(y, dtype=int)
Y = c[:, 1:]
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=787)
print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
# Layers definition
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) + "ยช 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)
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 load_image_into_numpy_array(image): (im_width, im_height) = image.size # For colored image model: # return np.array(image.getdata()).reshape((im_height, im_width, 3)).astype(np.uint8) return np.array(image.getdata()).reshape((im_height, im_width, 3)).astype(np.uint8)