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# Detecte Emotion By photo
import cv2
import os
from keras.models import model_from_json
import numpy as np
import matplotlib.pyplot as plt
import threading
def display_image(image_array):
cv2.imshow('My Image', image_array)
cv2.waitKey(0)
cv2.destroyAllWindows()
# os.chdir('models')
emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful",
3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"}
# load json and create model
# put your model path there
json_file = open(
"emotion_recognition/models/emotion_model(MyNet0.82).json", 'r')
loaded_model_json = json_file.read()
json_file.close()
emotion_model = model_from_json(loaded_model_json)
# load weights into new model
# put your weight path there
emotion_model.load_weights(
"emotion_recognition/models/emotion_model(MyNet0.82).h5")
print("Loaded model from disk")
def detectFace(net, frame, confidence_threshold=0.7):
frameOpencvDNN = frame.copy()
print(frameOpencvDNN.shape)
frameHeight = frameOpencvDNN.shape[0]
frameWidth = frameOpencvDNN.shape[1]
blob = cv2.dnn.blobFromImage(frameOpencvDNN, 1.0, (227, 227), [
124.96, 115.97, 106.13], swapRB=True, crop=False)
net.setInput(blob)
detections = net.forward()
faceBoxes = []
for i in range(detections.shape[2]):
confidence = detections[0, 0, i, 2]
if confidence > confidence_threshold:
x1 = int(detections[0, 0, i, 3]*frameWidth)
y1 = int(detections[0, 0, i, 4]*frameHeight)
x2 = int(detections[0, 0, i, 5]*frameWidth)
y2 = int(detections[0, 0, i, 6]*frameHeight)
print("x1=", x1, " x2=", x2, " y1=", y1, " y2=", y2)
faceBoxes.append([x1, y1, x2, y2])
cv2.rectangle(frameOpencvDNN, (x1, y1), (x2, y2),
(0, 255, 0), int(round(frameHeight/150)), 8)
return frameOpencvDNN, faceBoxes
faceProto = 'emotion_recognition/models/opencv_face_detector.pbtxt'
faceModel = 'emotion_recognition/models/opencv_face_detector_uint8.pb'
# Loding detecting face model
faceNet = cv2.dnn.readNet(faceModel, faceProto)
# Get a Test image and process it to send it to model
def ai(path):
f = cv2.imread(path, cv2.IMREAD_COLOR)
# cv2.imshow("fla",f)
gray_frame = cv2.cvtColor(f, cv2.COLOR_BGR2GRAY)
resultImg, faceBoxes = detectFace(faceNet, f)
print('faceBoxes', faceBoxes)
# Get the cordnate of face
x1, y1, x2, y2 = faceBoxes[0][0], faceBoxes[0][1], faceBoxes[0][2], faceBoxes[0][3]
print("x , y , w , h", x1, y1, x2, y2)
roi_gray_frame = gray_frame[y1-20:y2+10, x1-20:x2+10]
cropped_img = np.expand_dims(np.expand_dims(
cv2.resize(roi_gray_frame, (48, 48)), -1), 0)
img_resized = cv2.resize(resultImg, (0, 0), fx=0.5,
fy=0.5, interpolation=cv2.INTER_AREA)
# send photo to model
emotion_prediction = emotion_model.predict(cropped_img)
# Get the result
maxindex = int(np.argmax(emotion_prediction))
cv2.putText(img_resized, emotion_dict[maxindex], (x1+5, y1-20),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)
# cv2.imshow("crop",resultImg)
# cv2.resizeWindow("crop",720,460)
# cv2.imshow("crop2",roi_gray_frame)
# cv2.resizeWindow("crop2",720,460)
print("emotion_prediction=", emotion_dict[maxindex])
display_thread = threading.Thread(
target=display_image, args=(img_resized,))
display_thread.start()
return emotion_dict[maxindex]
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