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https://github.com/NanjingForestryUniversity/supermachine-tobacco.git
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Merge remote-tracking branch 'originn/master'
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FEIJINTI
commit
75ee16a9ad
@ -38,7 +38,7 @@ class Config:
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valve_merge_size = 2 # 每两个喷阀当中有任意一个出现杂质则认为都是杂质
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valve_horizontal_padding = 3 # 喷阀横向膨胀的尺寸，应该是奇数,3时表示左右各膨胀1
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max_open_valve_limit = 25 # 最大同时开启喷阀限制,按照电流计算，当前的喷阀可以开启的喷阀 600W的电源 / 12V电源 = 50A, 一个阀门1A
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max_time_spent = 200
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# save part
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offset_vertical = 0
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45
main.py
45
main.py
@ -15,57 +15,65 @@ def main(only_spec=False, only_color=False, if_merge=False, interval_time=None,
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single_spec=False, single_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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background_model_path=Config.rgb_background_model_path,
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ai_path=Config.ai_path)
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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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if not single_color:
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logging.info("create color fifo")
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if not os.access(img_fifo_path, os.F_OK):
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os.mkfifo(img_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 single_spec:
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logging.info("create rgb fifo")
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if not os.access(rgb_fifo_path, os.F_OK):
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os.mkfifo(rgb_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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logging.info(f"请注意!正在以调试模式运行程序，输出的信息可能较多。")
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# specially designed for Miaow.
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if (interval_time is not None) and (delay_repeat_time is not None):
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interval_time = float(interval_time) / 1000.0
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delay_repeat_time = int(delay_repeat_time)
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logging.warning(f'Delay {interval_time*1000:.2f}ms will be added per {delay_repeat_time} frames')
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delay_repeat_time_count = 0
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while True:
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if not single_color:
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img_data, rgb_data = None, None
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if single_spec:
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fd_img = os.open(img_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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data_total = os.read(fd_img, total_len)
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if len(data_total) < 3:
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try:
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threshold = int(float(data))
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threshold = int(float(data_total))
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Config.spec_size_threshold = threshold
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logging.info(f'[INFO] Get spec threshold: {threshold}')
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except Exception as e:
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logging.error(
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f'毁灭性错误:收到长度小于3却无法转化为整数spec_size_threshold的网络报文，报文内容为 {data},'
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f'毁灭性错误:收到长度小于3却无法转化为整数spec_size_threshold的网络报文，报文内容为 {data_total},'
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f' 错误为 {e}.')
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if single_spec:
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continue
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else:
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data_total = data
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data_total = data_total
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os.close(fd_img)
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try:
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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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print(f"get image_shape {img_data.shape}")
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except Exception as e:
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logging.error(f'毁灭性错误!收到的光谱数据长度为{len(data_total)}无法转化成指定的形状 {e}')
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if not single_spec:
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if single_color:
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fd_rgb = os.open(rgb_fifo_path, os.O_RDONLY)
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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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rgb_data_total = os.read(fd_rgb, total_rgb)
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if len(rgb_data_total) < 3:
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try:
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rgb_threshold = int(float(rgb_data))
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rgb_threshold = int(float(rgb_data_total))
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Config.rgb_size_threshold = rgb_threshold
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logging.info(f'Get rgb threshold: {rgb_threshold}')
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except Exception as e:
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@ -73,23 +81,29 @@ def main(only_spec=False, only_color=False, if_merge=False, interval_time=None,
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f' 错误为 {e}.')
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continue
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else:
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rgb_data_total = rgb_data
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rgb_data_total = rgb_data_total
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os.close(fd_rgb)
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try:
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rgb_data = np.frombuffer(rgb_data_total, dtype=np.uint8).reshape((Config.nRgbRows, Config.nRgbCols, -1))
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print(f"get rgb_data shape {rgb_data.shape}")
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except Exception as e:
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logging.error(f'毁灭性错误!收到的rgb数据长度为{len(rgb_data)}无法转化成指定形状 {e}')
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logging.error(f'毁灭性错误!收到的rgb数据长度为{len(rgb_data_total)}无法转化成指定形状 {e}')
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# 识别 read
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since = time.time()
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# predict
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if single_spec or single_color:
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print('start predict')
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if single_spec:
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print('spec predict', img_data.shape)
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mask_spec = spec_detector.predict(img_data).astype(np.uint8)
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masks = [mask_spec, ]
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print('spectral mask shape:', masks[0].shape)
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else:
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print('rgb predict', rgb_data.shape)
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mask_rgb = rgb_detector.predict(rgb_data).astype(np.uint8)
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masks = [mask_rgb, ]
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print("rgb mask shape: ", masks[0].shape)
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else:
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if only_spec:
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# 光谱识别
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@ -126,8 +140,10 @@ def main(only_spec=False, only_color=False, if_merge=False, interval_time=None,
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else:
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output_fifos = [mask_fifo_path, rgb_mask_fifo_path]
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for fifo, mask in zip(output_fifos, masks):
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print("open fifo")
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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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print("close fifo")
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os.close(fd_mask)
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time_spent = (time.time() - since) * 1000
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predict_by = 'spec' if single_spec else 'rgb' if single_color else 'spec+rgb'
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@ -161,4 +177,5 @@ if __name__ == '__main__':
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console_handler.setLevel(logging.DEBUG if args.d else logging.WARNING)
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logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s',
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handlers=[file_handler, console_handler], level=logging.DEBUG)
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main(only_spec=args.os, only_color=args.oc, if_merge=args.m, interval_time=args.dt, delay_repeat_time=args.df)
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main(only_spec=args.os, only_color=args.oc, if_merge=args.m, interval_time=args.dt, delay_repeat_time=args.df,
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single_spec=args.ss, single_color=args.sc)
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