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import os
import cv2
import torch
import random
import numpy as np
import gradio as gr
from util import util
from util.img2pixl import pixL
from data import create_dataset
from models import create_model
from options.test_options import TestOptions
opt = TestOptions().parse()
opt.num_threads = 0
opt.batch_size = 1
opt.display_id = -1
opt.no_dropout = True
model = create_model(opt)
model.setup(opt)
num_inferences = 0
def preprocess(image):
im_type = None
imgH, imgW = image.shape[:2]
aspect_ratio = imgW / imgH
if 0.75 <= aspect_ratio <= 1.75:
image = cv2.resize(image, (512, 512))
image = pixL().toThePixL(image,6,False)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = np.asarray([image])
image = np.transpose(image, (0, 3, 1, 2))
image = inference(image)
return image
elif 1.75 <= aspect_ratio: # upper boundary
image = cv2.resize(image, (1024, 512))
middlePoint = image.shape[1] // 2
half_1 = image[:,:middlePoint]
half_2 = image[:,middlePoint:]
images = [half_1,half_2]
for image in images:
image = pixL().toThePixL(image,6,False)
image = np.asarray([image])
image = np.transpose(image, (0, 3, 1, 2))
image = inference(image)
image = cv2.hconcat([images[0], images[1]])
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
return image
elif 0.00 <= aspect_ratio <= 0.75:
image = cv2.resize(image, (512, 1024))
middlePoint = image.shape[0] // 2
half_1 = image[:middlePoint,:]
half_2 = image[middlePoint:,:]
images = [half_1,half_2]
for image in images:
image = pixL().toThePixL(image,6,False)
image = np.asarray([image])
image = np.transpose(image, (0, 3, 1, 2))
image = inference(image)
image = cv2.vconcat([images[0], images[1]])
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
return image
def postprocess(image):
image = util.tensor2im(image)
return image
def inference(image):
global model
data = {"A": None, "A_paths": None}
data['A'] = torch.FloatTensor(image)
model.set_input(data)
model.test()
image = model.get_current_visuals()['fake']
return image
def pixera_CYCLEGAN(image):
global num_inferences
image = preprocess(image)
image = postprocess(image)
num_inferences += 1
print(num_inferences)
return image
title_ = "Pixera: Create your own Pixel Art"
description_ = "."
examples_path = f"{os.getcwd()}/imgs"
examples_ = os.listdir(examples_path)
random.shuffle(examples_)
examples_ = [[f"{examples_path}/{example}"] for example in examples_]
demo = gr.Interface(pixera_CYCLEGAN, inputs = [gr.Image(show_label= False)],
outputs = [gr.Image(show_label= False)],
examples = examples_,
title = title_,
description= description_)
demo.launch() |