--- comments: true description: Transform complex data into insightful heatmaps using Ultralytics YOLOv8. Discover patterns, trends, and anomalies with vibrant visualizations. keywords: Ultralytics, YOLOv8, heatmaps, data visualization, data analysis, complex data, patterns, trends, anomalies --- # Advanced Data Visualization: Heatmaps using Ultralytics YOLOv8 🚀 ## Introduction to Heatmaps A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) transforms complex data into a vibrant, color-coded matrix. This visual tool employs a spectrum of colors to represent varying data values, where warmer hues indicate higher intensities and cooler tones signify lower values. Heatmaps excel in visualizing intricate data patterns, correlations, and anomalies, offering an accessible and engaging approach to data interpretation across diverse domains.



Watch: Heatmaps using Ultralytics YOLOv8

## Why Choose Heatmaps for Data Analysis? - **Intuitive Data Distribution Visualization:** Heatmaps simplify the comprehension of data concentration and distribution, converting complex datasets into easy-to-understand visual formats. - **Efficient Pattern Detection:** By visualizing data in heatmap format, it becomes easier to spot trends, clusters, and outliers, facilitating quicker analysis and insights. - **Enhanced Spatial Analysis and Decision-Making:** Heatmaps are instrumental in illustrating spatial relationships, aiding in decision-making processes in sectors such as business intelligence, environmental studies, and urban planning. ## Real World Applications | Transportation | Retail | |:-----------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------:| | ![Ultralytics YOLOv8 Transportation Heatmap](https://github.com/RizwanMunawar/ultralytics/assets/62513924/288d7053-622b-4452-b4e4-1f41aeb764aa) | ![Ultralytics YOLOv8 Retail Heatmap](https://github.com/RizwanMunawar/ultralytics/assets/62513924/edef75ad-50a7-4c0a-be4a-a66cdfc12802) | | Ultralytics YOLOv8 Transportation Heatmap | Ultralytics YOLOv8 Retail Heatmap | !!! tip "Heatmap Configuration" - `heatmap_alpha`: Ensure this value is within the range (0.0 - 1.0). - `decay_factor`: Used for removing heatmap after an object is no longer in the frame, its value should also be in the range (0.0 - 1.0). !!! Example "Heatmaps using Ultralytics YOLOv8 Example" === "Heatmap" ```python import cv2 from ultralytics import YOLO, solutions model = YOLO("yolov8n.pt") cap = cv2.VideoCapture("path/to/video/file.mp4") assert cap.isOpened(), "Error reading video file" w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS)) # Video writer video_writer = cv2.VideoWriter("heatmap_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)) # Init heatmap heatmap_obj = solutions.Heatmap( colormap=cv2.COLORMAP_PARULA, view_img=True, shape="circle", classes_names=model.names, ) while cap.isOpened(): success, im0 = cap.read() if not success: print("Video frame is empty or video processing has been successfully completed.") break tracks = model.track(im0, persist=True, show=False) im0 = heatmap_obj.generate_heatmap(im0, tracks) video_writer.write(im0) cap.release() video_writer.release() cv2.destroyAllWindows() ``` === "Line Counting" ```python import cv2 from ultralytics import YOLO, solutions model = YOLO("yolov8n.pt") cap = cv2.VideoCapture("path/to/video/file.mp4") assert cap.isOpened(), "Error reading video file" w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS)) # Video writer video_writer = cv2.VideoWriter("heatmap_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)) line_points = [(20, 400), (1080, 404)] # line for object counting # Init heatmap heatmap_obj = solutions.Heatmap( colormap=cv2.COLORMAP_PARULA, view_img=True, shape="circle", count_reg_pts=line_points, classes_names=model.names, ) while cap.isOpened(): success, im0 = cap.read() if not success: print("Video frame is empty or video processing has been successfully completed.") break tracks = model.track(im0, persist=True, show=False) im0 = heatmap_obj.generate_heatmap(im0, tracks) video_writer.write(im0) cap.release() video_writer.release() cv2.destroyAllWindows() ``` === "Polygon Counting" ```python import cv2 from ultralytics import YOLO, solutions model = YOLO("yolov8n.pt") cap = cv2.VideoCapture("path/to/video/file.mp4") assert cap.isOpened(), "Error reading video file" w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS)) # Video writer video_writer = cv2.VideoWriter("heatmap_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)) # Define polygon points region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360), (20, 400)] # Init heatmap heatmap_obj = solutions.Heatmap( colormap=cv2.COLORMAP_PARULA, view_img=True, shape="circle", count_reg_pts=region_points, classes_names=model.names, ) while cap.isOpened(): success, im0 = cap.read() if not success: print("Video frame is empty or video processing has been successfully completed.") break tracks = model.track(im0, persist=True, show=False) im0 = heatmap_obj.generate_heatmap(im0, tracks) video_writer.write(im0) cap.release() video_writer.release() cv2.destroyAllWindows() ``` === "Region Counting" ```python import cv2 from ultralytics import YOLO, solutions model = YOLO("yolov8n.pt") cap = cv2.VideoCapture("path/to/video/file.mp4") assert cap.isOpened(), "Error reading video file" w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS)) # Video writer video_writer = cv2.VideoWriter("heatmap_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)) # Define region points region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)] # Init heatmap heatmap_obj = solutions.Heatmap( colormap=cv2.COLORMAP_PARULA, view_img=True, shape="circle", count_reg_pts=region_points, classes_names=model.names, ) while cap.isOpened(): success, im0 = cap.read() if not success: print("Video frame is empty or video processing has been successfully completed.") break tracks = model.track(im0, persist=True, show=False) im0 = heatmap_obj.generate_heatmap(im0, tracks) video_writer.write(im0) cap.release() video_writer.release() cv2.destroyAllWindows() ``` === "Im0" ```python import cv2 from ultralytics import YOLO, solutions model = YOLO("yolov8s.pt") # YOLOv8 custom/pretrained model im0 = cv2.imread("path/to/image.png") # path to image file h, w = im0.shape[:2] # image height and width # Heatmap Init heatmap_obj = solutions.Heatmap( colormap=cv2.COLORMAP_PARULA, view_img=True, shape="circle", classes_names=model.names, ) results = model.track(im0, persist=True) im0 = heatmap_obj.generate_heatmap(im0, tracks=results) cv2.imwrite("ultralytics_output.png", im0) ``` === "Specific Classes" ```python import cv2 from ultralytics import YOLO, solutions model = YOLO("yolov8n.pt") cap = cv2.VideoCapture("path/to/video/file.mp4") assert cap.isOpened(), "Error reading video file" w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS)) # Video writer video_writer = cv2.VideoWriter("heatmap_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)) classes_for_heatmap = [0, 2] # classes for heatmap # Init heatmap heatmap_obj = solutions.Heatmap( colormap=cv2.COLORMAP_PARULA, view_img=True, shape="circle", classes_names=model.names, ) while cap.isOpened(): success, im0 = cap.read() if not success: print("Video frame is empty or video processing has been successfully completed.") break tracks = model.track(im0, persist=True, show=False, classes=classes_for_heatmap) im0 = heatmap_obj.generate_heatmap(im0, tracks) video_writer.write(im0) cap.release() video_writer.release() cv2.destroyAllWindows() ``` ### Arguments `Heatmap()` | Name | Type | Default | Description | |--------------------|------------------|--------------------|-------------------------------------------------------------------| | `classes_names` | `dict` | `None` | Dictionary of class names. | | `imw` | `int` | `0` | Image width. | | `imh` | `int` | `0` | Image height. | | `colormap` | `int` | `cv2.COLORMAP_JET` | Colormap to use for the heatmap. | | `heatmap_alpha` | `float` | `0.5` | Alpha blending value for heatmap overlay. | | `view_img` | `bool` | `False` | Whether to display the image with the heatmap overlay. | | `view_in_counts` | `bool` | `True` | Whether to display the count of objects entering the region. | | `view_out_counts` | `bool` | `True` | Whether to display the count of objects exiting the region. | | `count_reg_pts` | `list` or `None` | `None` | Points defining the counting region (either a line or a polygon). | | `count_txt_color` | `tuple` | `(0, 0, 0)` | Text color for displaying counts. | | `count_bg_color` | `tuple` | `(255, 255, 255)` | Background color for displaying counts. | | `count_reg_color` | `tuple` | `(255, 0, 255)` | Color for the counting region. | | `region_thickness` | `int` | `5` | Thickness of the region line. | | `line_dist_thresh` | `int` | `15` | Distance threshold for line-based counting. | | `line_thickness` | `int` | `2` | Thickness of the lines used in drawing. | | `decay_factor` | `float` | `0.99` | Decay factor for the heatmap to reduce intensity over time. | | `shape` | `str` | `"circle"` | Shape of the heatmap blobs ('circle' or 'rect'). | ### Arguments `model.track` | Name | Type | Default | Description | |-----------|---------|----------------|-------------------------------------------------------------| | `source` | `im0` | `None` | source directory for images or videos | | `persist` | `bool` | `False` | persisting tracks between frames | | `tracker` | `str` | `botsort.yaml` | Tracking method 'bytetrack' or 'botsort' | | `conf` | `float` | `0.3` | Confidence Threshold | | `iou` | `float` | `0.5` | IOU Threshold | | `classes` | `list` | `None` | filter results by class, i.e. classes=0, or classes=[0,2,3] | ### Heatmap COLORMAPs | Colormap Name | Description | |---------------------------------|----------------------------------------| | `cv::COLORMAP_AUTUMN` | Autumn color map | | `cv::COLORMAP_BONE` | Bone color map | | `cv::COLORMAP_JET` | Jet color map | | `cv::COLORMAP_WINTER` | Winter color map | | `cv::COLORMAP_RAINBOW` | Rainbow color map | | `cv::COLORMAP_OCEAN` | Ocean color map | | `cv::COLORMAP_SUMMER` | Summer color map | | `cv::COLORMAP_SPRING` | Spring color map | | `cv::COLORMAP_COOL` | Cool color map | | `cv::COLORMAP_HSV` | HSV (Hue, Saturation, Value) color map | | `cv::COLORMAP_PINK` | Pink color map | | `cv::COLORMAP_HOT` | Hot color map | | `cv::COLORMAP_PARULA` | Parula color map | | `cv::COLORMAP_MAGMA` | Magma color map | | `cv::COLORMAP_INFERNO` | Inferno color map | | `cv::COLORMAP_PLASMA` | Plasma color map | | `cv::COLORMAP_VIRIDIS` | Viridis color map | | `cv::COLORMAP_CIVIDIS` | Cividis color map | | `cv::COLORMAP_TWILIGHT` | Twilight color map | | `cv::COLORMAP_TWILIGHT_SHIFTED` | Shifted Twilight color map | | `cv::COLORMAP_TURBO` | Turbo color map | | `cv::COLORMAP_DEEPGREEN` | Deep Green color map | These colormaps are commonly used for visualizing data with different color representations.