fix api

app.py

@@ -1,3 +1,81 @@
-from remove_background import app
+import PIL.Image
+import gradio as gr
+import huggingface_hub
+import onnxruntime as rt
+import numpy as np
+import cv2
 
-app.launch(share=False, debug=True, enable_queue=True, show_error=True)
+providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
+model_path = huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.onnx")
+rmbg_model = rt.InferenceSession(model_path, providers=providers)
+
+
+def custom_background(background, foreground):
+    x = (background.size[0] - foreground.size[0]) / 2
+    y = (background.size[1] - foreground.size[1]) / 2
+    box = (x, y, foreground.size[0] + x, foreground.size[1] + y)
+    crop = background.crop(box)
+    final_image = crop.copy()
+    # put the foreground in the centre of the background
+    paste_box = (0, final_image.size[1] - foreground.size[1], final_image.size[0], final_image.size[1])
+    final_image.paste(foreground, paste_box, mask=foreground)
+    return final_image
+
+
+def get_mask(img, s=1024):
+    img = (img / 255).astype(np.float32)
+    h, w = h0, w0 = img.shape[:-1]
+    h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
+    ph, pw = s - h, s - w
+    img_input = np.zeros([s, s, 3], dtype=np.float32)
+    img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(img, (w, h))
+    img_input = np.transpose(img_input, (2, 0, 1))
+    img_input = img_input[np.newaxis, :]
+    mask = rmbg_model.run(None, {'img': img_input})[0][0]
+    mask = np.transpose(mask, (1, 2, 0))
+    mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w]
+    mask = cv2.resize(mask, (w0, h0))[:, :, np.newaxis]
+    return mask
+
+
+def predict(img, new_background):
+    mask = get_mask(img)
+    img = (mask * img + 255 * (1 - mask)).astype(np.uint8)
+    mask = (mask * 255).astype(np.uint8)
+    img = np.concatenate([img, mask], axis=2, dtype=np.uint8)
+    mask = mask.repeat(3, axis=2)
+    if new_background is not None:
+        foreground = PIL.Image.fromarray(img)
+        return mask, custom_background(new_background, foreground)
+    return mask, img
+
+
+footer = r"""
+<center>
+<b>
+Demo based on <a href='https://github.com/SkyTNT/anime-segmentation'>SkyTNT Anime Segmentation</a>
+</b>
+</center>
+"""
+
+with gr.Blocks(title="Face Shine") as app:
+    gr.HTML("<center><h1>Anime Remove Background</h1></center>")
+    with gr.Row():
+        with gr.Column():
+            input_img = gr.Image(type="numpy", label="Input image")
+            new_img = gr.Image(type="pil", label="Custom background")
+            run_btn = gr.Button(variant="primary")
+        with gr.Column():
+            with gr.Accordion(label="Image mask", open=False):
+                output_mask = gr.Image(label="mask")
+            output_img = gr.Image(type="pil", label="result")
+
+    run_btn.click(predict, [input_img, new_img], [output_mask, output_img])
+
+    with gr.Row():
+        examples_data = [[f"examples/{x:02d}.jpg"] for x in range(1, 4)]
+        examples = gr.Dataset(components=[input_img], samples=examples_data)
+        examples.click(lambda x: x[0], [examples], [input_img])
+
+    with gr.Row():
+        gr.HTML(footer)