HandsomeGroup/作业/20200404136张文虎/Day8 20200404136张文虎/DigitForm.py

from PyQt5.QtGui import QImage, QPixmap
from PyQt5.QtWidgets import *
from DigitDev import DigitDev
from DigitUI import Ui_Digit
from reco2 import Lenet5Recognizier
from PIL import Image
import numpy as np
import copy
import sys
import cv2


class DigitForm(QDialog):
    def __init__(self):
        super(DigitForm, self).__init__()
        # 加载UI（先设计好）
        # 创建对象
        self.ui = Ui_Digit()
        # 使用setupUi绑定对话框（父窗体）
        self.ui.setupUi(self)
        # AI识别对象
        self.reco = Lenet5Recognizier
        # 创建视频对象
        self.dev = DigitDev()
        self.dev.signal_video.connect(self.show_video)
        self.dev.start()

    def img_cut(self, img_file):
        img = cv2.imread(img_file)  # 加载图像
        img2 = copy.deepcopy(img)  # 创建副本用于显示轮廓
        img3 = copy.deepcopy(img)
        img4 = copy.deepcopy(img)
        img5 = copy.deepcopy(img)
        img6 = copy.deepcopy(img)
        img7 = copy.deepcopy(img)
        img8 = copy.deepcopy(img)

        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # 将图像转换为灰度图像
        ret, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY_INV)  # 阈值处理将灰度图像转换为二值图像（反转）

        contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)  # 查找图像中的轮廓
        cv2.drawContours(img2, contours, -1, (0, 0, 255), 3)  # 绘制初始轮廓

        area = map(cv2.contourArea, contours)  # 计算每个轮廓的面积
        area_list = list(area)
        area_max = max(area_list)  # 找到最大面积的轮廓
        post = area_list.index(area_max)

        area_list.remove(area_max)  # 找到第二大面积的轮廓
        area_second_max = max(area_list)
        post_second = area_list.index(area_second_max)

        cv2.drawContours(img4, contours, post, (0, 0, 255), 3)  # 绘制只显示最大轮廓的图像

        cimg = np.zeros_like(img)
        cimg[:, :, :] = 255
        cv2.drawContours(cimg, contours, post, color=(0, 0, 0), thickness=-1)  # 创建只包含最大轮廓的图像
        final = cv2.bitwise_or(img5, cimg)  # 将原始图像和最大轮廓图像进行逐像素的或操作
        cv2.imwrite('img1.jpg',final)

        cv2.drawContours(img6, contours, post_second, (0, 0, 255), 3)  # 绘制只显示第二大轮廓的图像
        cimg = np.zeros_like(img)
        cimg[:, :, :] = 255
        cv2.drawContours(cimg, contours, post_second, color=(0, 0, 0), thickness=-1)  # 创建只包含第二大轮廓的图像

        final = cv2.bitwise_or(img7, cimg)  # 将原始图像和第二大轮廓图像进行逐像素的或操作
        cv2.imwrite('img2.jpg',final)
        cv2.destroyAllWindows()


    # 抓取图像按钮
    def capture_image(self):
        # 抓取图像
        self.capture_data = self.buffer_data
        self.capture_shape = self.buffer_shape
        # 显示抓取的图像
        # byte -> QImage
        h, w, ch = self.capture_shape
        image = QImage(self.capture_data, w, h, w*ch, QImage.Format_BGR888)
        # QImage -> QPixmap
        pixmap = QPixmap.fromImage(image)
        # QPixmap -> QLabel
        self.ui.lbl_image.setPixmap(pixmap)
        self.ui.lbl_image.setScaledContents(True)

        # 将QPixmap转化为png文件
        pixmap = QPixmap(pixmap)
        image = pixmap.toImage()
        width, height = image.width(), image.height()
        image.save('example.png')


    # 识别图像按钮
    def digit_recognize(self):
        self.img_cut('example.png')
        result1 = self.reco.recognizie("img1.jpg")
        result2 = self.reco.recognizie("img2.jpg")

        # self.ui.lbl_top1.setText('11')
        # self.ui.lbl_top1 = QLabel()

        self.ui.lbl_top1.setPixmap(QPixmap('img1.jpg'))
        self.ui.lbl_top1.setScaledContents(True)  # 自适应QLabel大小
        self.ui.lbl_prob1.setText(F"{result1[0]}")

        # self.ui.lbl_top2.setText('22')
        # # self.ui.lbl_top2 = QLabel()
        self.ui.lbl_top2.setPixmap(QPixmap('img2.jpg'))
        self.ui.lbl_top2.setScaledContents(True)
        self.ui.lbl_prob2.setText(F"{result2[0]}")

        self.ui.lbl_result.setText(self.judgment('img1.jpg','img2.jpg'))

    # 进行判断
    def judgment(self,x,y):
        r1 = self.reco.recognizie(x)
        r2 = self.reco.recognizie(y)
        if r1[0] == r2[0]:
            return '平局!'
        elif r1[0] == '剪刀' and r2[0] == '布' or r1[0] == '布' and r2[0] == '石头' or r1[0] == '石头' and r2[0] == '剪刀':
            return '左方胜利！'
        elif r1[0] == '布' and r2[0] == '剪刀' or r1[0] == '剪刀' and r2[0] == '石头' or r1[0] == '石头' and r2[0] == '布':
            return '右方胜利！'

    # 抓取图像处理
    def show_video(self, h, w, ch, data):
        self.buffer_data = data
        self.buffer_shape = (h, w, ch)
        # byte -> QImage
        image = QImage(data, w, h, w*ch, QImage.Format_RGB888)
        # QImage -> QPixmap
        pixmap = QPixmap.fromImage(image)
        # QPixmap -> QLabel 
        self.ui.lbl_video.setPixmap(pixmap)
        self.ui.lbl_video.setScaledContents(True)