camera.py

import cv2
from model import FacialExpressionModel
import numpy as np
import time

facec = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
model = FacialExpressionModel("model.json", "model_weights.h5")
font = cv2.FONT_HERSHEY_SIMPLEX

# Create a file to save predictions.
f = open("live_stat/RawPredictions.txt", "w+")
f.close()


class WebCamera(object):
    def __init__(self):
        self.video = cv2.VideoCapture(0)

    def __del__(self):
        self.video.release()

    # returns camera frames along with bounding boxes and predictions
    def get_frame(self):
        _, fr = self.video.read()
        gray_fr = cv2.cvtColor(fr, cv2.COLOR_BGR2GRAY)
        faces = facec.detectMultiScale(gray_fr, 1.3, 5)

        for (x, y, w, h) in faces:
            fc = gray_fr[y : y + h, x : x + w]

            roi = cv2.resize(fc, (48, 48))
            pred = model.predict_emotion(roi[np.newaxis, :, :, np.newaxis])

            # Generate timestamp.
            timestr = time.strftime("%H:%M:%S")
            # Send predictions to the Predictions file.
            f = open("live_stat/RawPredictions.txt", "a+")
            f.write(timestr + ": " + pred + "\n")
            f.close()

            cv2.putText(fr, pred, (x, y), font, 1, (0, 0, 255), 4)
            cv2.rectangle(fr, (x, y), (x + w, y + h), (255, 0, 0), 2)

        _, jpeg = cv2.imencode(".jpg", fr)
        return jpeg.tobytes()


class Video(object):
    def __init__(self, id):
        if id == 1:
            self.video = cv2.VideoCapture("videos/presidential_debate.mp4")
        if id == 2:
            self.video = cv2.VideoCapture("videos/facial_exp.mkv")

    def __del__(self):
        self.video.release()

    # returns camera frames along with bounding boxes and predictions
    def get_frame(self):
        _, fr = self.video.read()
        gray_fr = cv2.cvtColor(fr, cv2.COLOR_BGR2GRAY)
        faces = facec.detectMultiScale(gray_fr, 1.3, 5)

        for (x, y, w, h) in faces:
            fc = gray_fr[y : y + h, x : x + w]

            roi = cv2.resize(fc, (48, 48))
            pred = model.predict_emotion(roi[np.newaxis, :, :, np.newaxis])

            cv2.putText(fr, pred, (x, y), font, 1, (0, 0, 255), 4)
            cv2.rectangle(fr, (x, y), (x + w, y + h), (255, 0, 0), 2)

        _, jpeg = cv2.imencode(".jpg", fr)
        return jpeg.tobytes()


def get_img(img_path):
    fr = cv2.imread(img_path)
    gray_fr = cv2.cvtColor(fr, cv2.COLOR_BGR2GRAY)
    faces = facec.detectMultiScale(gray_fr, 1.3, 5)

    for (x, y, w, h) in faces:
        fc = gray_fr[y : y + h, x : x + w]

        roi = cv2.resize(fc, (48, 48))
        pred = model.predict_emotion(roi[np.newaxis, :, :, np.newaxis])

    return pred