<gh_stars>1-10
from statistics import mode

import cv2
from keras.models import load_model
import numpy as np

#import matplotlib
#matplotlib.use('agg')

from utils.datasets import get_labels
from utils.inference import detect_faces
from utils.inference import draw_text
from utils.inference import draw_bounding_box
from utils.inference import apply_offsets
from utils.inference import load_detection_model
from utils.preprocessor import preprocess_input

import argparse

import datetime
import base64
import paho.mqtt.publish as publish

# parameters for loading data and images
detection_model_path = '../trained_models/detection_models/haarcascade_frontalface_default.xml'
emotion_model_path = '../trained_models/emotion_models/fer2013_mini_XCEPTION.102-0.66.hdf5'
emotion_labels = get_labels('fer2013')

# hyper-parameters for bounding boxes shape
frame_window = 10
emotion_offsets = (20, 40)

# loading models
face_detection = load_detection_model(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)

# getting input model shapes for inference
emotion_target_size = emotion_classifier.input_shape[1:3]

# starting lists for calculating modes
emotion_window = []

# starting video streaming
def main():
    parser = argparse.ArgumentParser(description='Emotion detection with demo video file',
                                     prog='video_playback_emotion_demo.py')
    parser.add_argument("-f", "--file", type=str, help='video file', default='../w251demo/afew_train_angry.avi')
    args = parser.parse_args()
    video_file=args.file

    cv2.namedWindow('window_frame')
    video_capture = cv2.VideoCapture(video_file)
    frame_count = 0
    while (video_capture.isOpened()):
        bgr_image = video_capture.read()[1]
        gray_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY)
        rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
        faces = detect_faces(face_detection, gray_image)

        for face_coordinates in faces:

            x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
            gray_face = gray_image[y1:y2, x1:x2]
            try:
                gray_face = cv2.resize(gray_face, (emotion_target_size))
            except:
                continue

            gray_face = preprocess_input(gray_face, True)
            gray_face = np.expand_dims(gray_face, 0)
            gray_face = np.expand_dims(gray_face, -1)
            emotion_prediction = emotion_classifier.predict(gray_face)
            emotion_probability = np.max(emotion_prediction)
            emotion_label_arg = np.argmax(emotion_prediction)
            emotion_text = emotion_labels[emotion_label_arg]
            emotion_window.append(emotion_text)

            if len(emotion_window) > frame_window:
                emotion_window.pop(0)
            try:
                emotion_mode = mode(emotion_window)
            except:
                continue

            if emotion_text == 'angry':
                color = emotion_probability * np.asarray((255, 0, 0))
            elif emotion_text == 'sad':
                color = emotion_probability * np.asarray((0, 0, 255))
            elif emotion_text == 'happy':
                color = emotion_probability * np.asarray((255, 255, 0))
            elif emotion_text == 'surprise':
                color = emotion_probability * np.asarray((0, 255, 255))
            else:
                color = emotion_probability * np.asarray((0, 255, 0))

            color = color.astype(int)
            color = color.tolist()

            draw_bounding_box(face_coordinates, rgb_image, color)
            draw_text(face_coordinates, rgb_image, emotion_mode,
                      color, 0, -45, 1, 1)
                      
            print("*********************** START PUBLISHING ***********************")
            # Create JSON string
            str_ts = str(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
            str_classify = "{video_file: %s, frame_number: %s, emotion_probability: %s, emotion_text: %s, timestamp: %s}"%(video_file, frame_count, emotion_probability, emotion_text, str_ts)
            print("Classification String: ", str_classify)
            # Publish Image to MQTT
            encode_im = cv2.imencode(".jpg", rgb_image)[1].tostring()
            encode64 = base64.b64encode(encode_im)
            #publish.single(topic="test_ap_mqtt", payload=encode64, hostname="localhost")
            publish.single(topic="fc_img_mqtt", payload=encode64, hostname="172.16.17.32")
            publish.single(topic="fc_json_mqtt", payload=str_classify, hostname="172.16.17.32")
            frame_count += 1
            print("*********************** END PUBLISHING ***********************")

        bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
        cv2.imshow('window_frame', bgr_image)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

if __name__ == '__main__':
    main()

from statistics import mode

import cv2
from keras.models import load_model
import numpy as np

#import matplotlib
#matplotlib.use('agg')

from utils.datasets import get_labels
from utils.inference import detect_faces
from utils.inference import draw_text
from utils.inference import draw_bounding_box
from utils.inference import apply_offsets
from utils.inference import load_detection_model
from utils.preprocessor import preprocess_input

import argparse

import datetime
import base64
import paho.mqtt.publish as publish

# parameters for loading data and images
detection_model_path = '../trained_models/detection_models/haarcascade_frontalface_default.xml'
emotion_model_path = '../trained_models/emotion_models/fer2013_mini_XCEPTION.102-0.66.hdf5'
emotion_labels = get_labels('fer2013')

# hyper-parameters for bounding boxes shape
frame_window = 10
emotion_offsets = (20, 40)

# loading models
face_detection = load_detection_model(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)

# getting input model shapes for inference
emotion_target_size = emotion_classifier.input_shape[1:3]

# starting lists for calculating modes
emotion_window = []

# starting video streaming
def main():
    parser = argparse.ArgumentParser(description='Emotion detection with demo video file',
                                     prog='video_playback_emotion_demo.py')
    parser.add_argument("-f", "--file", type=str, help='video file', default='../w251demo/afew_train_angry.avi')
    args = parser.parse_args()
    video_file=args.file

    cv2.namedWindow('window_frame')
    video_capture = cv2.VideoCapture(video_file)
    frame_count = 0
    while (video_capture.isOpened()):
        bgr_image = video_capture.read()[1]
        gray_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY)
        rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
        faces = detect_faces(face_detection, gray_image)

        for face_coordinates in faces:

            x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
            gray_face = gray_image[y1:y2, x1:x2]
            try:
                gray_face = cv2.resize(gray_face, (emotion_target_size))
            except:
                continue

            gray_face = preprocess_input(gray_face, True)
            gray_face = np.expand_dims(gray_face, 0)
            gray_face = np.expand_dims(gray_face, -1)
            emotion_prediction = emotion_classifier.predict(gray_face)
            emotion_probability = np.max(emotion_prediction)
            emotion_label_arg = np.argmax(emotion_prediction)
            emotion_text = emotion_labels[emotion_label_arg]
            emotion_window.append(emotion_text)

            if len(emotion_window) > frame_window:
                emotion_window.pop(0)
            try:
                emotion_mode = mode(emotion_window)
            except:
                continue

            if emotion_text == 'angry':
                color = emotion_probability * np.asarray((255, 0, 0))
            elif emotion_text == 'sad':
                color = emotion_probability * np.asarray((0, 0, 255))
            elif emotion_text == 'happy':
                color = emotion_probability * np.asarray((255, 255, 0))
            elif emotion_text == 'surprise':
                color = emotion_probability * np.asarray((0, 255, 255))
            else:
                color = emotion_probability * np.asarray((0, 255, 0))

            color = color.astype(int)
            color = color.tolist()

            draw_bounding_box(face_coordinates, rgb_image, color)
            draw_text(face_coordinates, rgb_image, emotion_mode,
                      color, 0, -45, 1, 1)
                      
            print("*********************** START PUBLISHING ***********************")
            # Create JSON string
            str_ts = str(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
            str_classify = "{video_file: %s, frame_number: %s, emotion_probability: %s, emotion_text: %s, timestamp: %s}"%(video_file, frame_count, emotion_probability, emotion_text, str_ts)
            print("Classification String: ", str_classify)
            # Publish Image to MQTT
            encode_im = cv2.imencode(".jpg", rgb_image)[1].tostring()
            encode64 = base64.b64encode(encode_im)
            #publish.single(topic="test_ap_mqtt", payload=encode64, hostname="localhost")
            publish.single(topic="fc_img_mqtt", payload=encode64, hostname="172.16.17.32")
            publish.single(topic="fc_json_mqtt", payload=str_classify, hostname="172.16.17.32")
            frame_count += 1
            print("*********************** END PUBLISHING ***********************")

        bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
        cv2.imshow('window_frame', bgr_image)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

if __name__ == '__main__':
    main()
#import matplotlib #matplotlib.use('agg') # parameters for loading data and images # hyper-parameters for bounding boxes shape # loading models # getting input model shapes for inference # starting lists for calculating modes # starting video streaming # Create JSON string # Publish Image to MQTT #publish.single(topic="test_ap_mqtt", payload=encode64, hostname="localhost")