Дипломная работа

Дипломная работа/app/client/client.py

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+import requests
+import time
+import threading
+
+import cv2 as cv
+import numpy as np
+
+class DoorWorker():
+    def __init__(self):
+        self.door_status = False
+
+    def open_door(self):
+        if not self.door_status:
+            self.door_status = True
+            print("OPEN DOOR")
+            #GPIO.setmode(GPIO.BOARD)
+            #GPIO.setup(16, GPIO.OUT)
+            #GPIO.output(16, 1)
+            time.sleep(5)
+            #GPIO.output(16, 0)
+
+            white_list.clear()
+            permit_list.clear()
+            self.door_status = False
+
+class HttpWorker():
+    def __init__(self):
+        self.last_answer = None
+        self.search_filter = threading.Lock()
+
+    def search_face(self, position, img_data):
+        return
+        if not self.search_filter.locked():
+            with self.search_filter:
+                URL = ADDRESS + "/find_by_img"
+                headers = {
+                    'accept': 'application/json'
+                }
+                files = {
+                    'img_data': ('photo.jpg', img_data["image"], 'image/jpeg')
+                }
+                answer = requests.post(URL, headers=headers, files=files).json()
+                if answer["result"]:
+                    if answer["result"] in white_list:
+                        white_list[answer["result"]] = \
+                            {"position": position,
+                             "counter": white_list[answer["result"]]["counter"] + 1}
+                    else:
+                        white_list[answer["result"]] = \
+                            {"position": position, "counter": 1}
+
+                print(white_list)
+                self.last_answer = answer
+                time.sleep(0.1)
+
+class RecognitionWorker():
+    def __init__(self, cap):
+        self.face_cascade = cv.CascadeClassifier(cv.data.haarcascades + 'haarcascade_frontalface_default.xml')
+
+    def check_frame(self, frame):
+        gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
+        faces = self.face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)
+        return faces
+
+class VisualizeWorker():
+    def drawer(self, image, faces, door_status, permit_list, fps=None):
+        output = image.copy()
+
+        if fps:
+            cv.putText(output, 'FPS: {:.2f}'.format(fps), (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
+
+        white_position = list()
+        if permit_list:
+            for person in permit_list:
+                try:
+                    white_position.append(white_list[person]["position"])
+                except:
+                    pass
+
+        crop_img = list()
+        for position, face in enumerate(faces):
+            coords = faces[position].astype(np.int32)
+            for i in range(len(coords)):
+                if coords[i] < 0:
+                    coords[i] = 0
+
+            if door_status and position in white_position:
+                color = (0, 255, 0)
+            else:
+                color = (0, 0, 255)
+
+            cv.rectangle(output, (coords[0], coords[1]), (coords[0] + coords[2], coords[1] + coords[3]), color, 2)
+            cv.rectangle(output, (coords[0], coords[1]), (coords[0] + coords[2] + 10, coords[1] + coords[3] + 40), (0, 255, 0), 2)
+            crop_img.append(output[coords[1]:coords[1] + coords[3]+40, coords[0]:coords[0] + coords[2]+10])
+
+        return output, crop_img
+
+
+def resize(img):
+    height_size = 640
+    print(height_size, img.shape[0])
+    scale = height_size / img.shape[0]
+
+    width = int(img.shape[1] * scale)
+    height = int(img.shape[0] * scale)
+
+    return cv.resize(img, (width, height))
+
+def permit(check_list):
+    permit_ids = list()
+    if len(check_list) != 0:
+        for person in check_list:
+            if check_list[person]["counter"] >= 3:
+                permit_ids.append(person)
+        return permit_ids
+    else:
+        return
+
+if __name__ == '__main__':
+    ADDRESS = "http://127.0.0.1:5000"
+    device_id = 0
+
+    white_list = dict()
+    door_close = True
+
+    cap = cv.VideoCapture(device_id)
+    tm = cv.TickMeter()
+
+    cv.namedWindow('libfacedetection demo', cv.WINDOW_NORMAL)
+    cv.setWindowProperty('libfacedetection demo', cv.WND_PROP_FULLSCREEN, cv.WINDOW_FULLSCREEN)
+
+    visualize = VisualizeWorker()
+    recognition = RecognitionWorker(cap=cap)
+    request = HttpWorker()
+    door = DoorWorker()
+
+    while cv.waitKey(1) < 0:
+        has_frame, frame = cap.read()
+        if not has_frame:
+            print('No frames grabbed!')
+
+        permit_list = permit(white_list)
+
+        tm.start()
+        faces = recognition.check_frame(frame)
+        tm.stop()
+
+        if faces is not None:
+            frame, crop_imgs = visualize.drawer(frame, faces, door.door_status, permit_list, fps=tm.getFPS())
+
+            for position, img in enumerate(crop_imgs):
+                resize_frame = resize(img)
+
+                _, im_buf_arr = cv.imencode(".jpg", resize_frame)
+                byte_im = im_buf_arr.tobytes()
+
+                threading.Thread(target=request.search_face, args=(position, {'image': byte_im},)).start()
+
+        if permit_list:
+            threading.Thread(target=door.open_door).start()
+
+        cv.imshow('libfacedetection demo', frame)
+        tm.reset()
+
+    print("END")
+