Update app.py

app.py

@@ -1,5 +1,41 @@
-from transformers import pipeline
+from transformers import AutoModelForImageSegmentation
+model = AutoModelForImageSegmentation.from_pretrained("briaai/RMBG-1.4",trust_remote_code=True)
+def preprocess_image(im: np.ndarray, model_input_size: list) -> torch.Tensor:
+    if len(im.shape) < 3:
+        im = im[:, :, np.newaxis]
+    # orig_im_size=im.shape[0:2]
+    im_tensor = torch.tensor(im, dtype=torch.float32).permute(2,0,1)
+    im_tensor = F.interpolate(torch.unsqueeze(im_tensor,0), size=model_input_size, mode='bilinear')
+    image = torch.divide(im_tensor,255.0)
+    image = normalize(image,[0.5,0.5,0.5],[1.0,1.0,1.0])
+    return image
+
+def postprocess_image(result: torch.Tensor, im_size: list)-> np.ndarray:
+    result = torch.squeeze(F.interpolate(result, size=im_size, mode='bilinear') ,0)
+    ma = torch.max(result)
+    mi = torch.min(result)
+    result = (result-mi)/(ma-mi)
+    im_array = (result*255).permute(1,2,0).cpu().data.numpy().astype(np.uint8)
+    im_array = np.squeeze(im_array)
+    return im_array
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# prepare input
 image_path = "https://farm5.staticflickr.com/4007/4322154488_997e69e4cf_z.jpg"
-pipe = pipeline("image-segmentation", model="briaai/RMBG-1.4", trust_remote_code=True)
-pillow_mask = pipe(img_path, return_mask = True) # outputs a pillow mask
-pillow_image = pipe(image_path) # applies mask on input and returns a pillow image
+orig_im = io.imread(im_path)
+orig_im_size = orig_im.shape[0:2]
+image = preprocess_image(orig_im, model_input_size).to(device)
+
+# inference 
+result=model(image)
+
+# post process
+result_image = postprocess_image(result[0][0], orig_im_size)
+
+# save result
+pil_im = Image.fromarray(result_image)
+no_bg_image = Image.new("RGBA", pil_im.size, (0,0,0,0))
+orig_image = Image.open(im_path)
+no_bg_image.paste(orig_image, mask=pil_im)