real_time_image.py
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from keras.preprocessing.image import img_to_array
import imutils
import cv2
from keras.models import load_model
import numpy as np
import os
import time
def absoluteFilePaths(directory):
for dirpath,_,filenames in os.walk(directory):
for f in filenames:
yield os.path.abspath(os.path.join(dirpath, f))
# parameters for loading data and images
detection_model_path = 'haarcascade_files/haarcascade_frontalface_default.xml'
emotion_model_path = 'models/_mini_XCEPTION.102-0.66.hdf5'
# hyper-parameters for bounding boxes shape
# loading models
face_detection = cv2.CascadeClassifier(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)
EMOTIONS = ["angry" ,"disgust","scared", "happy", "sad", "surprised",
"neutral"]
# starting video streaming
start = time.time()
image_dir_path = 'videos/sample1_images'
emotionList = []
probList = []
for image_path in absoluteFilePaths(image_dir_path):
if 'txt' in image_path:
continue
frame = cv2.imread(image_path)
#reading the frame
frame = imutils.resize(frame,width=300)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_detection.detectMultiScale(gray,scaleFactor=1.1,minNeighbors=5,minSize=(30,30),flags=cv2.CASCADE_SCALE_IMAGE)
if len(faces) > 0:
faces = sorted(faces, reverse=True,
key=lambda x: (x[2] - x[0]) * (x[3] - x[1]))[0]
(fX, fY, fW, fH) = faces
roi = gray[fY:fY + fH, fX:fX + fW]
roi = cv2.resize(roi, (64, 64))
roi = roi.astype("float") / 255.0
roi = img_to_array(roi)
roi = np.expand_dims(roi, axis=0)
preds = emotion_classifier.predict(roi)[0]
emotion_probability = np.max(preds)
label = EMOTIONS[preds.argmax()]
emotionList.append(label)
probList.append(emotion_probability)
else:
emotionList.append('None')
probList.append(0)
continue
print(time.time()-start)
import datetime
image_interval = 1
time = datetime.datetime.strptime('00:00:00','%H:%M:%S')
with open(image_dir_path+'/emotions.txt','w') as file:
for emotion, prob in zip(emotionList,probList):
file.write(time.strftime("%H:%M:%S ")+emotion+' {:.2f}%'.format(prob*100) + '\n')
time += datetime.timedelta(seconds=image_interval)