import requests
from base64 import b64encode
from json import dumps
import os
import cv2
import numpy as np
import pdb
max_nu_area= 0

def get_object_mask(frame, ori_mask, mask_path, all_nu, result_path):
    global max_nu_area
    maskimg = cv2.imread(mask_path, 0)
    kernel = np.ones((5, 5), np.uint8)
    dst = ori_mask
    dst = cv2.erode(dst, kernel)
    dst = cv2.dilate(dst, kernel)
    dst = cv2.medianBlur(dst,3)
    if (cv2.__version__).split('.')[0] == '3':
        _, contours, _ = cv2.findContours(dst, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
    else:
        contours, _ = cv2.findContours(dst, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
    area,mask_area = 0,0
    if len(contours) == 1 or len(contours) == 0:
        return None
    for contour in contours:
        area_now = cv2.contourArea(contour)
        dst = cv2.fillPoly(dst, [contour], (0))
        if area_now > area:
            area = area_now
            Matrix = contour
            max_area = area_now
    if max_area > max_nu_area:
        max_nu_area = max_area #维护最大的五个
        flag = False
    else:flag = True
    (x, y, w, h) = cv2.boundingRect(Matrix)
    dst = cv2.fillPoly(dst, [Matrix], (255))
    coordination = [x, y, x + w, y + h]
    if max_area/(w*h)<0.3:
        return None
    #print('masking', maskimg.shape)
    #print('dst', dst.shape)
    #pdb.set_trace()
    mask_dst = cv2.bitwise_and(maskimg, dst)
    if w < 350 or h <350:
        return None
    if (cv2.__version__).split('.')[0] == '3':
        _, contours, _ = cv2.findContours(mask_dst, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
    else:
        contours, _ = cv2.findContours(mask_dst, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
    for contour in contours:
        mask_area_now = cv2.contourArea(contour)
        if mask_area_now > mask_area:
            mask_area = mask_area_now
    proportion = float(mask_area/max_area)
    if proportion<1.05 and proportion>0.5:
        #print(coordination)
        A,B,C = mask_dst, mask_dst, mask_dst
        mask_dst = cv2.merge([A,B,C])
        img = cv2.bitwise_and(mask_dst, frame)
        img = img[coordination[1]:coordination[3],coordination[0]:coordination[2]]
        frame = frame[coordination[1]:coordination[3],coordination[0]:coordination[2]]
        #cv2.imshow('dst',img)
        #cv2.waitKey(1000)
        #print(all_nu)
        #if all_nu>4: return 'True'
        ratio = (w/h if h>w else h/w)
        if ratio<0.5:
            return None
        if all_nu<5:
            savenu = all_nu
        elif all_nu>=5 and not(flag):
            savenu = all_nu%5
            print(savenu)
        else:
            return 'True'
        cv2.imwrite('images/' + str(savenu)+'.jpg', img)
        cv2.imwrite('images/' + 'ori'+ str(savenu)+'.jpg', frame)
        #cv2.imwrite(os.sep.join([result_path, str(all_nu)+'.jpg']), img)
        return 'con'
    else:
        return None


def get_object_location(file_test, mask_path,result_path):
    cap = cv2.VideoCapture(file_test)
    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
    fgbg = cv2.createBackgroundSubtractorMOG2(detectShadows = False)#高斯混合模型为基础背景
    nu= 10
    working_nu = 0
    while True:
        ret, frame = cap.read()
        if not ret:
            break
        frame = cv2.medianBlur(frame, ksize=3)
        frame_motion = frame.copy()
        fgmask = fgbg.apply(frame_motion)
        mask= cv2.threshold(fgmask, 25, 255, cv2.THRESH_BINARY)[1]  # 二值化
        mask = cv2.dilate(mask, kernel, iterations=1)
        if nu<=30:
            res = get_object_mask(frame, mask, mask_path, working_nu, result_path)
            #print(res)
            if res=='True':
                break
            elif res == 'con':
                working_nu+=1
            else: continue
        else:break
        nu+=1

def analysis(file_test, mask_path, result_path):
    #mask_path = 'mask.jpg'
    #result_path = 'result'
    if not (os.path.exists(result_path)):
        os.mkdir(result_path)
    get_object_location(file_test, mask_path, result_path)
if __name__ == '__main__':
    mask_path = 'mask2.jpg'
    result_path = 'images'
    file_test = "ftp/anonymous/20210908-150524_e8e24395-fc7b-42f2-a50a-068c4ac73ee9_6921168509256.mp4"
    analysis(file_test, mask_path, result_path)