def generate_shoe():
base64Img = request.args.get('image')
base64Img = base64Img.replace(" ", "+")
base64Img = Helper.get_fixed_base64_image(base64Img)
decoded_img = base64.b64decode(base64Img)
img_buffer = BytesIO(decoded_img)
imageData = Image.open(img_buffer).convert('LA')
#num_channel = len(imageData.split())
#print("num_channel:", num_channel)
img = ImageOps.fit(imageData, image_shape)
img_tensor = transforms(img)
img_tensor = img_tensor.unsqueeze(0)
with torch.no_grad():
generated_image = gen(img_tensor[:, 1:2, :, :])
print("generated_image.shape : ", generated_image.shape)
save_image(generated_image[0], "image1.png")
with open("image1.png", "rb") as image_file:
encoded_string = base64.b64encode(image_file.read()).decode('ascii')
return jsonify("data:image/png;base64," + encoded_string)
def generate_shoe_by_hed():
base64Img = request.args.get('image')
base64Img = base64Img.replace(" ", "+")
""""""
base64Img = Helper.get_fixed_base64_image(base64Img)
decoded_img = base64.b64decode(base64Img)
img_buffer = BytesIO(decoded_img)
imageData = Image.open(img_buffer).convert('LA')
img = ImageOps.fit(imageData, image_shape)
img_tensor = transforms(img)
img_tensor = img_tensor.unsqueeze(0)
print("img_tensor.shape : ", img_tensor.shape)
img = cv2.imdecode(img, cv2.IMREAD_COLOR)
cv2.imshow(img)
cv2.waitKey(0)
with torch.no_grad():
generated_image = gen(img_tensor[:, 0:1, :, :])
print("generated_image.shape : ", generated_image.shape)
save_image(generated_image[0], "image1.png")
with open("image1.png", "rb") as image_file:
encoded_string = base64.b64encode(image_file.read()).decode('ascii')
return jsonify("data:image/png;base64," + encoded_string)