import gradio as gr import os.path import numpy as np from collections import OrderedDict import torch import cv2 from PIL import Image, ImageOps import utils_image as util from network_fbcnn import FBCNN as net import requests def inference(input_img, is_gray, input_quality, enable_zoom, zoom, x_shift, y_shift, state): if is_gray: n_channels = 1 # set 1 for grayscale image, set 3 for color image model_name = 'fbcnn_gray.pth' else: n_channels = 3 # set 1 for grayscale image, set 3 for color image model_name = 'fbcnn_color.pth' nc = [64,128,256,512] nb = 4 input_quality = 100 - input_quality #model_pool = '/FBCNN/model_zoo' # fixed #model_path = os.path.join(model_pool, model_name) model_path = model_name if os.path.exists(model_path): print(f'loading model from {model_path}') else: os.makedirs(os.path.dirname(model_path), exist_ok=True) url = 'https://github.com/jiaxi-jiang/FBCNN/releases/download/v1.0/{}'.format(os.path.basename(model_path)) r = requests.get(url, allow_redirects=True) open(model_path, 'wb').write(r.content) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # ---------------------------------------- # load model # ---------------------------------------- if (not enable_zoom) or (state[1] is None): model = net(in_nc=n_channels, out_nc=n_channels, nc=nc, nb=nb, act_mode='R') model.load_state_dict(torch.load(model_path), strict=True) model.eval() for k, v in model.named_parameters(): v.requires_grad = False model = model.to(device) test_results = OrderedDict() test_results['psnr'] = [] test_results['ssim'] = [] test_results['psnrb'] = [] # ------------------------------------ # (1) img_L # ------------------------------------ if n_channels == 1: open_cv_image = Image.fromarray(input_img) open_cv_image = ImageOps.grayscale(open_cv_image) open_cv_image = np.array(open_cv_image) # PIL to open cv image img = np.expand_dims(open_cv_image, axis=2) # HxWx1 elif n_channels == 3: open_cv_image = np.array(input_img) # PIL to open cv image if open_cv_image.ndim == 2: open_cv_image = cv2.cvtColor(open_cv_image, cv2.COLOR_GRAY2RGB) # GGG else: open_cv_image = cv2.cvtColor(open_cv_image, cv2.COLOR_BGR2RGB) # RGB img_L = util.uint2tensor4(open_cv_image) img_L = img_L.to(device) # ------------------------------------ # (2) img_E # ------------------------------------ img_E,QF = model(img_L) QF = 1- QF img_E = util.tensor2single(img_E) img_E = util.single2uint(img_E) qf_input = torch.tensor([[1-input_quality/100]]).cuda() if device == torch.device('cuda') else torch.tensor([[1-input_quality/100]]) img_E,QF = model(img_L, qf_input) QF = 1- QF img_E = util.tensor2single(img_E) img_E = util.single2uint(img_E) if img_E.ndim == 3: img_E = img_E[:, :, [2, 1, 0]] if (state[1] is not None) and enable_zoom: img_E = state[1] out_img = Image.fromarray(img_E) out_img_w, out_img_h = out_img.size # output image size zoom = zoom/100 x_shift = x_shift/100 y_shift = y_shift/100 zoom_w, zoom_h = out_img_w*zoom, out_img_h*zoom zoom_left, zoom_right = int((out_img_w - zoom_w)*x_shift), int(zoom_w + (out_img_w - zoom_w)*x_shift) zoom_top, zoom_bottom = int((out_img_h - zoom_h)*y_shift), int(zoom_h + (out_img_h - zoom_h)*y_shift) if (state[0] is None) or not enable_zoom: in_img = Image.fromarray(input_img) state[0] = input_img else: in_img = Image.fromarray(state[0]) in_img = in_img.crop((zoom_left, zoom_top, zoom_right, zoom_bottom)) in_img = in_img.resize((int(zoom_w/zoom), int(zoom_h/zoom)), Image.NEAREST) out_img = out_img.crop((zoom_left, zoom_top, zoom_right, zoom_bottom)) out_img = out_img.resize((int(zoom_w/zoom), int(zoom_h/zoom)), Image.NEAREST) return img_E, in_img, out_img, [state[0],img_E] interface = gr.Interface( fn = inference, inputs = [gr.inputs.Image(), gr.inputs.Checkbox(label="Grayscale (Check this if your image is grayscale)"), gr.inputs.Slider(minimum=1, maximum=100, step=1, label="Intensity (Higher = more JPEG artifact removal)"), gr.inputs.Checkbox(default=False, label="Edit Zoom preview \n(This is optional. " "Check this after the image result is loaded to edit zoom parameters\n" "without processing the input image.)"), gr.inputs.Slider(minimum=10, maximum=100, step=1, default=50, label="Zoom Image \n" "(Use this to see the image quality up close. \n" "100 = original size)"), gr.inputs.Slider(minimum=0, maximum=100, step=1, label="Zoom preview horizontal shift \n" "(Increase to shift to the right)"), gr.inputs.Slider(minimum=0, maximum=100, step=1, label="Zoom preview vertical shift \n" "(Increase to shift downwards)"), gr.inputs.State(default=[None,None]) ], outputs = [gr.outputs.Image(label="Result"), gr.outputs.Image(label="Before:"), gr.outputs.Image(label="After:"), "state"], examples = [["doraemon.jpg",False,60,False,42,50,50], ["tomandjerry.jpg",False,60,False,40,57,44], ["somepanda.jpg",True,100,False,30,8,24], ["cemetry.jpg",False,70,False,20,44,77], ["michelangelo_david.jpg",True,30,False,12,53,27], ["elon_musk.jpg",False,45,False,15,33,30]], allow_flagging=False ).launch(enable_queue=True)