<p>尝试此操作(添加的代码用#NEW comment标记)</p>
<pre><code>...
waste_found = False # NEW
for frame1 in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):
t1 = cv2.getTickCount()
# Acquire frame and expand frame dimensions to have shape: [1, None, None, 3]
# i.e. a single-column array, where each item in the column has the pixel RGB value
frame = np.copy(frame1.array)
frame.setflags(write=1)
frame_expanded = np.expand_dims(frame, axis=0)
# Perform the actual detection by running the model with the image as input
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: frame_expanded})
# Draw the results of the detection (aka 'visulaize the results')
vis_util.visualize_boxes_and_labels_on_image_array(
frame,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=8,
min_score_thresh=0.40)
# p = GPIO.PWM(servoPIN, 50)
# p.start(2.5)
for i in range(classes.size):
if (classes[0][i] == 2 and scores[0][i] > 0.5):
print("e waste detected")
waste_found = True # NEW
break # NEW
# elif(classes[0][i] == 1 and scores[0][i]>0.5):
# print("recycle detected")
# p.start(2.5) # Initialization
## p.ChangeDutyCycle(5)
# time.sleep(4)
# p.ChangeDutyCycle(10)
# time.sleep(4)
# except KeyboardInterrupt:
# p.stop()
# GPIO.cleanup()
if waste_found: # NEW
break # NEW
# return image_np
</code></pre>