feat: Combine rekog and register into one run, so the system can swit…

…ch easily between recognizing and registering without exit the program

register.py

@@ -5,11 +5,10 @@
 from datetime import datetime
 import os
 
-now = datetime.now()
-this_time = now.strftime("%b-%d-%Y--%H:%M:%S")
-
-
 def register(database='./database', name='temp'):
+    now = datetime.now()
+    this_time = now.strftime("%b-%d-%Y--%H:%M:%S:%f")[:-3]
+
     file_path = os.path.join(database,name)
     r = 0
     R = 11
@@ -48,11 +47,13 @@ def register(database='./database', name='temp'):
         if isRegister:
             cv2.destroyWindow("Register Face")
             cv2.imshow("Register Success!", frame)
+            cv2.waitKey(3000)
             break
         else:
             cv2.imshow("Register Face", frame)
 
-        if cv2.waitKey(1) & 0xFF == ord('q'):
+        key = cv2.waitKey(1)
+        if key == ord('q'):
             break
 
     video.release()

rekog.py

@@ -6,25 +6,16 @@
 from model import get_input_shape
 import helper
 import time
+from register import register
 
-# n -> freeze frames for n seconds
-# F -> camera fps
-# f -> current frame
-# R -> wait time 
-# r -> radius
-F = 30
-n = F
-f = 0
-R = 0
-r = 180
-isFreeze = False
+FPS = 30
 
 def gstreamer_pipeline(
     capture_width=640,
     capture_height=480,
     display_width=640,
     display_height=480,
-    framerate=F,
+    framerate=FPS,
     flip_method=0,
 ):
     return (
@@ -46,90 +37,108 @@ def gstreamer_pipeline(
         )
     )
 
-# detector = MTCNN()
-FR = FaceRecognition()
+def rekog():
+    # n -> freeze frames for n seconds
+    # f -> current frame
+    # R -> wait time 
+    # r -> radius
+    n = 30
+    f = 0
+    R = 0
+    r = 180
+    isFreeze = False
+
+
+    # detector = MTCNN()
+    FR = FaceRecognition()
 
-# Using webcam
-video = cv2.VideoCapture(0)
-# Using gstreamer
-# video = cv2.VideoCapture(gstreamer_pipeline(), cv2.CAP_GSTREAMER)
+    # Using webcam
+    video = cv2.VideoCapture(0)
+    # Using gstreamer
+    # video = cv2.VideoCapture(gstreamer_pipeline(), cv2.CAP_GSTREAMER)
 
-if not video.isOpened():
-    raise Exception('Error opening the camera')
+    if not video.isOpened():
+        raise Exception('Error opening the camera')
 
-# Open the camera and get the camera size
-ret, temp = video.read()
+    # Open the camera and get the camera size
+    ret, temp = video.read()
 
-# convert image to grayscale image
-gray_image = cv2.cvtColor(temp, cv2.COLOR_BGR2GRAY)
-# convert the grayscale image to binary image
-ret,thresh = cv2.threshold(gray_image,127,255,0)
-# calculate moments of binary image
-M = cv2.moments(thresh)
-# calculate x,y coordinate of center
-CX = int(M["m10"] / M["m00"])
-CY = int(M["m01"] / M["m00"])
+    # convert image to grayscale image
+    gray_image = cv2.cvtColor(temp, cv2.COLOR_BGR2GRAY)
+    # convert the grayscale image to binary image
+    ret,thresh = cv2.threshold(gray_image,127,255,0)
+    # calculate moments of binary image
+    M = cv2.moments(thresh)
+    # calculate x,y coordinate of center
+    CX = int(M["m10"] / M["m00"])
+    CY = int(M["m01"] / M["m00"])
 
-while True:
-    # Open camera for recognizing
-    ret, frame = video.read()
-    rekog_frame = frame[CY-r:CY+r, CX-r:CX+r]
-
-    # Draw rekog circle
-    cv2.circle(frame, center=(CX,CY), radius=r, color=(243,144,29), thickness=3)
+    while True:
+        # Open camera for recognizing
+        ret, frame = video.read()
+        rekog_frame = frame[CY-r:CY+r, CX-r:CX+r]
+        
+        # Draw rekog circle
+        cv2.circle(frame, center=(CX,CY), radius=r, color=(243,144,29), thickness=3)
 
-    # When freeze don't analyze faces for 11 frames
-    if isFreeze == True and 0 <= R <= 10:
-        faces = []
-        R += 1
-        print(f"R = {R}")
-    else:
-        faces = helper.detectFacesLive(rekog_frame)
-        R = 0
-        isFreeze = False
+        # When freeze don't analyze faces for 11 frames
+        if isFreeze == True and 0 <= R <= 10:
+            faces = []
+            R += 1
+            print(f"R = {R}")
+        else:
+            faces = helper.detectFacesLive(rekog_frame)
+            R = 0
+            isFreeze = False
 
-    if len(faces) != 0:
-        x, y, w, h = faces[0]
-
-        # Draw rectangle in face
-        cv2.rectangle(rekog_frame,
-                    (x, y),
-                    (x+w, y+h),
-                    (0,155,255),
-                    2)
-
-        # Predict
-        img = helper.detectFace(rekog_frame[y:y+h, x:x+w], get_input_shape(), stream=True)
-        if img.shape[1:3] == get_input_shape(): 
-            pred, score = FR.predict(img)
-
-        # Count until freeze
-        f += 1
-        print(f"frame {f}")
-
-        # Freeze to show the predicted face
-        if f == n or score != "":
-            isFreeze = True
-            f = 0
-            freeze_img = rekog_frame.copy()
-            # Draw label class prediction
-            cv2.rectangle(freeze_img, (x, y+h + 65), (x+w, y+h), (0, 0, 0), cv2.FILLED)
-            if pred == "":
-                cv2.rectangle(freeze_img, (x, y), (x+w, y+h), (0,155,255), cv2.FILLED)
-                cv2.putText(freeze_img, "Not Employee!", (x + 3, y+h + 25), cv2.FONT_HERSHEY_DUPLEX, 0.8, (255, 255, 255), 1)
-            elif pred != "":
-                cv2.putText(freeze_img, f"{pred}", (x + 3, y+h + 25), cv2.FONT_HERSHEY_DUPLEX, 0.8, (255, 255, 255), 1)
-                cv2.putText(freeze_img, f"{score:.2f}", (x + 3, y+h + 55), cv2.FONT_HERSHEY_DUPLEX, 0.6, (255, 255, 255), 1)
-            else:
-                None
-            cv2.imshow(f"Hello {pred}", freeze_img)
-            cv2.waitKey(3000)
-            cv2.destroyWindow(f"Hello {pred}")
+        if len(faces) != 0:
+            x, y, w, h = faces[0]
+            
+            # Draw rectangle in face
+            cv2.rectangle(rekog_frame,
+                        (x, y),
+                        (x+w, y+h),
+                        (0,155,255),
+                        2)
+            
+            # Predict
+            img = helper.detectFace(rekog_frame[y:y+h, x:x+w], get_input_shape(), stream=True)
+            if img.shape[1:3] == get_input_shape(): 
+                pred, score = FR.predict(img)
+            
+            # Count until freeze
+            f += 1
+            print(f"frame {f}")
+            
+            # Freeze to show the predicted face
+            if f == n or score != "":
+                isFreeze = True
+                f = 0
+                freeze_img = rekog_frame.copy()
+                # Draw label class prediction
+                cv2.rectangle(freeze_img, (x, y+h + 65), (x+w, y+h), (0, 0, 0), cv2.FILLED)
+                if pred == "":
+                    cv2.rectangle(freeze_img, (x, y), (x+w, y+h), (0,155,255), cv2.FILLED)
+                    cv2.putText(freeze_img, "Not Employee!", (x + 3, y+h + 25), cv2.FONT_HERSHEY_DUPLEX, 0.8, (255, 255, 255), 1)
+                elif pred != "":
+                    cv2.putText(freeze_img, f"{pred}", (x + 3, y+h + 25), cv2.FONT_HERSHEY_DUPLEX, 0.8, (255, 255, 255), 1)
+                    cv2.putText(freeze_img, f"{score:.2f}", (x + 3, y+h + 55), cv2.FONT_HERSHEY_DUPLEX, 0.6, (255, 255, 255), 1)
+                else:
+                    None
+                cv2.imshow(f"Hello {pred}", freeze_img)
+                cv2.waitKey(3000)
+                cv2.destroyWindow(f"Hello {pred}")
 
-    cv2.imshow('Hello Welcome to iNews Tower', frame)
+        cv2.imshow('Hello Welcome to iNews Tower', frame)
 
-    if cv2.waitKey(1) & 0xFF == ord('q'):
-        break
+        key = cv2.waitKey(1)
+        if key == ord('q'):
+            break
+        elif key == ord('r'):
+            video.release()
+            cv2.destroyAllWindows()
+            register(name='daniel')
+            rekog()
 
-video.release()
-cv2.destroyAllWindows()
+    video.release()
+    cv2.destroyAllWindows()

run.py

@@ -0,0 +1,3 @@
+from rekog import rekog
+
+rekog()