华荣三照明、合信、荣欣八组合馈电

QT5_Project/KD_ZM_6/opencv/MultiFunc_V2.py

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+import sys
+import cv2
+import time
+import numpy as np
+import threading
+import socket
+import struct, os, select
+
+
+class GlobVar:
+    server_ip = '192.168.1.17'
+    server_port = 8000
+    name_namelist = ''  # 人脸匹配结果
+
+    event2 = threading.Event()  # 用于告知socket进程，数据已准备好，可以发送给服务器了
+    event3 = threading.Event()  # 用于告知socket进程，允许或不允许修改name_namelist
+
+    flag = 0
+
+    st_time = 0  # 记录定时1秒的起始时间
+    nd_time = 0  # 记录定时1秒的结束时间
+
+    frame_new = []
+    frame_old = []
+
+    sys_path = sys.path[0].replace("\\", "/")
+    
+    # 加载人脸检测模型
+    tensorflowWeightFile = sys_path + "/opencv_face_detector.pbtxt"
+    tensorflowModelFile = sys_path + "/opencv_face_detector_uint8.pb"
+
+    conf_threshold = 0.7
+
+    # net = cv2.dnn.readNetFromCaffe(caffeWeightFile, caffeModel=None)   # 未找到caffee的模型文件
+    net = cv2.dnn.readNetFromTensorflow(tensorflowModelFile, tensorflowWeightFile)
+
+    faces = np.array([])
+
+
+class ClientNetworkThread(threading.Thread):
+
+    def __init__(self):
+        self._running = True  # 定义线程状态变量
+        super().__init__()
+
+    def terminate(self):
+        self._running = False
+
+    def run(self):
+        while self._running:
+            if GlobVar.event2.isSet():
+                sock_client_image()
+            time.sleep(0.02)
+
+
+def sock_client_image():
+    # 1、建立socket
+    try:
+        s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+        s.connect((GlobVar.server_ip, GlobVar.server_port))
+        s.setblocking(0)  # 设置为非阻塞模式
+    except socket.error as msg:
+        print("%s L102" % msg)
+        print("未连接到服务器 L103")
+        try:
+            s.close()
+        except socket.error as msg:
+            print(msg)
+        return
+    # 2、发送图片数据，如果发送成功则等待接收，如果不成功则不进行接收
+    flag = socket_send(s)
+    # 3、接收识别结果
+    if flag: socket_recv(s)
+    # 4、关闭socket
+    try:
+        s.close()
+    except socket.error as msg:
+        print(msg)
+
+
+def action():
+    if len(GlobVar.faces) == 1:  # 检测到有且仅有一张人脸，并且保持静止1秒
+        if GlobVar.flag == 0:
+            GlobVar.flag = 1
+            GlobVar.frame_old = GlobVar.frame_new
+            GlobVar.st_time = time.time()  # 开始计时
+        else:
+            if moving_detect(GlobVar.frame_old, GlobVar.frame_new):  # 画面静止
+                GlobVar.nd_time = time.time()  # 计算时间是否达到1秒
+                # print("已保持不动 {0} 秒".format(end - start))
+                if GlobVar.nd_time - GlobVar.st_time >= 1 and GlobVar.flag == 1:  # 计时1秒，时间到
+                    GlobVar.flag = 2
+                    print('存在单张人脸L55')
+
+            else:  # 画面有变动
+                GlobVar.flag = 0
+    else:  # 无人脸或有多张人脸
+        GlobVar.flag = 0
+        if len(GlobVar.faces) > 1:
+            print("Error: More than one person！！！")
+        else:
+            print("No person！")
+
+
+def function(image):
+    GlobVar.faces = []  # 清空
+    GlobVar.name_namelist = ""  # 清空
+    GlobVar.event2.clear()
+    GlobVar.event3.set()
+    GlobVar.frame_new = image
+
+    (h, w) = image.shape[:2]
+    blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0,
+                                 (300, 300), (104.0, 177.0, 123.0))
+
+    GlobVar.net.setInput(blob)
+    detections = GlobVar.net.forward()
+
+    for i in range(0, detections.shape[2]):
+        # extract the confidence (i.e., probability) associated with the
+        # prediction
+        confidence = detections[0, 0, i, 2]
+        # filter out weak detections by ensuring the `confidence` is
+        # greater than the minimum confidence
+        if confidence < GlobVar.conf_threshold:
+            continue
+        # compute the (x, y)-coordinates of the bounding box for the
+        # object
+        box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
+        (startX, startY, endX, endY) = box.astype("int")
+
+        GlobVar.faces.append(i)
+        GlobVar.faces[i] = [startX, startY, endX, endY]
+        # cv2.rectangle(frame, (startX, startY), (endX, endY), (0, 255, 255), 1)
+
+    # 再根据人脸个数做出判断
+    action()
+
+    # 若存在单张人脸且静止时间达到1秒
+    if GlobVar.flag == 2:
+        print("返回人脸位置坐标，并且发送图片到server")
+        GlobVar.flag = 0
+        saveimage(h, w)
+        GlobVar.event2.set()  # 告知子进程数据已准备好
+
+    GlobVar.event3.clear()
+    name = GlobVar.name_namelist
+    GlobVar.event3.set()
+
+    # 返回faces和name
+    return GlobVar.faces, name
+
+
+def moving_detect(img1, img2):  # 比较两幅画面，判断是否静止
+    grey_diff = cv2.absdiff(img1, img2)  # 计算两幅图的像素差
+    change = np.average(grey_diff)
+    if change > 10:
+        return False  # 画面有明显变动，返回False
+    else:
+        return True  # 画面无明显变动，返回True
+
+
+def saveimage(height, width):
+    # 提取人脸区域并保存成图片
+    for (x, y, endx, endy) in GlobVar.faces:
+        (w, h) = (endx - x, endy - y)
+        left = int(x - 0.25 * w)
+        if left < 0: left = 0
+        right = int(x + 1.25 * w)
+        if right > width: right = width
+        top = int(y - 0.25 * h)
+        if top < 0: top = 0
+        bottom = int(y + 1.25 * h)
+        if bottom > height: bottom = height
+
+    cropped_image = GlobVar.frame_new[top:bottom, left:right]
+    cv2.imwrite("singleface.jpg", cropped_image)
+
+
+def socket_send(s):
+    file_path = 'singleface.jpg'  # 输入当前目录下的图片名
+    # pack按照图片格式、图片名字、图片大小
+
+    fsend = struct.pack(b'128sq',  # 图片打包的格式为128sq
+                        bytes(os.path.basename(file_path), encoding='utf-8'),  # 返回图片文件名，编码字节化
+                        os.stat(file_path).st_size)  # 返回读取文件的相关属性，然后得到其中st_size的数据(记录文件大小)
+    try:
+        s.send(fsend)
+    except socket.error as msg:
+        print(msg)
+        return 0  # 数据发送失败，返回0
+
+    fp = open(file_path, 'rb')  # 打开要传输的图片
+
+    while True:
+        data = fp.read(1024)  # 读入图片数据,data是图片的数据字节流(由于图片字节很多，这里一次只读取1024个字节)
+        if not data:  # 如果读到字节的数据没有了
+            print('{0} send over...'.format(len(data)))
+            break
+        try:
+            s.send(data)  # 以二进制格式发送图片数据(每次发1024个字节)
+        except socket.error as msg:
+            print(msg)
+            return 0  # 数据发送出错，返回0
+
+    print("数据已发送,开始接收...")
+    GlobVar.event2.clear()
+    return 1  # 数据发送完毕，返回1
+
+
+def socket_recv(s):
+    st1 = time.time()
+    response = ""
+
+    while True:
+        infds, outfds, errfds = select.select([s, ], [], [])
+        if len(infds) > 0:  # 有数据返回，不管是否成功接收，均跳出while循环
+            try:
+                buf = s.recv(1024).decode('utf-8')
+                print("received: " + str(buf))
+                response += str(buf)
+                break
+            except socket.error as msg:
+                print(msg)
+                break
+            except BlockingIOError:
+                break
+
+        else:
+            print("no data comes back")
+
+        st2 = time.time()
+        if st2 - st1 > 0.5:
+            break  # 等待超时
+    if GlobVar.event3.isSet():
+        GlobVar.name_namelist = response