44 lines
1.4 KiB
Python
44 lines
1.4 KiB
Python
import numpy as np
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import cv2
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from tensorflow.keras.models import model_from_json
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model_json_file = './input/model.json'
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model_weights_file = './input/model_weights.h5'
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with open(model_json_file, "r") as json_file:
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loaded_model_json = json_file.read()
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loaded_model = model_from_json(loaded_model_json)
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loaded_model.load_weights(model_weights_file)
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face_cascade = cv2.CascadeClassifier('./input/haarcascade_frontalface_default.xml')
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img_size = 64
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cap = cv2.VideoCapture(0)
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import copy
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while True:
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ret, frame = cap.read()
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img = copy.deepcopy(frame)
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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faces = face_cascade.detectMultiScale(gray, 1.3, 5)
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for (x,y,w,h) in faces:
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fc = gray[y:y+h, x:x+w]
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roi = cv2.resize(fc, (img_size,img_size))
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pred = loaded_model.predict(roi[np.newaxis, :, :, np.newaxis])
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text_idx=np.argmax(pred)
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text_list = ['Angry', 'Disgust', 'Fear', 'Happy', 'Neutral', 'Sad', 'Surprise']
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try:
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text = text_list[text_idx]
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except:
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text = 'err'
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cv2.putText(img, text, (x, y-5), cv2.FONT_HERSHEY_SIMPLEX, 2.5, (255, 0, 255), 2)
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img = cv2.rectangle(img, (x,y), (x+w, y+h), (0,0,255), 2)
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cv2.imshow("frame", img)
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key = cv2.waitKey(1) & 0xFF
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if key== ord('q'):
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break
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cap.release()
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cv2.destroyAllWindows() |