🟡 Feat: Classification of gender and emotion together with Class Structure

faceProcess.py

@@ -51,7 +51,19 @@ def run(self):
                 color = (245, 215, 130)
                 resized = self.frame[y - 20:y1 + 30, x - 10:x1 + 10]
                 cv2.rectangle(self.frame, (x, y), (x1, y1), color, 2)
-                gender_result = GenderClassification(resized, x, y, x1,
-                                                     y1).predict()
+                try:
+                    gender_result = GenderClassification(
+                        resized, x, y, x1, y1).predict()
+                except:
+                    continue
+
+                cv2.rectangle(self.frame, (x + 20, y1 + 20),
+                              (x + 170, y1 + 55), gender_result['color'], -1)
+                cv2.line(self.frame, (x, y1), (x + 20, y1 + 20),
+                         gender_result['color'],
+                         thickness=2)
+                cv2.putText(self.frame, '{}'.format(gender_result['label']),
+                            (x + 25, y1 + 45), cv2.FONT_HERSHEY_SIMPLEX, 0.8,
+                            (255, 255, 255), 2, cv2.LINE_AA)
 
         return self.frame

genderClassificationWithDLIB.py

@@ -9,6 +9,7 @@
 ap.add_argument("-vw", "--isVideoWriter", type=bool, default=False)
 args = vars(ap.parse_args())
 
+
 def shapePoints(shape):
     coords = np.zeros((68, 2), dtype="int")
     for i in range(0, 68):
@@ -32,18 +33,25 @@ def rectPoints(rect):
 genderClassifier = load_model(genderModelPath, compile=False)
 genderTargetSize = genderClassifier.input_shape[1:3]
 
-genders =  {
-    0: { "label": "Female", "color": (245,215,130) },
-    1: { "label": "Male", "color": (148,181,192) },
+genders = {
+    0: {
+        "label": "Female",
+        "color": (245, 215, 130)
+    },
+    1: {
+        "label": "Male",
+        "color": (148, 181, 192)
+    },
 }
 
 cap = cv2.VideoCapture(0)
 
 if args["isVideoWriter"] == True:
-    fourrcc = cv2.VideoWriter_fourcc("M","J","P","G")
+    fourrcc = cv2.VideoWriter_fourcc("M", "J", "P", "G")
     capWidth = int(cap.get(3))
     capHeight = int(cap.get(4))
-    videoWrite = cv2.VideoWriter("output.avi", fourrcc, 22, (capWidth, capHeight))
+    videoWrite = cv2.VideoWriter("output.avi", fourrcc, 22,
+                                 (capWidth, capHeight))
 
 while True:
     ret, frame = cap.read()
@@ -56,38 +64,37 @@ def rectPoints(rect):
         shape = predictor(frame, rect)
         points = shapePoints(shape)
         (x, y, w, h) = rectPoints(rect)
-        resized = frame[y-20: y+h+30, x-10:x+w+10]
+        resized = frame[y - 20:y + h + 30, x - 10:x + w + 10]
         cv2.imshow("resized: ", resized)
         try:
             frame_resize = cv2.resize(resized, genderTargetSize)
         except:
             continue
 
         frame_resize = frame_resize.astype('float32')
-        frame_scaled = frame_resize/255.0
-        frame_reshape = np.reshape(frame_scaled,(1, 100, 100 ,3))
+        frame_scaled = frame_resize / 255.0
+        frame_reshape = np.reshape(frame_scaled, (1, 100, 100, 3))
         frame_vstack = np.vstack([frame_reshape])
         gender_prediction = genderClassifier.predict(frame_vstack)
         gender_probability = np.max(gender_prediction)
-        color = (255,255,255)
-        if(gender_probability > 0.6):
-        	gender_label = np.argmax(gender_prediction)
-        	gender_result = genders[gender_label]["label"]
-        	color = genders[gender_label]["color"]
-        	cv2.putText(frame, gender_result , (x+5, y+h-5),
-        		cv2.FONT_HERSHEY_SIMPLEX, 1 , color, 2, cv2.LINE_AA)
-        	cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
+        color = (255, 255, 255)
+        if (gender_probability > 0.6):
+            gender_label = np.argmax(gender_prediction)
+            gender_result = genders[gender_label]["label"]
+            color = genders[gender_label]["color"]
+            cv2.putText(frame, gender_result, (x + 5, y + h - 5),
+                        cv2.FONT_HERSHEY_SIMPLEX, 1, color, 2, cv2.LINE_AA)
+            cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
         else:
-        	cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
+            cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
 
     if args["isVideoWriter"] == True:
         videoWrite.write(frame)
 
     cv2.imshow("Gender Classification", frame)
     k = cv2.waitKey(1) & 0xFF
     if k == 27:
-    	break
-
+        break
 
 cap.release()
 if args["isVideoWriter"] == True: