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https://github.com/NanjingForestryUniversity/supermachine-tobacco.git
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99 lines
4.1 KiB
Python
Executable File
99 lines
4.1 KiB
Python
Executable File
import os
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import cv2
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import time
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import numpy as np
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import utils
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from config import Config
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from models import RgbDetector, SpecDetector
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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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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 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(rgb_fifo_path, os.F_OK):
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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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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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threshold = int(float(data))
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Config.spec_size_threshold = threshold
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print("[INFO] Get spec threshold: ", threshold)
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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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rgb_threshold = int(float(rgb_data))
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Config.rgb_size_threshold = rgb_threshold
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print("[INFO] Get rgb threshold", rgb_threshold)
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continue
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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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.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 only_spec:
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# 光谱识别
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mask_spec = spec_detector.predict(img_data)
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mask_rgb = rgb_detector.predict(rgb_data)
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mask_rgb = np.zeros_like(mask_spec, dtype=np.uint8)
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elif only_color:
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# rgb识别
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mask_spec = spec_detector.predict(img_data)
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mask_rgb = rgb_detector.predict(rgb_data)
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mask_spec = np.zeros_like(mask_rgb, dtype=np.uint8)
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else:
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mask_spec = spec_detector.predict(img_data)
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mask_rgb = rgb_detector.predict(rgb_data)
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# 进行喷阀的合并
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masks = [utils.valve_expend(mask) for mask in [mask_spec, mask_rgb]]
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# control the size of the output masks, 在resize前，图像的宽度是和喷阀对应的
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masks = [cv2.resize(mask.astype(np.uint8), Config.target_size) for mask in masks]
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# 写出
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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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fd_mask = os.open(fifo, os.O_WRONLY)
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os.write(fd_mask, mask.tobytes())
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os.close(fd_mask)
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t3 = time.time()
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print(f'total time is:{t3 - t1}')
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if __name__ == '__main__':
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import argparse
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parser = argparse.ArgumentParser(description='主程序')
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parser.add_argument('-oc', default=False, action='store_true', help='只进行RGB彩色预测 only rgb', required=False)
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parser.add_argument('-os', default=False, action='store_true', help='只进行光谱预测 only spec', required=False)
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args = parser.parse_args()
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# fifo 参数
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img_fifo_path = "/tmp/dkimg.fifo"
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rgb_fifo_path = "/tmp/dkrgb.fifo"
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# mask 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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main(only_spec=args.os, only_color=args.oc)
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