Created
January 4, 2019 09:11
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import cv2 | |
from model import FacialExpressionModel | |
import numpy as np | |
facec = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') | |
model = FacialExpressionModel("model.json", "model_weights.h5") | |
font = cv2.FONT_HERSHEY_SIMPLEX | |
class VideoCamera(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]) | |
cv2.putText(fr, pred, (x, y), font, 1, (255, 255, 0), 2) | |
cv2.rectangle(fr,(x,y),(x+w,y+h),(255,0,0),2) | |
_, jpeg = cv2.imencode('.jpg', fr) | |
return jpeg.tobytes() |
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