1 | initial version |

If we look at the code closely we can see that everying is dependent on the outputs of `detections`

, line 121, and we should tweak its outputs to match them with the `outs`

of this, line 63. After spending almost a day, I came to a reasonable (not the perfect) solution. Basically, it is all about output blobs of `readNetFromCaffe`

and `readFromDarknet`

, because they output a blob with a shape `1x1xNx7`

and `NxC`

, respectively. Here `N`

s are the number of detections, but with different size vectors, namely, `N`

in `1x1xNx7`

is is a number of detections and an every detection is a vector of values
**[batchId, classId, confidence, left, top, right, bottom]** and `N`

in `NxC`

a number of
detected objects and C is a number of classes + 4 where the first 4 numbers are **[center_x, center_y, width, height]**. After analyzing these, we may replace (124-130 lines)

```
for i in np.arange(0, detections.shape[2]):
confidence = detections[0, 0, i, 2]
if confidence > args["confidence"]:
idx = int(detections[0, 0, i, 1])
if CLASSES[idx] != "person":
continue
box = detections[0, 0, i, 3:7] * np.array([W, H, W, H])
(startX, startY, endX, endY) = box.astype("int")
```

with equivalent lines

```
for i in np.arange(0, detections.shape[0]):
scores = detections[i][5:]
classId = np.argmax(scores)
confidence = scores[classId]
if confidence > args["confidence"]:
idx = int(classId)
if CLASSES[idx] != "person":
continue
center_x = int(detections[i][0] * 416)
center_y = int(detections[i][1] * 416)
width = int(detections[i][2] * 416)
height = int(detections[i][3] * 416)
left = int(center_x - width / 2)
top = int(center_y - height / 2)
right = width + left - 1
bottom = height + top - 1
box = [left, top, width, height]
(startX, startY, endX, endY) = box
```

This way we can keep track of "person" class using Darknet's *cfg* and *weights* and count them up/down with a visualiation line.

Again, there might be some other more simpler ways of tracking the detections of Darknet weights file, but this works for this particular case.

**A reference:** more about blobs output by `readNetFromCaffe`

and `readFromDarknet`

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