Sample Python code for sentiment analysis with Rekognition from laptop webcam

rekognition_webcam.py

import time
import cv2
import boto3

# Get the Client
session = boto3.Session()
rekog_client = session.client("rekognition", region_name='us-east-1')
width = 1280
height = 720
scale_factor = 0.1
text_color = (0, 0, 255)

# Current time in milliseconds


def current_milli_time():
    return int(round(time.time() * 1000))

# Use strongest emotion


def find_emotion(emotions):
    num = {'Confidence': 0.0, 'Type': None}
    for item in emotions:
        if item['Confidence'] > num['Confidence']:
            num = item
    return num

# Display captured frame and overlay boxes/output using OpenCV


def display(frame, responses):
    faces = responses['FaceDetails']
    boxes = []
    x1 = x2 = y1 = y2 = 0
    gender = age = smile = mustache = beard = emotion = ""
    for face in faces:
        # For each face, get the data
        boxes.append(face['BoundingBox'])
        gender = face['Gender']['Value']
        age = 'Age: ' + str(face['AgeRange']['Low']) + \
            '-' + str(face['AgeRange']['High'])
        smile = 'Smile: ' + str(face['Smile']['Value'])
        mustache = 'Mustache: ' + str(face['Mustache']['Value'])
        beard = 'Beard: ' + str(face['Beard']['Value'])
        emotion_map = find_emotion(face['Emotions'])
        emotion = emotion_map['Type']

    # Get the face bounding box
    for box in boxes:
        x1 = int(box['Left'] * width)
        y1 = int(box['Top'] * height)
        x2 = int(box['Left'] * width + box['Width'] * width)
        y2 = int(box['Top'] * height + box['Height'] * height)

    cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
    y1 = y1 - 15 if y1 - 15 > 15 else y1 + 15

    font = cv2.FONT_HERSHEY_COMPLEX_SMALL
    cv2.putText(frame, gender, (x2 + 40, y1 + 40),
                font, 2, text_color, 1, cv2.LINE_AA)
    cv2.putText(frame, age, (x2 + 40, y1 + 80),
                font, 2, text_color, 1, cv2.LINE_AA)
    cv2.putText(frame, smile, (x2 + 40, y1 + 120),
                font, 2, text_color, 1, cv2.LINE_AA)
    cv2.putText(frame, mustache, (x2 + 40, y1 + 160),
                font, 2, text_color, 1, cv2.LINE_AA)
    cv2.putText(frame, beard, (x2 + 40, y1 + 200),
                font, 2, text_color, 1, cv2.LINE_AA)
    cv2.putText(frame, emotion, (x2 + 40, y1 + 240),
                font, 2, text_color, 1, cv2.LINE_AA)

    window_name = 'Rekognition Demo'
    cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
    #cv2.setWindowProperty(
    #    window_name, cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN)
    cv2.imshow(window_name, frame)

# Start video capture and loop over frames, calling Rekognition, display results


def main():
    vidcap = cv2.VideoCapture(0)
    vidcap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
    vidcap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)

    while True:
        success, image = vidcap.read()
        cnt = 0
        t1 = current_milli_time()
        while success:
            success, frame = vidcap.read()
            if success:
                scaled_frame = cv2.resize(
                    frame, (int(width * scale_factor), int(height * scale_factor)))

                rval, buffer = cv2.imencode('.jpg', scaled_frame)
                img_bytes = bytearray(buffer)
                response = rekog_client.detect_faces(Image={'Bytes': img_bytes},
                                                     Attributes=['ALL'])
                display(frame, response)

            # Pressing Q for exit
            if cv2.waitKey(20) & 0xFF == ord('q'):
                break

    # Release the camera and windows
    vidcap.release()
    cv2.destroyAllWindows()


if __name__ == '__main__':
    main()