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README.md

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 # face-detection-azure
 Face, Emotion, Age, Gender Detection Using Microsoft Azure Face API
+
+Demo: https://youtu.be/e0xqMmpjYL8
+
+:star: Star me on GitHub — it helps!
+
+[![Maintenance](https://img.shields.io/badge/maintained-yes-green.svg)](https://github.com/ioarun/face-detection-azure/commits/main)
+[![Website shields.io](https://img.shields.io/badge/website-up-yellow)](https://youtu.be/e0xqMmpjYL8)
+[![Ask Me Anything !](https://img.shields.io/badge/ask%20me-linkedin-1abc9c.svg)](https://www.linkedin.com/in/ioarun/)
+[![License](http://img.shields.io/:license-mit-blue.svg?style=flat-square)](http://badges.mit-license.org)
+
+# Dependencies
+
+* Python 3.6.9
+* azure-cognitiveservices-vision-face==0.5.0
+* cv2==4.1.1
+* msrest==0.6.21
+* threading (for multi-threading)
+
+# Usage
+``
+python script.py
+``
+
+
+## License
+[![License](http://img.shields.io/:license-mit-blue.svg?style=flat-square)](http://badges.mit-license.org)
+
+- **[MIT license](http://opensource.org/licenses/mit-license.php)**

script.py

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+'''
+Face Detection using Azure Cognitive Services
+Demonstration link - https://youtu.be/e0xqMmpjYL8
+
+Environment Information:
+-----------------------
+	-- Python 3.6.9
+	-- azure-cognitiveservices-vision-face==0.5.0
+	-- cv2==4.1.1
+	-- msrest==0.6.21
+	-- threading (for multi-threading)
+
+'''
+
+import cv2
+import time
+from azure.cognitiveservices.vision.face import FaceClient
+from msrest.authentication import CognitiveServicesCredentials
+
+import threading
+
+subscription_key="YOUR_SUBSCRIPTION_KEY"
+endpoint="YOUR_ENDPOINT"
+face_client=FaceClient(endpoint, CognitiveServicesCredentials(subscription_key))
+
+class FaceDetector(threading.Thread):
+	'''
+	FaceDetector class inherits the Thread class which enables it to 
+	run multiple functions simultaneously.
+
+	We will run one function to render real-time video and
+	the other function to call face detector API and 
+	render the results after every 1 second.
+
+	'''
+	def __init__(self, threadID, name):
+		'''
+		Arguments
+		---------
+			threadID (int) : unique thread id
+			name (str) : title of the webcam
+
+		Returns
+		-------
+			None.
+		'''
+		threading.Thread.__init__(self)
+		self.threadID = threadID
+		self.name = name
+
+		# Position of the sub-headings
+		self.org = (50, 30)
+		# Font style of the text
+		self.font = cv2.FONT_HERSHEY_SIMPLEX
+		# Font size
+		self.font_scale = 1
+		# Font color
+		self.color = (0, 255, 0)
+		# Bounding box thickness
+		self.thickness = 2
+		# age
+		self.age = ""
+		# gender
+		self.gender = ""
+
+		# Initialize frames to None
+		self.frame2 = None
+		self.frame = None
+
+		# Initialze and launch web camera
+		self.cap = cv2.VideoCapture()
+		self.cap.open(0)
+
+		# Emotion counter
+		self.counter = 0
+
+	# Captures and displays real-time video
+	def run(self):
+		# Initialize frames to legitimate values 
+		_, self.frame = self.cap.read()
+		self.frame2 = self.frame.copy()
+
+		# Forever loop
+		while (True):
+			# Capture the frame
+			_, frame = self.cap.read()
+			# Copy the frame
+			self.frame = frame.copy()
+			# Put heading on the frame 
+			frame = cv2.putText(frame, "Real Time", self.org, self.font, self.font_scale, self.color, self.thickness, cv2.LINE_AA)
+			# Prepare split display
+			frame_full = cv2.hconcat([frame, self.frame2])
+			# Display
+			cv2.imshow(self.name, frame_full)
+			# Add a 1 ms delay
+			# Else the video won't render
+			cv2.waitKey(1)
+			# Check if the window is closed
+			# If yes, break.
+			if cv2.getWindowProperty(self.name, cv2.WND_PROP_VISIBLE) <1:
+				break
+		# Destroy all the windows
+		cv2.destroyAllWindows()
+
+	# Detects face and attributes
+	def detect_faces(self, local_image):
+		face_attributes = ['emotion', 'age', 'gender']
+		# Call the face detection web service
+		detected_faces = face_client.face.detect_with_stream(local_image, return_face_attributes=face_attributes, detection_model='detection_01')
+		return detected_faces
+
+	# The detector function
+	def detector(self):
+		emotions_ref = ["neutral", "sadness", "happiness", "disgust", "contempt", "anger", "surprise", "fear"]
+		emotions_found  = []
+		# Forever loop
+		while (True):
+			# Add a delay of 1 second
+			time.sleep(1)
+			# Copy the frame at the current moment
+			frame = self.frame.copy()
+			# Save it as an image
+			cv2.imwrite('test.jpg', frame)
+			# Open the image
+			local_image = open('test.jpg', "rb")
+			# Pass the image to the detector webservice
+			faces = self.detect_faces(local_image)
+			# Check if the faces are found
+			if (len(faces)>0):
+				# Extract face attributes of the first face found
+				age = faces[0].face_attributes.age
+				gender = faces[0].face_attributes.gender
+				gender = (gender.split('.'))[0]
+				# Extrac the emotion out of the face attributes
+				emotion = self.get_emotion(faces[0].face_attributes.emotion)
+				# If the detected emotion is new, update the counter
+				if(emotion[0] in emotions_ref):
+					self.counter += 1
+					emotions_ref.remove(emotion[0])
+				# Extract the bounding box
+				left, top, width, height = self.getRectangle(faces[0])
+				# Draw the bounding box and
+				# Display the results on screen
+				frame = cv2.rectangle(frame, (left, top), (left+width, top+height+100), (255, 0, 0), 3)
+				frame = cv2.rectangle(frame, (left, top+height), (left+width, top+height+100), (255, 0, 0), cv2.FILLED)
+				frame = cv2.putText(frame, "age: "+str(int(age)), (left, top+height+20), self.font, 0.6, self.color, self.thickness, cv2.LINE_AA)
+				frame = cv2.putText(frame, "gender: "+str(gender), (left, top+height+40), self.font, 0.6, self.color, self.thickness, cv2.LINE_AA)
+				frame = cv2.putText(frame, "emotion: ", (left, top+height+60), self.font, 0.6, self.color, self.thickness, cv2.LINE_AA)
+				frame = cv2.putText(frame, str(emotion[0]), (left, top+height+80), self.font, 0.6, self.color, self.thickness, cv2.LINE_AA)
+				frame = cv2.putText(frame, "Face Detection", self.org, self.font, self.font_scale, self.color, self.thickness, cv2.LINE_AA)
+				frame = cv2.putText(frame, "#emotions : "+str(self.counter), (400, 30), self.font,  self.font_scale, self.color, self.thickness, cv2.LINE_AA)
+				self.frame2 = frame
+
+	# Emotion extractor function
+	def get_emotion(self, emotion_obj):
+	    emotion_dict = dict()
+	    emotion_dict['anger'] = emotion_obj.anger
+	    emotion_dict['contempt'] = emotion_obj.contempt
+	    emotion_dict['disgust'] = emotion_obj.disgust
+	    emotion_dict['fear'] = emotion_obj.fear
+	    emotion_dict['happiness'] = emotion_obj.happiness
+	    emotion_dict['neutral'] = emotion_obj.neutral
+	    emotion_dict['sadness'] = emotion_obj.sadness
+	    emotion_dict['surprise'] = emotion_obj.surprise
+	    emotion_name = max(emotion_dict, key=emotion_dict.get)
+	    emotion_confidence = emotion_dict[emotion_name]
+	    return emotion_name, emotion_confidence
+
+	# Bounding box extractor function
+	def getRectangle(self, faceDictionary):
+	    rect = faceDictionary.face_rectangle
+	    left = rect.left
+	    top = rect.top
+	    width = rect.width
+	    height = rect.height
+	    return left, top, width, height
+
+# Create FaceDetector object
+detector = FaceDetector(1, "Face Detection - Azure")
+# Start the real-time capture and render
+detector.start()
+# Wait for half a second
+time.sleep(0.5)
+# Start the face detector function
+detector.detector()