擅长:python、mysql、java
<p>我通常使用帧ID进行此操作。基本上,您的模型只会在n帧之后进行预测。下面是如何使用它的代码。您可以编辑要跳过的帧数:</p>
<pre><code>frame_id =0
while(1):
frame_id +=1
ret, frame = cam.read()
if ret:
### displays video recording and region of interest
frame = cv2.flip(frame,1)
display = cv2.rectangle(frame.copy(),(startX,startY),(finishX,finishY),(0,0,255),2)
cv2.imshow('Total Input',display)
ROI = frame[startY:finishY, startX:finishX].copy()
cv2.imshow('Region of Interest', ROI)
#pauses for 10 seconds
time.sleep(10)
img = cv2.resize(display, (128, 128)) #R
img = img.reshape(1, 128, 128, 3)
if fram_id % 10 == 0:
predictions = model.predict(img) # Make predictions towards the test set
predicted_label = np.argmax(predictions) # Get index of the predicted label from prediction
print(predicted_label)
if cv2.waitKey(10) & 0xFF == ord('q'):
break
</code></pre>