Hello;
I‘m running the real_time_object_detection program,and the problem is :D:\python3.5.2\Model\object-detection-deep-learning>python real_time_object_detection.py --prototxt=MobileNetSSD_deploy.prototxt.txt --model=MobileNetSSD_deploy.caffemodel
[INFO] loading model...
[INFO] starting video stream...
Traceback (most recent call last):
File "real_time_object_detection.py", line 44, in
frame = imutils.resize(frame, width=400)
File "C:\Anaconda3\lib\site-packages\imutils\convenience.py", line 69, in resize
(h, w) = image.shape[:2]
AttributeError: 'NoneType' object has no attribute 'shape'
the code is:
import the necessary packages
from imutils.video import VideoStream
from imutils.video import FPS
import numpy as np
import argparse
import imutils
import time
import cv2
construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--prototxt", required=True,
help="path to Caffe'deploy'prototxt file")
ap.add_argument("-m", "--model", required=True,
help="path to Caffe pre-trained model")
ap.add_argument("-c", "--confidence", type=float, default=0.2,
help="minimum probability to filter weak detections")
args = vars(ap.parse_args())
initialize the list of class labels MobileNet SSD was trained to
detect, then generate a set of bounding box colors for each class
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
"sofa", "train", "tvmonitor"]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
load our serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])
initialize the video stream, allow the cammera sensor to warmup,
and initialize the FPS counter
print("[INFO] starting video stream...")
vs = VideoStream(src=1).start()
time.sleep(2.0)
fps = FPS().start()
loop over the frames from the video stream
while True:
# grab the frame from the threaded video stream and resize it
# to have a maximum width of 400 pixels
frame = vs.read()
frame = imutils.resize(frame, width=400)
# grab the frame dimensions and convert it to a blob
(h, w) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(frame, 0.007843, (300, 300), 127.5)
# pass the blob through the network and obtain the detections and
# predictions
net.setInput(blob)
detections = net.forward()
loop over the detections
for i in np.arange(0, detections.shape[2]):
# extract the confidence (i.e., probability) associated with
# the prediction
confidence = detections[0, 0, i, 2]
# filter out weak detections by ensuring the `confidence` is
# greater than the minimum confidence
if confidence > args["confidence"]:
# extract the index of the class label from the
# `detections`, then compute the (x, y)-coordinates of
# the bounding box for the object
idx = int(detections[0, 0, i, 1])
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
# draw the prediction on the frame
label = "{}: {:.2f}%".format(CLASSES[idx],
confidence * 100)
cv2.rectangle(frame, (startX, startY), (endX, endY),
COLORS[idx], 2)
y = startY - 15 if startY - 15 > 15 else startY + 15
cv2.putText(frame, label, (startX, y),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
show the output frame
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
# update the FPS counter
fps.update()
stop the timer and display FPS information
fps.stop()
print("[INFO] elapsed time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()
may be the problem is it didn't active the camera when I was running “vs = VideoStream(src=1).start()”?
appreciated for helping me,Thank you.