YOLOv8 Counting with Multiple Movable Regions Example (#4929)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>pull/4964/head
Muhammad Rizwan Munawar
1 year ago
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# Regions Counting Using YOLOv8 (Inference on Video) |
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- Region counting is a method employed to tally the objects within a specified area, allowing for more sophisticated analyses when multiple regions are considered. These regions can be adjusted interactively using a Left Mouse Click, and the counting process occurs in real time. |
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- Regions can be adjusted to suit the user's preferences and requirements. |
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<div> |
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<p align="center"> |
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<img src="https://github.com/RizwanMunawar/ultralytics/assets/62513924/978c8dd4-936d-468e-b41e-1046741ec323" width="45%"/> |
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<img src="https://github.com/RizwanMunawar/ultralytics/assets/62513924/069fd81b-8451-40f3-9f14-709a7ac097ca" width="45%"/> |
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</p> |
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</div> |
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## Table of Contents |
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- [Step 1: Install the Required Libraries](#step-1-install-the-required-libraries) |
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- [Step 2: Run the Region Counting Using Ultralytics YOLOv8](#step-2-run-the-region-counting-using-ultralytics-yolov8) |
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- [Usage Options](#usage-options) |
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- [FAQ](#faq) |
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## Step 1: Install the Required Libraries |
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Clone the repository, install dependencies and `cd` to this local directory for commands in Step 2. |
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```bash |
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# Clone ultralytics repo |
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git clone https://github.com/ultralytics/ultralytics |
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# cd to local directory |
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cd ultralytics/examples/YOLOv8-Region-Counter |
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``` |
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## Step 2: Run the Region Counting Using Ultralytics YOLOv8 |
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Here are the basic commands for running the inference: |
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### Note |
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After the video begins playing, you can freely move the region anywhere within the video by simply clicking and dragging using the left mouse button. |
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```bash |
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# If you want to save results |
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python yolov8_region_counter.py --source "path/to/video.mp4" --save-img --view-img |
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# If you want to change model file |
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python yolov8_region_counter.py --source "path/to/video.mp4" --save-img --weights "path/to/model.pt" |
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# If you dont want to save results |
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python yolov8_region_counter.py --source "path/to/video.mp4" --view-img |
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``` |
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## Usage Options |
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- `--source`: Specifies the path to the video file you want to run inference on. |
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- `--save-img`: Flag to save the detection results as images. |
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- `--weights`: Specifies a different YOLOv8 model file (e.g., `yolov8n.pt`, `yolov8s.pt`, `yolov8m.pt`, `yolov8l.pt`, `yolov8x.pt`). |
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- `--line-thickness`: Specifies the bounding box thickness |
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- `--region-thickness`: Specific the region boxes thickness |
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## FAQ |
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**1. What Does Region Counting Involve?** |
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Region counting is a computational method utilized to ascertain the quantity of objects within a specific area in recorded video or real-time streams. This technique finds frequent application in image processing, computer vision, and pattern recognition, facilitating the analysis and segmentation of objects or features based on their spatial relationships. |
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**2. Why Combine Region Counting with YOLOv8?** |
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YOLOv8 specializes in the detection and tracking of objects in video streams. Region counting complements this by enabling object counting within designated areas, making it a valuable application of YOLOv8. |
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**3. How Can I Troubleshoot Issues?** |
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To gain more insights during inference, you can include the `--debug` flag in your command: |
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```bash |
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python yolov8_region_counter.py --source "path to video file" --debug |
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``` |
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**4. Can I Employ Other YOLO Versions?** |
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Certainly, you have the flexibility to specify different YOLO model weights using the `--weights` option. |
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**5. Where Can I Access Additional Information?** |
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For a comprehensive guide on using YOLOv8 with Object Tracking, please refer to [Multi-Object Tracking with Ultralytics YOLO](https://docs.ultralytics.com/modes/track/). |
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import argparse |
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from collections import defaultdict |
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from pathlib import Path |
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import cv2 |
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import numpy as np |
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from ultralytics import YOLO |
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track_history = defaultdict(lambda: []) |
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from ultralytics.utils.files import increment_path |
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from ultralytics.utils.plotting import Annotator, colors |
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# Region utils |
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current_region = None |
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counting_regions = [{ |
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'name': 'YOLOv8 Region A', |
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'roi': (50, 100, 240, 300), |
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'counts': 0, |
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'dragging': False, |
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'region_color': (0, 255, 0)}, { |
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'name': 'YOLOv8 Region B', |
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'roi': (200, 250, 240, 300), |
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'counts': 0, |
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'dragging': False, |
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'region_color': (255, 144, 31)}] |
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def is_inside_roi(box, roi): |
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"""Compare bbox with region box.""" |
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x, y, _, _ = box |
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roi_x, roi_y, roi_w, roi_h = roi |
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return roi_x < x < roi_x + roi_w and roi_y < y < roi_y + roi_h |
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def mouse_callback(event, x, y, flags, param): |
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"""Mouse call back event.""" |
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global current_region |
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# Mouse left button down event |
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if event == cv2.EVENT_LBUTTONDOWN: |
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for region in counting_regions: |
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roi_x, roi_y, roi_w, roi_h = region['roi'] |
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if roi_x < x < roi_x + roi_w and roi_y < y < roi_y + roi_h: |
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current_region = region |
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current_region['dragging'] = True |
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current_region['offset_x'] = x - roi_x |
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current_region['offset_y'] = y - roi_y |
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# Mouse move event |
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elif event == cv2.EVENT_MOUSEMOVE: |
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if current_region is not None and current_region['dragging']: |
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current_region['roi'] = (x - current_region['offset_x'], y - current_region['offset_y'], |
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current_region['roi'][2], current_region['roi'][3]) |
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# Mouse left button up event |
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elif event == cv2.EVENT_LBUTTONUP: |
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if current_region is not None and current_region['dragging']: |
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current_region['dragging'] = False |
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def run(weights='yolov8n.pt', |
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source='test.mp4', |
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view_img=False, |
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save_img=False, |
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exist_ok=False, |
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line_thickness=2, |
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region_thickness=2): |
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""" |
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Run Region counting on a video using YOLOv8 and ByteTrack. |
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Supports movable region for real time counting inside specific area. |
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Supports multiple regions counting. |
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Args: |
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weights (str): Model weights path. |
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source (str): Video file path. |
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view_img (bool): Show results. |
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save_img (bool): Save results. |
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exist_ok (bool): Overwrite existing files. |
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line_thickness (int): Bounding box thickness. |
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region_thickness (int): Region thickness. |
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""" |
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vid_frame_count = 0 |
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# Check source path |
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if not Path(source).exists(): |
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raise FileNotFoundError(f"Source path '{source}' does not exist.") |
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# Setup Model |
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model = YOLO(f'{weights}') |
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# Video setup |
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videocapture = cv2.VideoCapture(source) |
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frame_width, frame_height = int(videocapture.get(3)), int(videocapture.get(4)) |
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fps, fourcc = int(videocapture.get(5)), cv2.VideoWriter_fourcc(*'mp4v') |
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# Output setup |
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save_dir = increment_path(Path('ultralytics_rc_output') / 'exp', exist_ok) |
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save_dir.mkdir(parents=True, exist_ok=True) |
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video_writer = cv2.VideoWriter(str(save_dir / f'{Path(source).stem}.mp4'), fourcc, fps, (frame_width, frame_height)) |
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# Iterate over video frames |
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while videocapture.isOpened(): |
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success, frame = videocapture.read() |
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if not success: |
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break |
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vid_frame_count += 1 |
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# Extract the results |
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results = model.track(frame, persist=True) |
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boxes = results[0].boxes.xywh.cpu() |
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track_ids = results[0].boxes.id.int().cpu().tolist() |
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clss = results[0].boxes.cls.cpu().tolist() |
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names = results[0].names |
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annotator = Annotator(frame, line_width=line_thickness, example=str(names)) |
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for box, track_id, cls in zip(boxes, track_ids, clss): |
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x, y, w, h = box |
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label = str(names[cls]) |
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xyxy = (x - w / 2), (y - h / 2), (x + w / 2), (y + h / 2) |
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# Bounding box |
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bbox_color = colors(cls, True) |
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annotator.box_label(xyxy, label, color=bbox_color) |
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# Tracking Lines |
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track = track_history[track_id] |
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track.append((float(x), float(y))) |
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if len(track) > 30: |
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track.pop(0) |
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points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2)) |
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cv2.polylines(frame, [points], isClosed=False, color=bbox_color, thickness=line_thickness) |
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# Check If detection inside region |
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for region in counting_regions: |
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if is_inside_roi(box, region['roi']): |
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region['counts'] += 1 |
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# Draw region boxes |
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for region in counting_regions: |
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region_label = str(region['counts']) |
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roi_x, roi_y, roi_w, roi_h = region['roi'] |
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region_color = region['region_color'] |
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center_x = roi_x + roi_w // 2 |
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center_y = roi_y + roi_h // 2 |
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text_margin = 15 |
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# Region plotting |
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cv2.rectangle(frame, (roi_x, roi_y), (roi_x + roi_w, roi_y + roi_h), region_color, region_thickness) |
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t_size, _ = cv2.getTextSize(region_label, cv2.FONT_HERSHEY_SIMPLEX, fontScale=1.0, thickness=line_thickness) |
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text_x = center_x - t_size[0] // 2 - text_margin |
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text_y = center_y + t_size[1] // 2 + text_margin |
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cv2.rectangle(frame, (text_x - text_margin, text_y - t_size[1] - text_margin), |
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(text_x + t_size[0] + text_margin, text_y + text_margin), region_color, -1) |
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cv2.putText(frame, region_label, (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 0), line_thickness) |
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if view_img: |
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if vid_frame_count == 1: |
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cv2.namedWindow('Ultralytics YOLOv8 Region Counter Movable') |
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cv2.setMouseCallback('Ultralytics YOLOv8 Region Counter Movable', mouse_callback) |
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cv2.imshow('Ultralytics YOLOv8 Region Counter Movable', frame) |
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if save_img: |
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video_writer.write(frame) |
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for region in counting_regions: # Reinitialize count for each region |
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region['counts'] = 0 |
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if cv2.waitKey(1) & 0xFF == ord('q'): |
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break |
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del vid_frame_count |
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video_writer.release() |
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videocapture.release() |
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cv2.destroyAllWindows() |
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def parse_opt(): |
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"""Parse command line arguments.""" |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--weights', type=str, default='yolov8n.pt', help='initial weights path') |
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parser.add_argument('--source', type=str, required=True, help='video file path') |
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parser.add_argument('--view-img', action='store_true', help='show results') |
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parser.add_argument('--save-img', action='store_true', help='save results') |
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parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') |
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parser.add_argument('--line-thickness', type=int, default=2, help='bounding box thickness') |
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parser.add_argument('--region-thickness', type=int, default=4, help='Region thickness') |
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return parser.parse_args() |
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def main(opt): |
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"""Main function.""" |
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run(**vars(opt)) |
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if __name__ == '__main__': |
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opt = parse_opt() |
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main(opt) |
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