import os
import time
import numpy as np
from config import Config
from models import ManualTree, AnonymousColorDetector


# 主函数
def main():
    threshold = Config.threshold
    rgb_threshold = Config.rgb_threshold
    manualTree = ManualTree(blk_model_path=Config.blk_model_path, pixel_model_path=Config.pixel_model_path)
    tobacco_detector = AnonymousColorDetector(file_path=Config.rgb_tobacco_model_path)
    background_detector = AnonymousColorDetector(file_path=Config.rgb_background_model_path)

    total_len = Config.nRows * Config.nCols * Config.nBands * 4  # float型变量, 4个字节
    total_rgb = Config.nrgbRows * Config.nrgbCols * Config.nrgbBands * 1  # int型变量
    if not os.access(img_fifo_path, os.F_OK):
        os.mkfifo(img_fifo_path, 0o777)
    if not os.access(mask_fifo_path, os.F_OK):
        os.mkfifo(mask_fifo_path, 0o777)
    if not os.access(rgb_fifo_path, os.F_OK):
        os.mkfifo(rgb_fifo_path, 0o777)
    while True:
        fd_img = os.open(img_fifo_path, os.O_RDONLY)
        fd_rgb = os.open(rgb_fifo_path, os.O_RDONLY)
        data = os.read(fd_img, total_len)

        # 读取(开启一个管道)
        if len(data) < 3:
            threshold = int(float(data))
            print(threshold)
            continue
        else:
            data_total = data
            rgb_data = os.read(fd_rgb, total_rgb)
            if len(rgb_data) < 3:
                rgb_threshold = int(float(rgb_data))
                print(rgb_threshold)
                continue
            else:
                rgb_data_total = rgb_data

        os.close(fd_img)
        os.close(fd_rgb)

        # 识别
        t1 = time.time()
        img_data = np.frombuffer(data_total, dtype=np.float32).reshape((Config.nRows, Config.nBands, -1)).transpose(0,
                                                                                                                    2,
                                                                                                                    1)
        rgb_data = np.frombuffer(rgb_data_total, dtype=np.uint8)
        pixel_predict_result = manualTree.pixel_predict_ml_dilation(data=img_data, iteration=1)
        blk_predict_result = manualTree.blk_predict(data=img_data)
        rgb_data = tobacco_detector.pretreatment(rgb_data)
        rgb_predict_result = 1 - (
                background_detector.predict(rgb_data) | tobacco_detector.swell(tobacco_detector.predict(rgb_data)))
        mask_rgb = rgb_predict_result.reshape(Config.nRows, Config.nCols // Config.blk_size, Config.blk_size) \
            .sum(axis=2).reshape(Config.nRows // 4, Config.blk_size, Config.nCols // Config.blk_size) \
            .sum(axis=1)
        mask_rgb[mask_rgb <= rgb_threshold] = 0
        mask_rgb[mask_rgb > rgb_threshold] = 1

        mask = (pixel_predict_result & blk_predict_result).astype(np.uint8)
        mask = mask.reshape(Config.nRows, Config.nCols // Config.blk_size, Config.blk_size) \
            .sum(axis=2).reshape(Config.nRows // 4, Config.blk_size, Config.nCols // Config.blk_size) \
            .sum(axis=1)
        # print(threshold)
        mask[mask <= threshold] = 0
        mask[mask > threshold] = 1
        mask_result = (mask | mask_rgb).astype(np.uint8)
        mask_result = mask_result.repeat(Config.blk_size, axis=0).repeat(Config.blk_size, axis=1).astype(np.uint8)
        t2 = time.time()
        # print(f'shibie time is:{t2 - t1}')
        print(f'rgb len = {len(rgb_data)}')

        # 写出
        fd_mask = os.open(mask_fifo_path, os.O_WRONLY)
        os.write(fd_mask, mask_result.tobytes())
        os.close(fd_mask)
        t3 = time.time()
        print(f'total time is:{t3 - t1}')


if __name__ == '__main__':
    # 相关参数
    img_fifo_path = "/tmp/dkimg.fifo"
    mask_fifo_path = "/tmp/dkmask.fifo"
    rgb_fifo_path = "/tmp/dkrgb.fifo"

    # 主函数
    main()