<reponame>y-kunii/face_classification<filename>src/video_emotion_color_demo_stereo.py
from statistics import mode

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

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

def draw_image(gray_image,rgb_image, faces):
    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)


# 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
cv2.namedWindow('window_frame')
video_captureL = cv2.VideoCapture(0)
video_captureR = cv2.VideoCapture(2)
bgr_imageL = np.zeros((480,640,3), np.uint8)
bgr_imageR = np.zeros((480,640,3), np.uint8)
while True:
    bgr_imageL = video_captureL.read()[1]
    bgr_imageR = video_captureR.read()[1]
    gray_imageL = cv2.cvtColor(bgr_imageL, cv2.COLOR_BGR2GRAY)
    gray_imageR = cv2.cvtColor(bgr_imageR, cv2.COLOR_BGR2GRAY)
    rgb_imageL = cv2.cvtColor(bgr_imageL, cv2.COLOR_BGR2RGB)
    rgb_imageR = cv2.cvtColor(bgr_imageR, cv2.COLOR_BGR2RGB)
    facesL = detect_faces(face_detection, gray_imageL)
    facesR = detect_faces(face_detection, gray_imageR)

    draw_image(gray_imageL,rgb_imageL, facesL)
    draw_image(gray_imageR,rgb_imageR, facesR)

    bgr_imageL = cv2.cvtColor(rgb_imageL, cv2.COLOR_RGB2BGR)
    bgr_imageR = cv2.cvtColor(rgb_imageR, cv2.COLOR_RGB2BGR)
    stereoImg = cv2.hconcat([bgr_imageL, bgr_imageR])
    cv2.imshow('window_frame', stereoImg)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
video_captureL.release()
video_captureR.release()
cv2.destroyAllWindows()

from statistics import mode

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

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

def draw_image(gray_image,rgb_image, faces):
    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)


# 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
cv2.namedWindow('window_frame')
video_captureL = cv2.VideoCapture(0)
video_captureR = cv2.VideoCapture(2)
bgr_imageL = np.zeros((480,640,3), np.uint8)
bgr_imageR = np.zeros((480,640,3), np.uint8)
while True:
    bgr_imageL = video_captureL.read()[1]
    bgr_imageR = video_captureR.read()[1]
    gray_imageL = cv2.cvtColor(bgr_imageL, cv2.COLOR_BGR2GRAY)
    gray_imageR = cv2.cvtColor(bgr_imageR, cv2.COLOR_BGR2GRAY)
    rgb_imageL = cv2.cvtColor(bgr_imageL, cv2.COLOR_BGR2RGB)
    rgb_imageR = cv2.cvtColor(bgr_imageR, cv2.COLOR_BGR2RGB)
    facesL = detect_faces(face_detection, gray_imageL)
    facesR = detect_faces(face_detection, gray_imageR)

    draw_image(gray_imageL,rgb_imageL, facesL)
    draw_image(gray_imageR,rgb_imageR, facesR)

    bgr_imageL = cv2.cvtColor(rgb_imageL, cv2.COLOR_RGB2BGR)
    bgr_imageR = cv2.cvtColor(rgb_imageR, cv2.COLOR_RGB2BGR)
    stereoImg = cv2.hconcat([bgr_imageL, bgr_imageR])
    cv2.imshow('window_frame', stereoImg)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
video_captureL.release()
video_captureR.release()
cv2.destroyAllWindows()
# 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