import time

import cv2
import numpy as np
from PIL import Image

from deeplab import Deeplab

if __name__ == "__main__":
    #-------------------------------------------------------------------------#
    #   如果想要修改对应种类的颜色，到generate函数里修改self.colors即可
    #-------------------------------------------------------------------------#
    deeplab = Deeplab()
    #-------------------------------------------------------------------------#
    #   mode用于指定测试的模式：
    #   'predict'表示单张图片预测
    #   'video'表示视频检测
    #   'fps'表示测试fps
    #-------------------------------------------------------------------------#
    mode = "predict"
    #-------------------------------------------------------------------------#
    #   video_path用于指定视频的路径，当video_path=0时表示检测摄像头
    #   video_save_path表示视频保存的路径，当video_save_path=""时表示不保存 
    #   video_fps用于保存的视频的fps
    #   video_path、video_save_path和video_fps仅在mode='video'时有效
    #   保存视频时需要ctrl+c退出才会完成完整的保存步骤，不可直接结束程序。
    #-------------------------------------------------------------------------#
    video_path      = 0
    video_save_path = ""
    video_fps       = 25.0

    if mode == "predict":
        '''
        predict.py有几个注意点
        1、该代码无法直接进行批量预测，如果想要批量预测，可以利用os.listdir()遍历文件夹，利用Image.open打开图片文件进行预测。
        具体流程可以参考get_miou_prediction.py，在get_miou_prediction.py即实现了遍历。
        2、如果想要保存，利用r_image.save("img.jpg")即可保存。
        3、如果想要原图和分割图不混合，可以把blend参数设置成False。
        4、如果想根据mask获取对应的区域，可以参考detect_image函数中，利用预测结果绘图的部分，判断每一个像素点的种类，然后根据种类获取对应的部分。
        seg_img = np.zeros((np.shape(pr)[0],np.shape(pr)[1],3))
        for c in range(self.num_classes):
            seg_img[:, :, 0] += ((pr == c)*( self.colors[c][0] )).astype('uint8')
            seg_img[:, :, 1] += ((pr == c)*( self.colors[c][1] )).astype('uint8')
            seg_img[:, :, 2] += ((pr == c)*( self.colors[c][2] )).astype('uint8')
        '''
        while True:
            img = input('Input image filename:')
            try:
                image = Image.open(img)
            except:
                print('Open Error! Try again!')
                continue
            else:
                r_image = deeplab.detect_image(image)
                r_image.show()

    elif mode == "video":
        capture=cv2.VideoCapture(video_path)
        if video_save_path!="":
            fourcc = cv2.VideoWriter_fourcc(*'XVID')
            size = (int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)), int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
            out = cv2.VideoWriter(video_save_path, fourcc, video_fps, size)

        fps = 0.0
        while(True):
            t1 = time.time()
            # 读取某一帧
            ref,frame=capture.read()
            # 格式转变，BGRtoRGB
            frame = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)
            # 转变成Image
            frame = Image.fromarray(np.uint8(frame))
            # 进行检测
            frame = np.array(deeplab.detect_image(frame))
            # RGBtoBGR满足opencv显示格式
            frame = cv2.cvtColor(frame,cv2.COLOR_RGB2BGR)
            
            fps  = ( fps + (1./(time.time()-t1)) ) / 2
            print("fps= %.2f"%(fps))
            frame = cv2.putText(frame, "fps= %.2f"%(fps), (0, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
            
            cv2.imshow("video",frame)
            c= cv2.waitKey(1) & 0xff 
            if video_save_path!="":
                out.write(frame)

            if c==27:
                capture.release()
                break
        capture.release()
        out.release()
        cv2.destroyAllWindows()

    elif mode == "fps":
        test_interval = 100
        img = Image.open('img/street.jpg')
        tact_time = deeplab.get_FPS(img, test_interval)
        print(str(tact_time) + ' seconds, ' + str(1/tact_time) + 'FPS, @batch_size 1')
    else:
        raise AssertionError("Please specify the correct mode: 'predict', 'video' or 'fps'.")