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@@ -60,8 +60,10 @@ differ = None |
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now = '' |
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framecounter = 0 |
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trackeron = 0 |
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people_count_total = 0 |
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while True: |
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people_count_per_frame = 0 |
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frame = vs.read() |
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frame = frame if args.get("video", None) is None else frame[1] |
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# if the frame can not be grabbed, then we have reached the end of the video |
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@@ -126,9 +128,11 @@ while True: |
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for i in np.arange(0, detections.shape[2]): |
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confidence = detections[0, 0, i, 2] |
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confidence_level = 0.7 |
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confidence_level = 0.8 |
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if confidence > confidence_level: |
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people_count_per_frame+=1 |
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people_count_total+=1 |
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# extract the index of the class label from the `detections`, then compute the (x, y)-coordinates of |
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# the bounding box for the object |
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idx = int(detections[0, 0, i, 1]) |
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@@ -156,7 +160,7 @@ while True: |
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# check to see if we are currently tracking an object, if so, ignore other boxes |
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# this code is relevant if we want to identify particular persons (section 2 of this tutorial) |
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# this code is relevant if we want to identify particular persons |
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if initBB2 is not None: |
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# grab the new bounding box coordinates of the object |
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@@ -183,6 +187,8 @@ while True: |
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info = [ |
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("Success", "Yes" if success else "No"), |
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("FPS", "{:.2f}".format(fps.fps())), |
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("People Frame", "{}".format(people_count_per_frame)), |
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("People Total", "{}".format(people_count_total)) |
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] |
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# loop over the info tuples and draw them on our frame |