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Add PaddleSeg doc and infer.cc demo (#114)
* Update README.md * Update README.md * Update README.md * Create README.md * Update README.md * Update README.md * Update README.md * Update README.md * Add evaluation calculate time and fix some bugs * Update classification __init__ * Move to ppseg * Add segmentation doc * Add PaddleClas infer.py * Update PaddleClas infer.py * Delete .infer.py.swp * Add PaddleClas infer.cc * Update README.md * Update README.md * Update README.md * Update infer.py * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Add PaddleSeg doc and infer.cc demo * Update README.md * Update README.md * Update README.md Co-authored-by: Jason <jiangjiajun@baidu.com>
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@@ -8,11 +8,9 @@ def parse_arguments():
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import ast
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--model", required=True, help="Path of PaddleClas model.")
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"--model", required=True, help="Path of PaddleSeg model.")
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parser.add_argument(
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"--image", type=str, required=True, help="Path of test image file.")
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parser.add_argument(
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"--topk", type=int, default=1, help="Return topk results.")
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parser.add_argument(
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"--device",
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type=str,
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@@ -43,14 +41,17 @@ args = parse_arguments()
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# 配置runtime,加载模型
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runtime_option = build_option(args)
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model_file = os.path.join(args.model, "inference.pdmodel")
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params_file = os.path.join(args.model, "inference.pdiparams")
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config_file = os.path.join(args.model, "inference_cls.yaml")
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#model = fd.vision.classification.PaddleClasModel(model_file, params_file, config_file, runtime_option=runtime_option)
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model = fd.vision.classification.ResNet50vd(
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model_file = os.path.join(args.model, "model.pdmodel")
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params_file = os.path.join(args.model, "model.pdiparams")
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config_file = os.path.join(args.model, "deploy.yaml")
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model = fd.vision.segmentation.PaddleSegModel(
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model_file, params_file, config_file, runtime_option=runtime_option)
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# 预测图片分类结果
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im = cv2.imread(args.image)
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result = model.predict(im, args.topk)
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result = model.predict(im)
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print(result)
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# 可视化结果
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vis_im = fd.vision.visualize.vis_segmentation(im, result)
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cv2.imwrite("vis_img.png", vis_im)
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