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https://github.com/NanjingForestryUniversity/supermachine-tobacco.git
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大小滤波修改
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parent
4d6fd3cd3a
commit
34d9e9a676
2
main.py
2
main.py
@ -11,7 +11,7 @@ def main(only_spec=False, only_color=False):
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spec_detector = SpecDetector(blk_model_path=Config.blk_model_path, pixel_model_path=Config.pixel_model_path)
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rgb_detector = RgbDetector(tobacco_model_path=Config.rgb_tobacco_model_path,
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background_model_path=Config.rgb_background_model_path)
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_, _ = spec_detector.predict(np.ones(Config.nRows, Config.nCols, Config.nBands, dtype=float)*0.4),\
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_, _ = spec_detector.predict(np.ones((Config.nRows, Config.nCols, Config.nBands), dtype=float)*0.4),\
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rgb_detector.predict(np.ones((Config.nRgbRows, Config.nRgbCols, Config.nRgbBands), dtype=np.uint8)*40)
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total_len = Config.nRows * Config.nCols * Config.nBands * 4 # float型变量, 4个字节
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total_rgb = Config.nRgbRows * Config.nRgbCols * Config.nRgbBands * 1 # int型变量
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4
utils.py
4
utils.py
@ -132,11 +132,13 @@ def lab_scatter(dataset: dict, class_max_num=None, is_3d=False, is_ps_color_spac
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plt.show()
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def size_threshold(img, blk_size, threshold):
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def size_threshold(img, blk_size, threshold, last_end: np.ndarray=None) -> np.ndarray:
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mask = img.reshape(img.shape[0], img.shape[1] // blk_size, blk_size).sum(axis=2). \
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reshape(img.shape[0] // blk_size, blk_size, img.shape[1] // blk_size).sum(axis=1)
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mask[mask <= threshold] = 0
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mask[mask > threshold] = 1
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if last_end is not None:
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mask_up = np.concatenate((last_end, img[:-(blk_size//2), :]))
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return mask
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