fix predict

remove_background/__init__.py

@@ -1 +0,0 @@
-from .interface import app

remove_background/__main__.py

@@ -1,9 +0,0 @@
-from remove_background import app
-
-
-def main():
-    app.queue().launch(share=True, debug=True)
-
-
-if __name__ == "__main__":
-    main()

remove_background/interface.py

@@ -1,81 +0,0 @@
-import PIL.Image
-import gradio as gr
-import huggingface_hub
-import onnxruntime as rt
-import numpy as np
-import cv2
-
-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)