def from_video_get_img(video_path):
    print(video_path)
    print(video_path + ' Is Loading...')
    cap = cv2.VideoCapture(video_path)
    if cap.isOpened():
        # best>MOG
        fgbg = cv2.bgsegm.createBackgroundSubtractorMOG()
        origrial_path_list = []
        id = 0
        flag = True
        while flag:
            ret, frame = cap.read()
            if ret:
                fgmask = fgbg.apply(frame)
                save_file_name = video_path.split('/')[-1].split('.')[0]
                save_origrial_path = 'Package/Origrial/' + save_file_name + '/'
                save_normalPic_path = 'Package/Normal/' + save_file_name + '/'
                if not os.path.exists(save_origrial_path):
                    os.makedirs(save_origrial_path)
                if not os.path.exists(save_normalPic_path):
                    os.makedirs(save_normalPic_path)
                a, b = fgmask.shape
                origrial_path_list.append(fgmask)
                #cv2.imwrite(save_origrial_path+format_id(id)+'.png', fgmask)
                #cv2.imwrite(save_normalPic_path+format_id(id)+'.png', frame)
                id += 1
                cv2.imshow('frame', fgmask)
                cv2.waitKey(1)
            else:
                break
    cap.release()
    cv2.destroyAllWindows()

def get_cImg(path, cut_path, size):
    '''
    剪切图片
    :param path: 输入图片路径
    :param cut_path: 剪切图片后的输出路径
    :param size: 要剪切的图片大小
    :return:
    '''
    dirs_list = os.listdir(path)
    for dir in dirs_list:
        files_list = os.listdir(os.path.join(path, dir))
        firstId, lastId = 10000000000000000, 0
        for pic in files_list:
            firstId = min(int(pic.split('.')[0]), firstId)
            lastId = max(int(pic.split('.')[0]), lastId)
        midId = (firstId+lastId)>>1
        for id in range(midId-30, midId+30):
            img_name = str(id) + '.png'
            img = Image.open(os.path.join(path, dir, img_name))
            image, flag = cut(img)
            goal_dir = os.path.join(cut_path, dir)
            create_file(goal_dir)
            if not flag:
                Image.fromarray(image).convert('L').resize((size, size)).save(os.path.join(goal_dir, img_name))
        print(dir + ' is Cuted!')
def cut(image):
    '''
    通过找到人的最小最大高度与宽度把人的轮廓分割出来，、
    因为原始轮廓图为二值图，因此头顶为将二值图像列相加后，形成一列后第一个像素值不为0的索引。
    同理脚底为形成一列后最后一个像素值不为0的索引。
    人的宽度也同理。
    :param image: 需要裁剪的图片 N*M的矩阵
    :return: temp: 裁剪后的图片 size*size的矩阵。flag：是否是符合要求的图片
    '''
    image = np.array(image)
    # 找到人的最小最大高度与宽度
    height_min = (image.sum(axis=1) != 0).argmax()
    height_max = ((image.sum(axis=1) != 0).cumsum()).argmax()
    width_min = (image.sum(axis=0) != 0).argmax()
    width_max = ((image.sum(axis=0) != 0).cumsum()).argmax()
    head_top = image[height_min, :].argmax()
    # 设置切割后图片的大小，为size*size，因为人的高一般都会大于宽
    size = height_max - height_min
    temp = np.zeros((size, size))
    # 将width_max-width_min（宽）乘height_max-height_min（高，szie）的人的轮廓图，放在size*size的图片中央
    # l = (width_max-width_min)//2
    # r = width_max-width_min-l
    # 以头为中心，将width_max-width_min（宽）乘height_max-height_min（高，size）的人的轮廓图，放在size*size的图片中央
    l1 = head_top-width_min
    r1 = width_max-head_top
    # 若宽大于高，或头的左侧或右侧身子比要生成图片的一般要大。则此图片为不符合要求的图片
    flag = False
    if size <= width_max-width_min or size//2 < r1 or size//2 < l1:
        flag = True
        return temp, flag
    # centroid = np.array([(width_max+width_min)/2,(height_max+height_min)/2],dtype='int')
    temp[:, (size//2-l1):(size//2+r1)] = image[height_min:height_max, width_min:width_max]
    return temp, flag
def get_humanHead(root_path):
    save_img_path = 'DetectDataSetsget\Head'
    create_file(save_img_path)
    for dir_name in os.listdir(root_path):
        src_dir = os.path.join(root_path, dir_name)
        #print(src_dir)
        dst_dir = src_dir.replace(root_path, save_img_path)
        #print(dst_dir)
        create_file(dst_dir)
        for img_name in os.listdir(src_dir):
            img = cv.imread(os.path.join(src_dir, img_name))
            x, y, w, h = 50, 0, 120, 40
            croped_img = img[y: y+h, x: x+w]
            cv.imwrite(os.path.join(dst_dir, img_name), croped_img)
            cv.waitKey(0)