import os
import time

import matplotlib.pyplot as plt
import numpy as np
import scipy.io

from config import Config
from models import RgbDetector, SpecDetector, ManualTree, AnonymousColorDetector
import cv2


def main():
    spec_detector = SpecDetector(blk_model_path=Config.blk_model_path, pixel_model_path=Config.pixel_model_path)
    rgb_detector = RgbDetector(tobacco_model_path=Config.rgb_tobacco_model_path,
                               background_model_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)

        # spec data read
        data = os.read(fd_img, total_len)
        if len(data) < 3:
            threshold = int(float(data))
            Config.spec_size_threshold = threshold
            print("[INFO] Get spec threshold: ", threshold)
        else:
            data_total = data
        os.close(fd_img)
        
        # rgb data read
        rgb_data = os.read(fd_rgb, total_rgb)
        if len(rgb_data) < 3:
            rgb_threshold = int(float(rgb_data))
            Config.rgb_size_threshold = rgb_threshold
            print("[INFO] Get rgb threshold", rgb_threshold)
            continue
        else:
            rgb_data_total = rgb_data
        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).reshape((Config.nRgbRows, Config.nRgbCols, -1))
        # 光谱识别
        mask = spec_detector.predict(img_data)
        # rgb识别
        mask_rgb = rgb_detector.predict(rgb_data)
        
        # 结果合并
        mask_result = (mask | mask_rgb).astype(np.uint8)
        
        # mask_result = 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()

        # 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}\n')


def read_c_captures(buffer_path):
    buffer_names = [buffer_name for buffer_name in os.listdir(buffer_path) if buffer_name.endswith('.raw')]
    for buffer_name in buffer_names:
        with open(os.path.join(buffer_path, buffer_name), 'rb') as f:
            data = f.read()
        img = np.frombuffer(data, dtype=np.float32).reshape((Config.nRows, Config.nBands, -1)) \
            .transpose(0, 2, 1)
        mask_name = buffer_name.replace('buf', 'mask').replace('.raw', '')
        with open(os.path.join(buffer_path, mask_name), 'rb') as f:
            data = f.read()
        mask = np.frombuffer(data, dtype=np.uint8).reshape((256, 1024, -1))
        # mask = cv2.resize(mask, (1024, 256))
        fig, axs = plt.subplots(2, 1)
        axs[0].imshow(img[..., [21, 3, 0]])
        axs[0].set_title(buffer_name)
        axs[1].imshow(mask)
        axs[1].set_title(mask_name)
        plt.show()


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()
    # read_c_captures('/home/lzy/2022.7.20/tobacco_v1_0/')