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videos.py
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videos.py
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from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
from tensorflow.keras.models import load_model
import numpy as np
import cv2
import matplotlib.pyplot as plt
import os
facenet = cv2.dnn.readNet('models/deploy.prototxt', 'models/res10_300x300_ssd_iter_140000.caffemodel')
model = load_model('models/mask_detector.model')
cap = cv2.VideoCapture('Video_Sample.mp4')
ret, img = cap.read()
fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
out = cv2.VideoWriter('output.mp4', fourcc, cap.get(cv2.CAP_PROP_FPS), (img.shape[1], img.shape[0]))
while cap.isOpened():
ret, img = cap.read()
if not ret:
break
h, w = img.shape[:2]
blob = cv2.dnn.blobFromImage(img, scalefactor=1., size=(300, 300), mean=(104., 177., 123.))
facenet.setInput(blob)
dets = facenet.forward()
result_img = img.copy()
for i in range(dets.shape[2]):
confidence = dets[0, 0, i, 2]
if confidence < 0.5:
continue
x1 = int(dets[0, 0, i, 3] * w)
y1 = int(dets[0, 0, i, 4] * h)
x2 = int(dets[0, 0, i, 5] * w)
y2 = int(dets[0, 0, i, 6] * h)
face = img[y1:y2, x1:x2]
face_input = cv2.resize(face, dsize=(224, 224))
face_input = cv2.cvtColor(face_input, cv2.COLOR_BGR2RGB)
face_input = preprocess_input(face_input)
face_input = np.expand_dims(face_input, axis=0)
mask, nomask = model.predict(face_input).squeeze()
if mask > nomask:
color = (0, 255, 0)
label = 'Mask %d%%' % (mask * 100)
else:
color = (0, 0, 255)
label = 'No Mask %d%%' % (nomask * 100)
cv2.rectangle(result_img, pt1=(x1, y1), pt2=(x2, y2), thickness=2, color=color, lineType=cv2.LINE_AA)
cv2.putText(result_img, text=label, org=(x1, y1 - 10), fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.8, color=color, thickness=2, lineType=cv2.LINE_AA)
out.write(result_img)
cv2.imshow('result', result_img)
if cv2.waitKey(1) == ord('q'):
break
out.release()
cap.release()