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图块拼接BetaTest
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README.md
18
README.md
@ -125,3 +125,21 @@
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| 识别结果 |  |
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| 模型约束情况 |  |
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## 图像的对齐
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引入了RGB和光谱图像的原因,这里牵扯到图像对齐的问题。
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这里可以看到对齐的结果:
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根据这张图片的换算结果可以得知光谱图像比RGB图像超前了69个像素,大概2.02厘米的样子。
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根据这张图片,可以得知,图像上下偏差是2.3厘米
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这张图片里的上下偏差则达到了2.6厘米左右。
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@ -30,5 +30,9 @@ class Config:
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threshold_s = 175
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rgb_size_threshold = 4
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# Mask parameter
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target_size = (1024, 1024) # (Width, Height) of mask
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# mask parameter
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target_size = (256, 1024) # (Width, Height) of mask
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# save part
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offset_vertical = -70
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32
main.py
32
main.py
@ -25,12 +25,17 @@ def main():
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os.mkfifo(rgb_fifo_path, 0o777)
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if not os.access(mask_fifo_path, os.F_OK):
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os.mkfifo(mask_fifo_path, 0o777)
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if not os.access(rgb_mask_fifo_path, os.F_OK):
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os.mkfifo(rgb_mask_fifo_path, 0o777)
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# 进行补偿buffer的开启
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if Config.offset_vertical < 0:
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# 纵向的补偿小于0,那就意味着光谱图要上移才能补上,那么我们应该补偿SPEC相机的全 0 图像
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conserve_part = np.zeros((abs(Config.offset_vertical) // 4, Config.nRows, Config.nBands))
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elif Config.offset_vertical > 0:
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# 纵向的补偿小于0,说明光谱图下移才能补上去,那么我们就需要补偿RGB相机的全 0 图像
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conserve_part = np.zeros(abs(Config.offset_vertical), Config.nRgbRows, Config.nRgbBands)
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while True:
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fd_img = os.open(img_fifo_path, os.O_RDONLY)
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fd_rgb = os.open(rgb_fifo_path, os.O_RDONLY)
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# spec data read
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data = os.read(fd_img, total_len)
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if len(data) < 3:
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@ -40,7 +45,6 @@ def main():
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else:
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data_total = data
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os.close(fd_img)
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# rgb data read
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rgb_data = os.read(fd_rgb, total_rgb)
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if len(rgb_data) < 3:
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@ -51,23 +55,34 @@ def main():
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else:
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rgb_data_total = rgb_data
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os.close(fd_rgb)
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# 识别
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t1 = time.time()
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img_data = np.frombuffer(data_total, dtype=np.float32).reshape((Config.nRows, Config.nBands, -1)) \
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img_data = np.frombuffer(data_total, dtype=np.float32).reshape((Config.nRows, Config.nBands, -1))\
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.transpose(0, 2, 1)
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rgb_data = np.frombuffer(rgb_data_total, dtype=np.uint8).reshape((Config.nRgbRows, Config.nRgbCols, -1))
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if Config.offset_vertical < 0:
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# 纵向的补偿小于0,那就意味着光谱图要上移才能补上,那么我们应该补偿SPEC相机的全 0 图像
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new_conserve_part, real_part = img_data[:abs(Config.offset_vertical) // 4, ...],\
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img_data[abs(Config.offset_vertical) // 4:, ...]
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img_data = np.concatenate([real_part, conserve_part], axis=0)
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conserve_part = new_conserve_part
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elif Config.offset_vertical > 0:
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# 纵向的补偿小于0,说明光谱图下移才能补上去,那么我们就需要补偿RGB相机的全 0 图像
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new_conserve_part, real_part = rgb_data[:abs(Config.offset_vertical), ...],\
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rgb_data[abs(Config.offset_vertical):, ...]
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rgb_data = np.concatenate([real_part, conserve_part], axis=0)
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conserve_part = new_conserve_part
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# 光谱识别
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mask_spec = spec_detector.predict(img_data)
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# rgb识别
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mask_rgb = rgb_detector.predict(rgb_data)
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# 结果合并
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# mask_result = (mask_spec | mask_rgb).astype(np.uint8)
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mask_result = (mask_spec | mask_rgb).astype(np.uint8)
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# control the size of the output masks
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masks = [cv2.resize(mask.astype(np.uint8), Config.target_size) for mask in [mask_spec, mask_rgb]]
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# 写出
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output_fifos = [mask_fifo_path, rgb_mask_fifo_path]
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output_fifos = [mask_fifo_path, ]
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for fifo, mask in zip(output_fifos, masks):
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fd_mask = os.open(fifo, os.O_WRONLY)
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os.write(fd_mask, mask.tobytes())
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@ -82,7 +97,6 @@ if __name__ == '__main__':
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rgb_fifo_path = "/tmp/dkrgb.fifo"
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mask_fifo_path = "/tmp/dkmask.fifo"
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rgb_mask_fifo_path = "/tmp/dkmask_rgb.fifo"
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# 主函数
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main()
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# read_c_captures('/home/lzy/2022.7.15/tobacco_v1_0/', no_mask=True, nrows=256, ncols=1024,
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