mirror of
https://github.com/NanjingForestryUniversity/supermachine-tobacco.git
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104 lines
4.5 KiB
Python
Executable File
104 lines
4.5 KiB
Python
Executable File
import os
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import time
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from queue import Queue
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import numpy as np
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from matplotlib import pyplot as plt
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import models
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import transmit
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from config import Config
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from models import RgbDetector, SpecDetector
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import cv2
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def main():
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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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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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# 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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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 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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# 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, ]
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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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# 相关参数
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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_path = "/tmp/dkmask.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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# selected_bands=[380, 300, 200])
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