Sure, here's a step-by-step guide to help you write a person counter using Python and OpenCV:

Step 1: Install Python and OpenCV
Make sure you have Python installed on your computer. You can download it from the official website. Once you have Python installed, you need to install OpenCV. You can install OpenCV using the pip command:

```
pip install opencv-python
```

Step 2: Import necessary libraries
Once you have OpenCV installed, you need to import the necessary libraries in your Python code. Here's the code for importing the required libraries:

```python
import cv2
import imutils
```

The cv2 library is used for image processing, while the imutils library provides additional convenience functions for working with images.

Step 3: Load the video
To count the number of people in a video, you need to load the video using OpenCV. Here's the code to load a video:

```python
video = cv2.VideoCapture("path/to/video.mp4")
```

Replace "path/to/video.mp4" with the actual path to your video file.

Step 4: Define the person detector
Next, you need to define a person detector using OpenCV's HOGDescriptor class. This detector will be used to detect people in each frame of the video. Here's the code to define the person detector:

```python
hog = cv2.HOGDescriptor()
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
```

Step 5: Loop through each frame of the video
Now, you need to loop through each frame of the video and detect people in each frame using the person detector. Here's the code to loop through each frame of the video:

```python
while True:
    ret, frame = video.read()
    if not ret:
        break
```

In this code, ret is a Boolean variable that indicates whether a frame was successfully read from the video. If ret is False, it means that there are no more frames to read and the loop should break. The frame variable contains the current frame of the video.

Step 6: Detect people in the current frame
Next, you need to detect people in the current frame using the person detector. Here's the code to detect people in the current frame:

```python
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
rects, _ = hog.detectMultiScale(gray, winStride=(4, 4), padding=(8, 8), scale=1.05)
```

In this code, gray is the grayscale version of the current frame. The hog.detectMultiScale() function is used to detect people in the grayscale image. The winStride parameter controls the step size of the sliding window, while the padding parameter controls the amount of padding around each window. The scale parameter controls the scale factor used to resize the image.

The hog.detectMultiScale() function returns a list of rectangles that represent the bounding boxes of the detected people in the current frame. The _ variable is used to ignore the second return value, which is not needed for this application.

Step 7: Draw bounding boxes around the detected people
We can use the same image variable and draw a rectangle around the detected people using the following code:

```python
for (x, y, w, h) in person_detections:
    cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)

```
The cv2.rectangle() function takes five arguments:
- image: the image we want to draw the rectangle on
- (x, y): the coordinates of the top-left corner of the rectangle
- (x + w, y + h): the coordinates of the bottom-right corner of the rectangle
- (0, 255, 0): the color of the rectangle in BGR format (here we use green)
- 2: the thickness of the rectangle in pixels

Step 8: Display the final image
Finally, we can display the final image with the bounding boxes around the detected people using the following code:

```python
cv2.imshow('Person Counter', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
```

The cv2.imshow() function displays the image in a window titled 'Person Counter'. The cv2.waitKey(0) function waits indefinitely for a key press (in this case, we use it to keep the window open until we press a key). The cv2.destroyAllWindows() function closes all open windows.

Congratulations, you have now written a person counter using Python and OpenCV!
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