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Runtime error
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74e9d98
1
Parent(s):
eecbd6f
Update app.py
Browse files
app.py
CHANGED
@@ -73,63 +73,6 @@ def reload_segmodel(model, path=""):
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return model
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# def rec(opt):
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# # print(torch.__version__) # 1.5.1
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# img_list = os.listdir(opt.distorrted_path) # distorted images list
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# if not os.path.exists(opt.gsave_path): # create save path
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# os.mkdir(opt.gsave_path)
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# if not os.path.exists(opt.isave_path): # create save path
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# os.mkdir(opt.isave_path)
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# GeoTr_Seg_model = GeoTr_Seg()#.cuda()
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# # reload segmentation model
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# reload_segmodel(GeoTr_Seg_model.msk, opt.Seg_path)
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# # reload geometric unwarping model
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# reload_model(GeoTr_Seg_model.GeoTr, opt.GeoTr_path)
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# IllTr_model = IllTr()#.cuda()
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# # reload illumination rectification model
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# reload_model(IllTr_model, opt.IllTr_path)
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# # To eval mode
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# GeoTr_Seg_model.eval()
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# IllTr_model.eval()
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# for img_path in img_list:
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# name = img_path.split('.')[-2] # image name
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# img_path = opt.distorrted_path + img_path # read image and to tensor
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# im_ori = np.array(Image.open(img_path))[:, :, :3] / 255.
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# h, w, _ = im_ori.shape
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# im = cv2.resize(im_ori, (288, 288))
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# im = im.transpose(2, 0, 1)
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# im = torch.from_numpy(im).float().unsqueeze(0)
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# with torch.no_grad():
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# # geometric unwarping
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# bm = GeoTr_Seg_model(im)
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# bm = bm.cpu()
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# bm0 = cv2.resize(bm[0, 0].numpy(), (w, h)) # x flow
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# bm1 = cv2.resize(bm[0, 1].numpy(), (w, h)) # y flow
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# bm0 = cv2.blur(bm0, (3, 3))
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# bm1 = cv2.blur(bm1, (3, 3))
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# lbl = torch.from_numpy(np.stack([bm0, bm1], axis=2)).unsqueeze(0) # h * w * 2
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# out = F.grid_sample(torch.from_numpy(im_ori).permute(2,0,1).unsqueeze(0).float(), lbl, align_corners=True)
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# img_geo = ((out[0]*255).permute(1, 2, 0).numpy())[:,:,::-1].astype(np.uint8)
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# cv2.imwrite(opt.gsave_path + name + '_geo' + '.png', img_geo) # save
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# # illumination rectification
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# if opt.ill_rec:
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# ill_savep = opt.isave_path + name + '_ill' + '.png'
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# rec_ill(IllTr_model, img_geo, saveRecPath=ill_savep)
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# print('Done: ', img_path)
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def process_image(input_image):
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@@ -169,6 +112,14 @@ def process_image(input_image):
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else:
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return Image.fromarray(img_geo)
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# Define Gradio interface
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input_image = gr.inputs.Image()
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output_image = gr.outputs.Image(type='pil')
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return model
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def process_image(input_image):
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else:
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return Image.fromarray(img_geo)
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desired_width = 200
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scale_ratio = desired_width / output_image.width
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desired_height = int(output_image.height * scale_ratio)
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output_image_resized = output_image.resize((desired_width, desired_height), Image.ANTIALIAS)
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return output_image_resized
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# Define Gradio interface
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input_image = gr.inputs.Image()
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output_image = gr.outputs.Image(type='pil')
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