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add: rest of the files.
Browse files- README.md +1 -1
- app.py +54 -0
- cartoonizer.onnx +3 -0
- mountain.jpeg +0 -0
- requirements.txt +3 -0
README.md
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---
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title: Cartoonizer Demo Onnx
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colorTo: gray
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sdk: gradio
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---
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title: Cartoonizer Demo Onnx
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emoji: 🗻
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colorFrom: green
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colorTo: gray
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sdk: gradio
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app.py
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import os
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import cv2
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import gradio as gr
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import numpy as np
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import onnxruntime as ort
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from PIL import Image
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_sess_options = ort.SessionOptions()
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_sess_options.intra_op_num_threads = os.cpu_count()
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MODEL_SESS = ort.InferenceSession(
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"cartoonizer.onnx", _sess_options, providers=["CPUExecutionProvider"]
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)
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def preprocess_image(image: Image) -> np.ndarray:
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image = np.array(image)
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image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
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h, w, c = np.shape(image)
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if min(h, w) > 720:
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if h > w:
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h, w = int(720 * h / w), 720
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else:
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h, w = 720, int(720 * w / h)
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image = cv2.resize(image, (w, h), interpolation=cv2.INTER_AREA)
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h, w = (h // 8) * 8, (w // 8) * 8
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image = image[:h, :w, :]
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image = image.astype(np.float32) / 127.5 - 1
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return np.expand_dims(image, axis=0)
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def inference(image: np.ndarray) -> Image:
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image = preprocess_image(image)
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results = MODEL_SESS.run(None, {"input_photo:0": image})
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output = (np.squeeze(results[0]) + 1.0) * 127.5
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output = np.clip(output, 0, 255).astype(np.uint8)
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output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
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return Image.fromarray(output)
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title = "Generate cartoonized images"
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article = "Demo of CartoonGAN model (https://systemerrorwang.github.io/White-box-Cartoonization/). \nDemo image is from https://unsplash.com/photos/f0SgAs27BYI."
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iface = gr.Interface(
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inference,
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inputs=gr.inputs.Image(type="pil", label="Input Image"),
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outputs="image",
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title=title,
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article=article,
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allow_flagging="never",
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examples=[["mountain.jpeg"]],
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)
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iface.launch()
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cartoonizer.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:c035adb7ce7590680fca3d82941ed5d1f6bcb25a6f61ac3fea06dcfb656c325a
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size 5886605
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mountain.jpeg
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requirements.txt
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onnxruntime
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numpy
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opencv-python
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