ultralytics 8.0.224 Counting and Heatmaps updates (#6855)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: Muhammad Rizwan Munawar <chr043416@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher
GitHub
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15 changed files with 225 additions and 145 deletions
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@ -35,12 +35,11 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
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from ultralytics.solutions import heatmap
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import cv2
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model = YOLO("yolov8s.pt")
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model = YOLO("yolov8s.pt") # YOLOv8 custom/pretrained model
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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if not cap.isOpened():
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print("Error reading video file")
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exit(0)
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assert cap.isOpened(), "Error reading video file"
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# Heatmap Init
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heatmap_obj = heatmap.Heatmap()
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heatmap_obj.set_args(colormap=cv2.COLORMAP_CIVIDIS,
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imw=cap.get(4), # should same as im0 width
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@ -52,7 +51,7 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
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if not success:
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exit(0)
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results = model.track(im0, persist=True)
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frame = heatmap_obj.generate_heatmap(im0, tracks=results)
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im0 = heatmap_obj.generate_heatmap(im0, tracks=results)
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```
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@ -62,14 +61,13 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
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from ultralytics.solutions import heatmap
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import cv2
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model = YOLO("yolov8s.pt")
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model = YOLO("yolov8s.pt") # YOLOv8 custom/pretrained model
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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if not cap.isOpened():
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print("Error reading video file")
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exit(0)
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assert cap.isOpened(), "Error reading video file"
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classes_for_heatmap = [0, 2]
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# Heatmap init
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heatmap_obj = heatmap.Heatmap()
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heatmap_obj.set_args(colormap=cv2.COLORMAP_CIVIDIS,
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imw=cap.get(4), # should same as im0 width
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@ -80,29 +78,28 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
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success, im0 = cap.read()
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if not success:
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exit(0)
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results = model.track(im0, persist=True,
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classes=classes_for_heatmap)
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frame = heatmap_obj.generate_heatmap(im0, tracks=results)
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results = model.track(im0, persist=True, classes=classes_for_heatmap)
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im0 = heatmap_obj.generate_heatmap(im0, tracks=results)
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```
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=== "Heatmap with Save Output"
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```python
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from ultralytics import YOLO
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import heatmap
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from ultralytics.solutions import heatmap
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import cv2
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model = YOLO("yolov8n.pt")
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model = YOLO("yolov8s.pt") # YOLOv8 custom/pretrained model
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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if not cap.isOpened():
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print("Error reading video file")
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exit(0)
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assert cap.isOpened(), "Error reading video file"
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# Video writer
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video_writer = cv2.VideoWriter("heatmap_output.avi",
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cv2.VideoWriter_fourcc(*'mp4v'),
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int(cap.get(5)),
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(int(cap.get(3)), int(cap.get(4))))
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# Heatmap init
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heatmap_obj = heatmap.Heatmap()
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heatmap_obj.set_args(colormap=cv2.COLORMAP_CIVIDIS,
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imw=cap.get(4), # should same as im0 width
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@ -113,22 +110,55 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
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success, im0 = cap.read()
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if not success:
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exit(0)
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results = model.track(im0, persist=True)
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frame = heatmap_obj.generate_heatmap(im0, tracks=results)
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results = model.track(im0, persist=True, classes=classes_for_heatmap)
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im0 = heatmap_obj.generate_heatmap(im0, tracks=results)
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video_writer.write(im0)
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video_writer.release()
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```
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=== "Heatmap with Object Counting"
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```python
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from ultralytics import YOLO
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from ultralytics.solutions import heatmap
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import cv2
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model = YOLO("yolov8s.pt") # YOLOv8 custom/pretrained model
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cap = cv2.VideoCapture("path/to/video/file.mp4") # Video file Path, webcam 0
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assert cap.isOpened(), "Error reading video file"
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# Region for object counting
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count_reg_pts = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
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# Heatmap Init
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heatmap_obj = heatmap.Heatmap()
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heatmap_obj.set_args(colormap=cv2.COLORMAP_JET,
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imw=cap.get(4), # should same as im0 width
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imh=cap.get(3), # should same as im0 height
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view_img=True,
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count_reg_pts=count_reg_pts)
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while cap.isOpened():
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success, im0 = cap.read()
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if not success:
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exit(0)
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results = model.track(im0, persist=True)
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im0 = heatmap_obj.generate_heatmap(im0, tracks=results)
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```
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### Arguments `set_args`
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| Name | Type | Default | Description |
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|---------------|----------------|---------|--------------------------------|
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| view_img | `bool` | `False` | Display the frame with heatmap |
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| colormap | `cv2.COLORMAP` | `None` | cv2.COLORMAP for heatmap |
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| imw | `int` | `None` | Width of Heatmap |
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| imh | `int` | `None` | Height of Heatmap |
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| heatmap_alpha | `float` | `0.5` | Heatmap alpha value |
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| Name | Type | Default | Description |
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|---------------------|----------------|-----------------|---------------------------------|
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| view_img | `bool` | `False` | Display the frame with heatmap |
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| colormap | `cv2.COLORMAP` | `None` | cv2.COLORMAP for heatmap |
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| imw | `int` | `None` | Width of Heatmap |
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| imh | `int` | `None` | Height of Heatmap |
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| heatmap_alpha | `float` | `0.5` | Heatmap alpha value |
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| count_reg_pts | `list` | `None` | Object counting region points |
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| count_txt_thickness | `int` | `2` | Count values text size |
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| count_reg_color | `tuple` | `(255, 0, 255)` | Counting region color |
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| region_thickness | `int` | `5` | Counting region thickness value |
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### Arguments `model.track`
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@ -140,3 +170,32 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
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| `conf` | `float` | `0.3` | Confidence Threshold |
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| `iou` | `float` | `0.5` | IOU Threshold |
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| `classes` | `list` | `None` | filter results by class, i.e. classes=0, or classes=[0,2,3] |
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### Heatmap COLORMAPs
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| Colormap Name | Description |
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|---------------------------------|----------------------------------------|
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| `cv::COLORMAP_AUTUMN` | Autumn color map |
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| `cv::COLORMAP_BONE` | Bone color map |
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| `cv::COLORMAP_JET` | Jet color map |
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| `cv::COLORMAP_WINTER` | Winter color map |
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| `cv::COLORMAP_RAINBOW` | Rainbow color map |
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| `cv::COLORMAP_OCEAN` | Ocean color map |
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| `cv::COLORMAP_SUMMER` | Summer color map |
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| `cv::COLORMAP_SPRING` | Spring color map |
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| `cv::COLORMAP_COOL` | Cool color map |
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| `cv::COLORMAP_HSV` | HSV (Hue, Saturation, Value) color map |
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| `cv::COLORMAP_PINK` | Pink color map |
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| `cv::COLORMAP_HOT` | Hot color map |
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| `cv::COLORMAP_PARULA` | Parula color map |
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| `cv::COLORMAP_MAGMA` | Magma color map |
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| `cv::COLORMAP_INFERNO` | Inferno color map |
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| `cv::COLORMAP_PLASMA` | Plasma color map |
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| `cv::COLORMAP_VIRIDIS` | Viridis color map |
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| `cv::COLORMAP_CIVIDIS` | Cividis color map |
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| `cv::COLORMAP_TWILIGHT` | Twilight color map |
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| `cv::COLORMAP_TWILIGHT_SHIFTED` | Shifted Twilight color map |
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| `cv::COLORMAP_TURBO` | Turbo color map |
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| `cv::COLORMAP_DEEPGREEN` | Deep Green color map |
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These colormaps are commonly used for visualizing data with different color representations.
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@ -23,7 +23,6 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
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|  |  |
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| Conveyor Belt Packets Counting Using Ultralytics YOLOv8 | Fish Counting in Sea using Ultralytics YOLOv8 |
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!!! Example "Object Counting Example"
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=== "Object Counting"
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@ -34,9 +33,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
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model = YOLO("yolov8n.pt")
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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if not cap.isOpened():
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print("Error reading video file")
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exit(0)
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assert cap.isOpened(), "Error reading video file"
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counter = object_counter.ObjectCounter() # Init Object Counter
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region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
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@ -61,9 +58,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
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model = YOLO("yolov8n.pt")
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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if not cap.isOpened():
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print("Error reading video file")
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exit(0)
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assert cap.isOpened(), "Error reading video file"
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classes_to_count = [0, 2]
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counter = object_counter.ObjectCounter() # Init Object Counter
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@ -91,9 +86,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
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model = YOLO("yolov8n.pt")
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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if not cap.isOpened():
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print("Error reading video file")
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exit(0)
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assert cap.isOpened(), "Error reading video file"
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video_writer = cv2.VideoWriter("object_counting.avi",
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cv2.VideoWriter_fourcc(*'mp4v'),
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@ -134,7 +127,6 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
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| track_thickness | `int` | `2` | Tracking line thickness |
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| draw_tracks | `bool` | `False` | Draw Tracks lines |
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### Arguments `model.track`
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| Name | Type | Default | Description |
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@ -23,7 +23,6 @@ Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://gi
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|  |  |
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| PushUps Counting | PullUps Counting |
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!!! Example "Workouts Monitoring Example"
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=== "Workouts Monitoring"
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@ -34,9 +33,7 @@ Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://gi
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model = YOLO("yolov8n-pose.pt")
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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if not cap.isOpened():
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print("Error reading video file")
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exit(0)
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assert cap.isOpened(), "Error reading video file"
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gym_object = ai_gym.AIGym() # init AI GYM module
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gym_object.set_args(line_thickness=2,
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@ -62,9 +59,7 @@ Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://gi
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model = YOLO("yolov8n-pose.pt")
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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if not cap.isOpened():
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print("Error reading video file")
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exit(0)
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assert cap.isOpened(), "Error reading video file"
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video_writer = cv2.VideoWriter("workouts.avi",
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cv2.VideoWriter_fourcc(*'mp4v'),
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@ -90,7 +90,6 @@ This will capture:
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- Mosaic per epoch
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- Validation images per epoch
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That's a lot right? 🤯 Now, we can visualize all of this information in the ClearML UI to get an overview of our training progress. Add custom columns to the table view (such as e.g. mAP_0.5) so you can easily sort on the best performing model. Or select multiple experiments and directly compare them!
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There even more we can do with all of this information, like hyperparameter optimization and remote execution, so keep reading if you want to see how that works!
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