some design

emotionRecognition.py

@@ -61,7 +61,7 @@ def rectPoints(rect):
 detector = dlib.get_frontal_face_detector()
 predictor = dlib.shape_predictor(faceLandmarks)
 
-emotionModelPath = 'models/emotionModel.hdf5'  # fer2013_mini_XCEPTION.110-0.65
+emotionModelPath = 'models/emotionModel.hdf5'
 emotionClassifier = load_model(emotionModelPath, compile=False)
 emotionTargetSize = emotionClassifier.input_shape[1:3]
 

genderClassificationForImage.py

@@ -4,87 +4,92 @@
 import dlib
 import cv2
 
+
 def shapePoints(shape):
-	coords = np.zeros((68, 2), dtype="int")
-	for i in range(0, 68):
-		coords[i] = (shape.part(i).x, shape.part(i).y)
-	return coords
+    coords = np.zeros((68, 2), dtype="int")
+    for i in range(0, 68):
+        coords[i] = (shape.part(i).x, shape.part(i).y)
+    return coords
 
 
 def rectPoints(rect):
-	x = rect.left()
-	y = rect.top()
-	w = rect.right() - x
-	h = rect.bottom() - y
-	return (x, y, w, h)
-
+    x = rect.left()
+    y = rect.top()
+    w = rect.right() - x
+    h = rect.bottom() - y
+    return (x, y, w, h)
 
 
 genderModelPath = 'models\genderModel_VGG16.hdf5'
 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)
+    },
 }
 
-
 # pre-trained model
 modelFile = "faceDetection/models/dnn/res10_300x300_ssd_iter_140000.caffemodel"
 # prototxt has the information of where the training data is located.
 configFile = "faceDetection/models/dnn/deploy.prototxt"
 net = cv2.dnn.readNetFromCaffe(configFile, modelFile)
 
-def detectFacesWithDNN(frame):
-	# A neural network that really supports the input value
-	size = (300,300)
-
-	# After executing the average reduction, the image needs to be scaled
-	scalefactor = 1.0
 
-	# These are our mean subtraction values. They can be a 3-tuple of the RGB means or
-	# they can be a single value in which case the supplied value is subtracted from every
-	# channel of the image.
-	swapRB = (104.0, 117.0, 123.0)
-
-	height, width = frame.shape[:2]
-	resizedFrame = cv2.resize(frame, size)
-	blob = cv2.dnn.blobFromImage(resizedFrame, scalefactor, size, swapRB)
-	net.setInput(blob)
-	dnnFaces = net.forward()
-	for i in range(dnnFaces.shape[2]):
-		confidence = dnnFaces[0, 0, i, 2]
-		if confidence > 0.5:
-			box = dnnFaces[0, 0, i, 3:7] * np.array([width, height, width, height])
-			(x, y, x1, y1) = box.astype("int")
-			resized = frame[y: y1, x:x1]
-			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_vstack = np.vstack([frame_reshape])
-			gender_prediction = genderClassifier.predict(frame_vstack)
-			gender_probability = np.max(gender_prediction)
-			color = (255,255,255)
-			if(gender_probability > 0.4):
-
-				gender_label = np.argmax(gender_prediction)
-				gender_result = genders[gender_label]["label"]
-				color = genders[gender_label]["color"]
-				cv2.putText(frame, gender_result , (x+5, y1-5),
-				cv2.FONT_HERSHEY_SIMPLEX, 1 , color, 2,   cv2.LINE_AA)
-				cv2.rectangle(frame, (x, y), (x1, y1), color, 2)
-			else:
-				cv2.rectangle(frame, (x, y), (x1, y1), color, 2)
-	return frame
-
-photo = cv2.imread("images/photo1.jpg")
-photo = cv2.resize(photo, (1080,720))
+def detectFacesWithDNN(frame):
+    size = (300, 300)
+    scalefactor = 1.0
+    swapRB = (104.0, 117.0, 123.0)
+
+    height, width = frame.shape[:2]
+    resizedFrame = cv2.resize(frame, size)
+    blob = cv2.dnn.blobFromImage(resizedFrame, scalefactor, size, swapRB)
+    net.setInput(blob)
+    dnnFaces = net.forward()
+    for i in range(dnnFaces.shape[2]):
+        confidence = dnnFaces[0, 0, i, 2]
+        if confidence > 0.5:
+            box = dnnFaces[0, 0, i, 3:7] * np.array(
+                [width, height, width, height])
+            (x, y, x1, y1) = box.astype("int")
+            resized = frame[y:y1, x:x1]
+            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_vstack = np.vstack([frame_reshape])
+            gender_prediction = genderClassifier.predict(frame_vstack)
+            gender_probability = np.max(gender_prediction)
+            color = (255, 255, 255)
+            if (gender_probability > 0.4):
+
+                gender_label = np.argmax(gender_prediction)
+                gender_result = genders[gender_label]["label"]
+                color = genders[gender_label]["color"]
+                cv2.rectangle(frame, (x, y), (x1, y1), color, 2)
+                cv2.line(frame, (x, y1), (x + 20, y1 + 20), color, thickness=2)
+                cv2.rectangle(frame, (x + 20, y1 + 20), (x + 135, y1 + 40),
+                              color, -1)
+                cv2.putText(frame, gender_result, (x + 25, y1 + 36),
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1,
+                            cv2.LINE_AA)
+            else:
+                cv2.rectangle(frame, (x, y), (x1, y1), color, 2)
+    return frame
+
+
+photo = cv2.imread("images/barackObama.jpeg")
+photo = cv2.resize(photo, (1080, 720))  # option
 frame = detectFacesWithDNN(photo)
 cv2.imshow("Gender Classification", frame)
 cv2.waitKey(0)

genderClassificationWithDNN.py

@@ -26,15 +26,8 @@
 
 
 def detectFacesWithDNN(frame):
-    # A neural network that really supports the input value
     size = (300, 300)
-
-    # After executing the average reduction, the image needs to be scaled
     scalefactor = 1.0
-
-    # These are our mean subtraction values. They can be a 3-tuple of the RGB means or
-    # they can be a single value in which case the supplied value is subtracted from every
-    # channel of the image.
     swapRB = (104.0, 117.0, 123.0)
 
     height, width = frame.shape[:2]