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entex / real_time_video.py

kysmet

emo

87e340a almost 2 years ago

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3.13 kB

	from keras.preprocessing.image import img_to_array
	import imutils
	import cv2
	from keras.models import load_model
	import numpy as np

	# 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"]


	#feelings_faces = []
	#for index, emotion in enumerate(EMOTIONS):
	# feelings_faces.append(cv2.imread('emojis/' + emotion + '.png', -1))

	# starting video streaming
	cv2.namedWindow('your_face')
	camera = cv2.VideoCapture(0)
	while True:
	frame = camera.read()[1]
	#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)

	canvas = np.zeros((250, 300, 3), dtype="uint8")
	frameClone = frame.copy()
	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
	# Extract the ROI of the face from the grayscale image, resize it to a fixed 28x28 pixels, and then prepare
	# the ROI for classification via the CNN
	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()]
	else: continue


	for (i, (emotion, prob)) in enumerate(zip(EMOTIONS, preds)):
	# construct the label text
	text = "{}: {:.2f}%".format(emotion, prob * 100)

	# draw the label + probability bar on the canvas
	# emoji_face = feelings_faces[np.argmax(preds)]


	w = int(prob * 300)
	cv2.rectangle(canvas, (7, (i * 35) + 5),
	(w, (i * 35) + 35), (0, 0, 255), -1)
	cv2.putText(canvas, text, (10, (i * 35) + 23),
	cv2.FONT_HERSHEY_SIMPLEX, 0.45,
	(255, 255, 255), 2)
	cv2.putText(frameClone, label, (fX, fY - 10),
	cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
	cv2.rectangle(frameClone, (fX, fY), (fX + fW, fY + fH),
	(0, 0, 255), 2)
	# for c in range(0, 3):
	# frame[200:320, 10:130, c] = emoji_face[:, :, c] * \
	# (emoji_face[:, :, 3] / 255.0) + frame[200:320,
	# 10:130, c] * (1.0 - emoji_face[:, :, 3] / 255.0)


	cv2.imshow('your_face', frameClone)
	cv2.imshow("Probabilities", canvas)
	if cv2.waitKey(1) & 0xFF == ord('q'):
	break

	camera.release()
	cv2.destroyAllWindows()