help me setup so this works correctly so I can check the background process:



x, y, w, h = 100, 100, 200, 200
threshold = 400

# Paths to template images
window_template_path = "shot2Target.png"
dot_template_path = "dottemplate.png"

# Load template images
window_template = cv2.imread(window_template_path, 0)
dot_template = cv2.imread(dot_template_path, 0)

# Global variable to store the server’s response
SERVER_RESPONSE = None


# Image processing function
def process_images():
    global SERVER_RESPONSE
    # url = "http://10.30.227.207:8088z/shot.jpg"
    frame = cv2.imread("shot(3).jpg")
    # url = "http://192.168.229.55:8080/shot.jpg"
    window_match_threshold = 0.75  # Adjust according to your needs
    dot_match_threshold = 0.75  # Adjust according to your needs

    while True:
        # Fetch the frame
        # img_resp = requests.get(url)
        # img_arr = np.frombuffer(img_resp.content, np.uint8)
        # frame = cv2.imdecode(img_arr, -1)
        gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

        # Perform template matching for the window
        res = cv2.matchTemplate(gray_frame, window_template, cv2.TM_CCOEFF_NORMED)
        loc = np.where(res >= window_match_threshold)

        if len(loc[0]) > 0:
            min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
            top_left = max_loc
            bottom_right = (
                top_left[0] + window_template.shape[1],
                top_left[1] + window_template.shape[0],
            )
            window_roi = frame[
                top_left[1] : bottom_right[1], top_left[0] : bottom_right[0]
            ]

            # Convert the ROI to grayscale for dot detection
            gray_window_roi = cv2.cvtColor(window_roi, cv2.COLOR_BGR2GRAY)

            thresholded_image = cv2.adaptiveThreshold(
                gray_window_roi,
                255,
                cv2.ADAPTIVE_THRESH_MEAN_C,
                cv2.THRESH_BINARY,
                11,
                11,
            )
            blurred_image = cv2.GaussianBlur(thresholded_image, (3, 3), 0)
            # Perform template matching for the dot within the window ROI
            dot_res = cv2.matchTemplate(
                blurred_image, dot_template, cv2.TM_CCOEFF_NORMED
            )
            dot_loc = np.where(dot_res >= dot_match_threshold)

            if len(dot_loc[0]) > 0:
                # A dot is found, perform the necessary tasks

                # Save the image
                if not os.path.isdir("images"):
                    os.mkdir("images")
                image_path = "images/captured_image.jpg"
                cv2.imwrite(image_path, blurred_image)

                # Check the saved image
                result = check_picture(image_path)  # Assuming this function is defined
                SERVER_RESPONSE = result

                # You can break or continue depending on your process
                # break
                # If you decide to continue, you can throttle the loop
                # time.sleep(1)  # Pause the loop for a given time (in seconds)

        # Throttle the loop to avoid excessive CPU usage
        time.sleep(0.5)


@app.post("/start_processing")
async def start_processing(background_tasks: BackgroundTasks):
    background_tasks.add_task(process_images)
    return {"message": "Image processing started"}


@app.get("/get_response")
async def get_response():
    return {"response": SERVER_RESPONSE}