adding face recognition

src/cascades/data/__init__.py

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+import os
+
+haarcascades = os.path.join(os.path.dirname(__file__), '')

src/faces-train.py

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+import cv2
+import os
+import numpy as np
+from PIL import Image
+import pickle
+
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+image_dir = os.path.join(BASE_DIR, "images")
+
+face_cascade = cv2.CascadeClassifier(
+    'cascades/data/haarcascade_frontalface_alt2.xml')
+recognizer = cv2.face.LBPHFaceRecognizer_create()
+
+current_id = 0
+label_ids = {}
+y_labels = []
+x_train = []
+
+for root, dirs, files in os.walk(image_dir):
+    # print(root)
+    # print(dirs)
+    # print(files)
+    for file in files:
+        if file.endswith("png") or file.endswith("jpg"):
+            path = os.path.join(root, file)
+            label = os.path.basename(root).replace(" ", "-").lower()
+            #print(label, path)
+            if not label in label_ids:
+                label_ids[label] = current_id
+                current_id += 1
+
+            id_ = label_ids[label]
+            # print(label_ids)
+            # y_labels.append(label) #some number
+            # x_train.append(path) # verify this image turn into a numpy array ,gray
+            pil_image = Image.open(path).convert("L")  # gray scale
+            size = (550, 550)
+            final_image = pil_image.resize(size, Image.ANTIALIAS)
+            image_array = np.array(final_image, "uint8")
+            # print(image_array)
+            faces = face_cascade.detectMultiScale(
+                image_array, scaleFactor=1.5, minNeighbors=5)
+
+            for (x, y, w, h) in faces:
+                roi = image_array[y:y + h, x:x + w]
+                x_train.append(roi)
+                y_labels.append(id_)
+
+# print(y_labels)
+# print(x_train)
+
+
+with open("labels.pickle", "wb") as f:
+    pickle.dump(label_ids, f)
+
+recognizer.train(x_train, np.array(y_labels))
+recognizer.save("trainner.yml")

src/labels.pickle

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+(dp0
+S'fun'
+p1
+I9
+sS'paulina'
+p2
+I3
+sS'kit-harington'
+p3
+I1
+sS'justin'
+p4
+I4
+sS'peter-dinklage'
+p5
+I6
+sS'emilia-clarke'
+p6
+I8
+sS'nikolaj-coster-waldau'
+p7
+I2
+sS'images'
+p8
+I0
+sS'logo'
+p9
+I5
+sS'bartek'
+p10
+I7
+s.

src/smart_intercom.py

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+import cv2
+import datetime
+import time
+import numpy as np
+from pygame import mixer
+import pickle
+import copy
+
+
+def alert():
+    mixer.init()
+    alert = mixer.Sound('noises/beep-07.wav')
+    for i in range(5):
+        alert.play()
+        time.sleep(0.1)
+        alert.play()
+
+
+def click_event(event, x, y, flags, param):
+    if event == cv2.EVENT_LBUTTONDOWN:
+
+        if 620 <= y <= 703 and 20 <= x <= 169:
+            print('cam')
+
+            global cam_status
+            if cam_status == 1:
+                cam_status = 0
+            elif cam_status == 0:
+                cam_status = 1
+
+        elif 620 <= y <= 703 and 189 <= x <= 448:
+            print('doorbell')
+            alert()
+        elif 620 <= y <= 703 and 468 <= x <= 734:
+            print('open gate')
+            global wait_notification
+            wait_notification = 1
+        elif 620 <= y <= 703 and 754 <= x <= 945:
+            print('listen')
+        elif 620 <= y <= 703 and 965 <= x <= 1173:
+            print('pick up')
+        elif 620 <= y <= 703 and 1193 <= x <= 1267:
+            print('off')
+            cap.release()
+            out.release()
+            cv2.destroyAllWindows()
+            exit()
+
+
+def click_event2(event, x, y, flags, param):
+    if event == cv2.EVENT_LBUTTONDOWN:
+        global displayString
+
+        if 348 <= y <= 370 and 59 <= x <= 86:
+            print("1 outdoor")
+
+            displayString = displayString + '1'
+            print(displayString)
+
+        if 348 <= y <= 370 and 100 <= x <= 128:
+            print("2 outdoor")
+
+            displayString = displayString + '2'
+            print(displayString)
+
+        if 348 <= y <= 370 and 142 <= x <= 169:
+            print("3 outdoor")
+            displayString = displayString + '3'
+            print(displayString)
+        if 390 <= y <= 412 and 59 <= x <= 86:
+            print("4 outdoor")
+            displayString = displayString + '4'
+            print(displayString)
+        if 390 <= y <= 412 and 100 <= x <= 128:
+            print("5 outdoor")
+            displayString = displayString + '5'
+            print(displayString)
+        if 390 <= y <= 412 and 142 <= x <= 169:
+            print("6 outdoor")
+            displayString = displayString + '6'
+            print(displayString)
+        if 431 <= y <= 453 and 59 <= x <= 86:
+            print("7 outdoor")
+            displayString = displayString + '7'
+            print(displayString)
+        if 431 <= y <= 453 and 100 <= x <= 128:
+            print("8 outdoor")
+            displayString = displayString + '8'
+            print(displayString)
+        if 431 <= y <= 453 and 142 <= x <= 169:
+            print("9 outdoor")
+            displayString = displayString + '9'
+            print(displayString)
+        if 472 <= y <= 495 and 59 <= x <= 86:
+            print("* outdoor")
+            displayString = displayString + '*'
+            print(displayString)
+            print("0 outdoor")
+            displayString = displayString + '0'
+            print(displayString)
+        if 472 <= y <= 495 and 142 <= x <= 169:
+            print("# outdoor")
+            displayString = displayString + '#'
+            print(displayString)
+
+        display_work(len(displayString))
+
+
+def display_work(length):
+    global displayString
+    if length == 1:
+        onestar = cv2.imread('images/*.png')
+        outDoor[220:304, 54:214] = onestar
+    elif length == 2:
+        twostar = cv2.imread('images/**.png')
+        outDoor[220:304, 54:214] = twostar
+
+    elif length == 3:
+        threestar = cv2.imread('images/***.png')
+        outDoor[220:304, 54:214] = threestar
+
+    elif length == 4:
+
+        if displayString == "1234":
+            goodpass = cv2.imread('images/goodpass.png')
+            outDoor[220:304, 54:214] = goodpass
+
+            if wait_seconds(3) == True:
+                displayString = displayString + '#'
+
+        else:
+            badpass = cv2.imread('images/badpass.png')
+            outDoor[220:304, 54:214] = badpass
+
+    elif length == 5:
+        displayString = ''
+
+
+def notification():
+    not1 = cv2.imread('notification/not1.png')
+    frame[50:50 + 139, 800:800 + 406] = not1
+
+    if wait_seconds(3) == True:
+        global wait_notification
+        wait_notification = 0
+
+
+def putOnDisplay(frame, fheight, sheight, fwidth,
+                 swidth, src):
+    img = cv2.imread(src)
+    frame[fheight: sheight, fwidth: swidth] = img
+
+
+def putTimeOnDisplay(frame):
+    font = cv2.FONT_HERSHEY_SIMPLEX
+
+    actual_time = str(
+        datetime.datetime.now().strftime("%b %d %Y %H:%M:%S"))
+
+    cv2.putText(frame, actual_time, (10, 50),
+                font, 1, (0, 255, 255), 2, cv2.LINE_AA)
+
+
+def wait_3_sec(start):
+    live_time = datetime.datetime.now()
+    time_to_expire = datetime.timedelta(0, 3)
+    if (live_time - start) < time_to_expire:
+        live_time = datetime.datetime.now()
+    else:
+        return True
+
+
+def wait_seconds(time):
+    global pusto
+    if pusto == None:
+        pusto = datetime.datetime.now()
+    else:
+        live_time = datetime.datetime.now()
+        time_to_expire = datetime.timedelta(0, time)
+        if (live_time - pusto) < time_to_expire:
+            live_time = datetime.datetime.now()
+        else:
+            pusto = None
+            return True
+
+
+def most_frequent(List):
+
+    return max(set(List), key=List.count)
+
+
+# Declaration
+global displayString, wait_notification, pusto
+displayString = ""
+wait_notification = 0
+pusto = None
+global cam_status
+cam_status = 1
+List = []
+count = 0
+
+
+cap = cv2.VideoCapture(0)
+four_cc = cv2.VideoWriter_fourcc(*'XVID')
+out = cv2.VideoWriter('recordings/output.avi', four_cc, 10.0, (1280, 720))
+cap.set(3, 1280)
+cap.set(4, 720)
+
+# 3
+
+face_cascade = cv2.CascadeClassifier(
+    'cascades/data/haarcascade_frontalface_alt2.xml')
+recognizer = cv2.face.LBPHFaceRecognizer_create()
+recognizer.read("trainner.yml")
+
+labels = {}
+with open("labels.pickle", "rb") as f:
+    og_labels = pickle.load(f)
+    labels = {v: k for k, v in og_labels.items()}
+###
+
+################################################################################
+
+while True:
+
+    if not cap.isOpened():
+        print("Camera error")
+        break
+
+    ret, frameprev = cap.read()
+
+    if cam_status == 1:
+        frame = copy.deepcopy(frameprev)
+    if cam_status == 0:
+        putOnDisplay(frame, 0, 720, 0, 1280, "images/black.png")
+
+    # 3
+    gray = cv2.cvtColor(frameprev, cv2.COLOR_BGR2GRAY)
+
+    faces = face_cascade.detectMultiScale(
+        gray, scaleFactor=1.5, minNeighbors=5)
+
+    if len(List) < 11:
+
+        for (x, y, w, h) in faces:
+            # print(x, y, w, h)
+            roi_gray = gray[y:y+h, x:x+w]  # (ycord_start, ycord_end)
+            roi_color = frameprev[y:y + h, x:x + w]
+
+            id_, conf = recognizer.predict(roi_gray)
+
+            if conf >= 1:  # and conf <= 85:
+                # print(id_)
+                count = count + 1
+                print(str(count) + " " + labels[id_])
+                font = cv2.FONT_HERSHEY_SIMPLEX
+                name = labels[id_]
+                color = (255, 255, 255)
+                cv2.putText(frame, name, (x, y), font,
+                            1, color, 2, cv2.LINE_AA)
+
+                cam_status = 1
+
+                img_item = "my-image.png"
+                cv2.imwrite(img_item, roi_gray)
+
+                color = (255, 0, 0)  # BGR 0-255
+                stroke = 2
+                end_cord_x = x + w
+                end_cord_y = y + h
+                cv2.rectangle(frame, (x, y), (end_cord_x,
+                                              end_cord_y), color, stroke)
+                List.append(labels[id_])
+
+    if len(List) == 11:
+        font = cv2.FONT_HERSHEY_SIMPLEX
+        putOnDisplay(frame, 120, 255, 60, 430, "images/is_coming.png")
+        cv2.putText(frame, most_frequent(List), (80, 170),
+                    font, 1, (0, 255, 255), 2, cv2.LINE_AA)
+        cv2.putText(frame, "is coming!", (250, 225),
+                    font, 1, (0, 255, 255), 2, cv2.LINE_AA)
+    
+
+    ###################
+
+    if ret:
+
+        # home_screen = copy.deepcopy(frame)
+
+        putTimeOnDisplay(frame)
+
+        putOnDisplay(frame, 620, 720, 0, 1280, 'images/menu.png')
+        putOnDisplay(frame, 640, 640 + 63, 20, 169, 'icons/icon1.png')
+        putOnDisplay(frame, 640, 640 + 63, 189, 448, 'icons/icon2.png')
+        putOnDisplay(frame, 640, 640 + 63, 468, 734, 'icons/icon3.png')
+        putOnDisplay(frame, 640, 640 + 63, 754, 945, 'icons/icon4.png')
+        putOnDisplay(frame, 640, 640 + 63, 965, 1173, 'icons/icon5.png')
+        putOnDisplay(frame, 640, 640 + 63, 1193, 1267, 'icons/icon6.png')
+
+        if wait_notification == 1:
+            notification()
+
+        cv2.imshow('frame', frame)
+
+        outDoor = cv2.imread('images/outDoorPanel.png')
+        display_work(len(displayString))
+
+        #cv2.imshow('outDoor', outDoor)
+
+        out.write(frameprev)
+        cv2.setMouseCallback('frame', click_event)
+        #cv2.setMouseCallback('outDoor', click_event2)
+
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+    else:
+        break
+
+
+cap.release()
+out.release()
+cv2.destroyAllWindows()

src/test.py

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+def most_frequent(List):
+
+    return max(set(List), key = List.count) 
+  
+
+
+List = []
+List = ["1", "2", "3", "4", "5", "2"]
+print(List)
+List.append("3")
+print(List)
+print(len(List))
+
+
+
+
+
+#List = [2, 1, 2, 2, 1, 3] 
+#print(most_frequent(List))s