mirror of
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Add line counting and circular heatmaps in Ultralytics Solutions (#7113)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Muhammad Rizwan Munawar
GitHub
parent
a5735724c5
commit
38eaf5e29f
@ -20,14 +20,19 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
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| Transportation | Retail |
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|:-----------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------:|
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|  |  |
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|  |  |
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| Ultralytics YOLOv8 Transportation Heatmap | Ultralytics YOLOv8 Retail Heatmap |
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???+ tip "heatmap_alpha"
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heatmap_alpha value should be in range (0.0 - 1.0)
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!!! Example "Heatmap Example"
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???+ tip "decay_factor"
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Used for removal of heatmap after object removed from frame, value should be in range (0.0 - 1.0)
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!!! Example "Heatmaps using Ultralytics YOLOv8 Example"
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=== "Heatmap"
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```python
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@ -35,31 +40,126 @@ 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") # YOLOv8 custom/pretrained model
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model = YOLO("yolov8n.pt")
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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assert cap.isOpened(), "Error reading video file"
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# Heatmap Init
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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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# Init heatmap
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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 cap width
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imh=cap.get(3), # should same as cap height
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heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA ,
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imw=cap.get(4), # should same as cap height
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imh=cap.get(3), # should same as cap width
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view_img=True,
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decay_factor=0.99)
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shape="circle")
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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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print("Video frame is empty or video processing has been successfully completed.")
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break
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tracks = model.track(im0, persist=True, show=False)
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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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im0 = heatmap_obj.generate_heatmap(im0, tracks)
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video_writer.write(im0)
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cap.release()
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video_writer.release()
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cv2.destroyAllWindows()
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```
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=== "Line 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("yolov8n.pt")
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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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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line_points = [(256, 409), (694, 532)] # line for object counting
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# Init heatmap
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heatmap_obj = heatmap.Heatmap()
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heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA ,
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imw=cap.get(4), # should same as cap height
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imh=cap.get(3), # should same as cap width
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view_img=True,
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shape="circle",
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count_reg_pts=line_points)
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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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print("Video frame is empty or video processing has been successfully completed.")
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break
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tracks = model.track(im0, persist=True, show=False)
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im0 = heatmap_obj.generate_heatmap(im0, tracks)
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video_writer.write(im0)
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cap.release()
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video_writer.release()
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cv2.destroyAllWindows()
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```
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=== "Heatmap with im0"
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=== "Region 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("yolov8n.pt")
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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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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# Define region points
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region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
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# Init heatmap
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heatmap_obj = heatmap.Heatmap()
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heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA ,
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imw=cap.get(4), # should same as cap height
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imh=cap.get(3), # should same as cap width
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view_img=True,
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shape="circle",
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count_reg_pts=region_points)
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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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print("Video frame is empty or video processing has been successfully completed.")
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break
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tracks = model.track(im0, persist=True, show=False)
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im0 = heatmap_obj.generate_heatmap(im0, tracks)
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video_writer.write(im0)
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cap.release()
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video_writer.release()
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cv2.destroyAllWindows()
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```
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=== "Im0"
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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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@ -71,10 +171,11 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
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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=im0.shape[0], # should same as im0 width
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imh=im0.shape[1], # should same as im0 height
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view_img=True)
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heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA ,
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imw=cap.get(4), # should same as cap height
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imh=cap.get(3), # should same as cap width
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view_img=True,
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shape="circle")
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results = model.track(im0, persist=True)
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@ -82,43 +183,13 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
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cv2.imwrite("ultralytics_output.png", im0)
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```
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=== "Heatmap with Specific Classes"
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=== "Specific Classes"
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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")
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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 cap width
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imh=cap.get(3), # should same as cap height
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view_img=True)
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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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print("Video frame is empty or video processing has been successfully completed.")
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break
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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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cv2.destroyAllWindows()
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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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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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model = YOLO("yolov8n.pt")
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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assert cap.isOpened(), "Error reading video file"
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@ -128,64 +199,36 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
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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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classes_for_heatmap = [0, 2] # classes for heatmap
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# Init heatmap
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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 cap width
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imh=cap.get(3), # should same as cap height
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view_img=True)
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heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA ,
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imw=cap.get(4), # should same as cap height
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imh=cap.get(3), # should same as cap width
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view_img=True,
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shape="circle")
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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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print("Video frame is empty or video processing has been successfully completed.")
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break
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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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tracks = model.track(im0, persist=True, show=False,
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classes=classes_for_heatmap)
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im0 = heatmap_obj.generate_heatmap(im0, tracks)
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video_writer.write(im0)
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cap.release()
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video_writer.release()
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cv2.destroyAllWindows()
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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 cap width
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imh=cap.get(3), # should same as cap 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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print("Video frame is empty or video processing has been successfully completed.")
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break
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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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cv2.destroyAllWindows()
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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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|---------------------|----------------|-------------------|-----------------------------------------------------------|
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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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@ -193,9 +236,13 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
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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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| count_txt_color | `RGB Color` | `(0, 0, 0)` | Foreground color for Object counts text |
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| count_color | `RGB Color` | `(255, 255, 255)` | Background color for Object counts text |
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| count_reg_color | `RGB Color` | `(255, 0, 255)` | Counting region color |
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| region_thickness | `int` | `5` | Counting region thickness value |
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| decay_factor | `float` | `0.99` | Decay factor for heatmap area removal after specific time |
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| shape | `str` | `circle` | Heatmap shape for display "rect" or "circle" supported |
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| line_dist_thresh | `int` | `15` | Euclidean Distance threshold for line counter |
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### Arguments `model.track`
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@ -34,9 +34,9 @@ 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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!!! Example "Object Counting using YOLOv8 Example"
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=== "Object Counting"
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=== "Region"
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```python
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from ultralytics import YOLO
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from ultralytics.solutions import object_counter
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@ -46,8 +46,17 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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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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# Define region points
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region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
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# Video writer
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video_writer = cv2.VideoWriter("object_counting_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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# Init Object Counter
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counter = object_counter.ObjectCounter()
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counter.set_args(view_img=True,
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reg_pts=region_points,
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classes_names=model.names,
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@ -59,12 +68,17 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
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print("Video frame is empty or video processing has been successfully completed.")
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break
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tracks = model.track(im0, persist=True, show=False)
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im0 = counter.start_counting(im0, tracks)
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im0 = counter.start_counting(im0, tracks)
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video_writer.write(im0)
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cap.release()
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video_writer.release()
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cv2.destroyAllWindows()
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```
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=== "Object Counting with Specific Classes"
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=== "Line"
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```python
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from ultralytics import YOLO
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from ultralytics.solutions import object_counter
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@ -74,11 +88,60 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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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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region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
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# Define line points
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line_points = [(20, 400), (1080, 400)]
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# Video writer
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video_writer = cv2.VideoWriter("object_counting_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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# Init Object Counter
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counter = object_counter.ObjectCounter()
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counter.set_args(view_img=True,
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reg_pts=region_points,
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reg_pts=line_points,
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classes_names=model.names,
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draw_tracks=True)
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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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print("Video frame is empty or video processing has been successfully completed.")
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break
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tracks = model.track(im0, persist=True, show=False)
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im0 = counter.start_counting(im0, tracks)
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video_writer.write(im0)
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cap.release()
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video_writer.release()
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cv2.destroyAllWindows()
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```
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=== "Specific Classes"
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```python
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from ultralytics import YOLO
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from ultralytics.solutions import object_counter
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import cv2
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model = YOLO("yolov8n.pt")
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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assert cap.isOpened(), "Error reading video file"
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line_points = [(20, 400), (1080, 400)] # line or region points
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classes_to_count = [0, 2] # person and car classes for count
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# Video writer
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video_writer = cv2.VideoWriter("object_counting_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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# Init Object Counter
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counter = object_counter.ObjectCounter()
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counter.set_args(view_img=True,
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reg_pts=line_points,
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classes_names=model.names,
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draw_tracks=True)
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@ -89,42 +152,11 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
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break
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tracks = model.track(im0, persist=True, show=False,
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classes=classes_to_count)
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im0 = counter.start_counting(im0, tracks)
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cv2.destroyAllWindows()
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```
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=== "Object Counting with Save Output"
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```python
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from ultralytics import YOLO
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from ultralytics.solutions import object_counter
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import cv2
|
||||
|
||||
model = YOLO("yolov8n.pt")
|
||||
cap = cv2.VideoCapture("path/to/video/file.mp4")
|
||||
assert cap.isOpened(), "Error reading video file"
|
||||
|
||||
video_writer = cv2.VideoWriter("object_counting.avi",
|
||||
cv2.VideoWriter_fourcc(*'mp4v'),
|
||||
int(cap.get(5)),
|
||||
(int(cap.get(3)), int(cap.get(4))))
|
||||
|
||||
counter = object_counter.ObjectCounter() # Init Object Counter
|
||||
region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
|
||||
counter.set_args(view_img=True,
|
||||
reg_pts=region_points,
|
||||
classes_names=model.names,
|
||||
draw_tracks=True)
|
||||
|
||||
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 = counter.start_counting(im0, tracks)
|
||||
video_writer.write(im0)
|
||||
|
||||
cap.release()
|
||||
video_writer.release()
|
||||
cv2.destroyAllWindows()
|
||||
```
|
||||
@ -135,15 +167,22 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
|
||||
|
||||
### Optional Arguments `set_args`
|
||||
|
||||
|
||||
| Name | Type | Default | Description |
|
||||
|-----------------|---------|--------------------------------------------------|---------------------------------------|
|
||||
| view_img | `bool` | `False` | Display the frame with counts |
|
||||
| line_thickness | `int` | `2` | Increase the thickness of count value |
|
||||
| reg_pts | `list` | `(20, 400), (1080, 404), (1080, 360), (20, 360)` | Region Area Points |
|
||||
| classes_names | `dict` | `model.model.names` | Classes Names Dict |
|
||||
| region_color | `tuple` | `(0, 255, 0)` | Region Area Color |
|
||||
| track_thickness | `int` | `2` | Tracking line thickness |
|
||||
| draw_tracks | `bool` | `False` | Draw Tracks lines |
|
||||
|---------------------|-------------|----------------------------|-----------------------------------------------|
|
||||
| view_img | `bool` | `False` | Display frames with counts |
|
||||
| line_thickness | `int` | `2` | Increase bounding boxes thickness |
|
||||
| reg_pts | `list` | `[(20, 400), (1260, 400)]` | Points defining the Region Area |
|
||||
| classes_names | `dict` | `model.model.names` | Dictionary of Class Names |
|
||||
| region_color | `RGB Color` | `(255, 0, 255)` | Color of the Object counting Region or Line |
|
||||
| track_thickness | `int` | `2` | Thickness of Tracking Lines |
|
||||
| draw_tracks | `bool` | `False` | Enable drawing Track lines |
|
||||
| track_color | `RGB Color` | `(0, 255, 0)` | Color for each track line |
|
||||
| line_dist_thresh | `int` | `15` | Euclidean Distance threshold for line counter |
|
||||
| count_txt_thickness | `int` | `2` | Thickness of Object counts text |
|
||||
| count_txt_color | `RGB Color` | `(0, 0, 0)` | Foreground color for Object counts text |
|
||||
| count_color | `RGB Color` | `(255, 255, 255)` | Background color for Object counts text |
|
||||
| region_thickness | `int` | `5` | Thickness for object counter region or line |
|
||||
|
||||
### Arguments `model.track`
|
||||
|
||||
@ -155,3 +194,4 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
|
||||
| `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] |
|
||||
| `verbose` | `bool` | `True` | Display the object tracking results |
|
||||
|
||||
@ -10,8 +10,7 @@ from ultralytics.utils.plotting import Annotator
|
||||
|
||||
check_requirements('shapely>=2.0.0')
|
||||
|
||||
from shapely.geometry import Polygon
|
||||
from shapely.geometry.point import Point
|
||||
from shapely.geometry import LineString, Point, Polygon
|
||||
|
||||
|
||||
class Heatmap:
|
||||
@ -23,6 +22,7 @@ class Heatmap:
|
||||
# Visual information
|
||||
self.annotator = None
|
||||
self.view_img = False
|
||||
self.shape = 'circle'
|
||||
|
||||
# Image information
|
||||
self.imw = None
|
||||
@ -38,17 +38,22 @@ class Heatmap:
|
||||
self.boxes = None
|
||||
self.track_ids = None
|
||||
self.clss = None
|
||||
self.track_history = None
|
||||
self.track_history = defaultdict(list)
|
||||
|
||||
# Counting info
|
||||
# Region & Line Information
|
||||
self.count_reg_pts = None
|
||||
self.count_region = None
|
||||
self.counting_region = None
|
||||
self.line_dist_thresh = 15
|
||||
self.region_thickness = 5
|
||||
self.region_color = (255, 0, 255)
|
||||
|
||||
# Object Counting Information
|
||||
self.in_counts = 0
|
||||
self.out_counts = 0
|
||||
self.count_list = []
|
||||
self.counting_list = []
|
||||
self.count_txt_thickness = 0
|
||||
self.count_reg_color = (0, 255, 0)
|
||||
self.region_thickness = 5
|
||||
self.count_txt_color = (0, 0, 0)
|
||||
self.count_color = (255, 255, 255)
|
||||
|
||||
# Decay factor
|
||||
self.decay_factor = 0.99
|
||||
@ -64,9 +69,13 @@ class Heatmap:
|
||||
view_img=False,
|
||||
count_reg_pts=None,
|
||||
count_txt_thickness=2,
|
||||
count_txt_color=(0, 0, 0),
|
||||
count_color=(255, 255, 255),
|
||||
count_reg_color=(255, 0, 255),
|
||||
region_thickness=5,
|
||||
decay_factor=0.99):
|
||||
line_dist_thresh=15,
|
||||
decay_factor=0.99,
|
||||
shape='circle'):
|
||||
"""
|
||||
Configures the heatmap colormap, width, height and display parameters.
|
||||
|
||||
@ -78,27 +87,55 @@ class Heatmap:
|
||||
view_img (bool): Flag indicating frame display
|
||||
count_reg_pts (list): Object counting region points
|
||||
count_txt_thickness (int): Text thickness for object counting display
|
||||
count_txt_color (RGB color): count text color value
|
||||
count_color (RGB color): count text background color value
|
||||
count_reg_color (RGB color): Color of object counting region
|
||||
region_thickness (int): Object counting Region thickness
|
||||
line_dist_thresh (int): Euclidean Distance threshold for line counter
|
||||
decay_factor (float): value for removing heatmap area after object passed
|
||||
shape (str): Heatmap shape, rect or circle shape supported
|
||||
"""
|
||||
self.imw = imw
|
||||
self.imh = imh
|
||||
self.colormap = colormap
|
||||
self.heatmap_alpha = heatmap_alpha
|
||||
self.view_img = view_img
|
||||
self.colormap = colormap
|
||||
|
||||
self.heatmap = np.zeros((int(self.imw), int(self.imh)), dtype=np.float32) # Heatmap new frame
|
||||
|
||||
# Region and line selection
|
||||
if count_reg_pts is not None:
|
||||
self.track_history = defaultdict(list)
|
||||
self.count_reg_pts = count_reg_pts
|
||||
self.count_region = Polygon(self.count_reg_pts)
|
||||
|
||||
self.count_txt_thickness = count_txt_thickness # Counting text thickness
|
||||
self.count_reg_color = count_reg_color
|
||||
if len(count_reg_pts) == 2:
|
||||
print('Line Counter Initiated.')
|
||||
self.count_reg_pts = count_reg_pts
|
||||
self.counting_region = LineString(count_reg_pts)
|
||||
|
||||
elif len(count_reg_pts) == 4:
|
||||
print('Region Counter Initiated.')
|
||||
self.count_reg_pts = count_reg_pts
|
||||
self.counting_region = Polygon(self.count_reg_pts)
|
||||
|
||||
else:
|
||||
print('Region or line points Invalid, 2 or 4 points supported')
|
||||
print('Using Line Counter Now')
|
||||
self.counting_region = Polygon([(20, 400), (1260, 400)]) # dummy points
|
||||
|
||||
# Heatmap new frame
|
||||
self.heatmap = np.zeros((int(self.imw), int(self.imh)), dtype=np.float32)
|
||||
|
||||
self.count_txt_thickness = count_txt_thickness
|
||||
self.count_txt_color = count_txt_color
|
||||
self.count_color = count_color
|
||||
self.region_color = count_reg_color
|
||||
self.region_thickness = region_thickness
|
||||
self.decay_factor = decay_factor
|
||||
self.line_dist_thresh = line_dist_thresh
|
||||
self.shape = shape
|
||||
|
||||
# shape of heatmap, if not selected
|
||||
if self.shape not in ['circle', 'rect']:
|
||||
print("Unknown shape value provided, 'circle' & 'rect' supported")
|
||||
print('Using Circular shape now')
|
||||
self.shape = 'circle'
|
||||
|
||||
def extract_results(self, tracks):
|
||||
"""
|
||||
@ -128,13 +165,26 @@ class Heatmap:
|
||||
self.annotator = Annotator(self.im0, self.count_txt_thickness, None)
|
||||
|
||||
if self.count_reg_pts is not None:
|
||||
|
||||
# Draw counting region
|
||||
self.annotator.draw_region(reg_pts=self.count_reg_pts,
|
||||
color=self.count_reg_color,
|
||||
color=self.region_color,
|
||||
thickness=self.region_thickness)
|
||||
|
||||
for box, cls, track_id in zip(self.boxes, self.clss, self.track_ids):
|
||||
self.heatmap[int(box[1]):int(box[3]), int(box[0]):int(box[2])] += 1
|
||||
|
||||
if self.shape == 'circle':
|
||||
center = (int((box[0] + box[2]) // 2), int((box[1] + box[3]) // 2))
|
||||
radius = min(int(box[2]) - int(box[0]), int(box[3]) - int(box[1])) // 2
|
||||
|
||||
y, x = np.ogrid[0:self.heatmap.shape[0], 0:self.heatmap.shape[1]]
|
||||
mask = (x - center[0]) ** 2 + (y - center[1]) ** 2 <= radius ** 2
|
||||
|
||||
self.heatmap[int(box[1]):int(box[3]), int(box[0]):int(box[2])] += \
|
||||
(2 * mask[int(box[1]):int(box[3]), int(box[0]):int(box[2])])
|
||||
|
||||
else:
|
||||
self.heatmap[int(box[1]):int(box[3]), int(box[0]):int(box[2])] += 2
|
||||
|
||||
# Store tracking hist
|
||||
track_line = self.track_history[track_id]
|
||||
@ -143,16 +193,39 @@ class Heatmap:
|
||||
track_line.pop(0)
|
||||
|
||||
# Count objects
|
||||
if self.count_region.contains(Point(track_line[-1])):
|
||||
if track_id not in self.count_list:
|
||||
self.count_list.append(track_id)
|
||||
if box[0] < self.count_region.centroid.x:
|
||||
if len(self.count_reg_pts) == 4:
|
||||
if self.counting_region.contains(Point(track_line[-1])):
|
||||
if track_id not in self.counting_list:
|
||||
self.counting_list.append(track_id)
|
||||
if box[0] < self.counting_region.centroid.x:
|
||||
self.out_counts += 1
|
||||
else:
|
||||
self.in_counts += 1
|
||||
|
||||
elif len(self.count_reg_pts) == 2:
|
||||
distance = Point(track_line[-1]).distance(self.counting_region)
|
||||
if distance < self.line_dist_thresh:
|
||||
if track_id not in self.counting_list:
|
||||
self.counting_list.append(track_id)
|
||||
if box[0] < self.counting_region.centroid.x:
|
||||
self.out_counts += 1
|
||||
else:
|
||||
self.in_counts += 1
|
||||
else:
|
||||
for box, cls in zip(self.boxes, self.clss):
|
||||
self.heatmap[int(box[1]):int(box[3]), int(box[0]):int(box[2])] += 1
|
||||
|
||||
if self.shape == 'circle':
|
||||
center = (int((box[0] + box[2]) // 2), int((box[1] + box[3]) // 2))
|
||||
radius = min(int(box[2]) - int(box[0]), int(box[3]) - int(box[1])) // 2
|
||||
|
||||
y, x = np.ogrid[0:self.heatmap.shape[0], 0:self.heatmap.shape[1]]
|
||||
mask = (x - center[0]) ** 2 + (y - center[1]) ** 2 <= radius ** 2
|
||||
|
||||
self.heatmap[int(box[1]):int(box[3]), int(box[0]):int(box[2])] += \
|
||||
(2 * mask[int(box[1]):int(box[3]), int(box[0]):int(box[2])])
|
||||
|
||||
else:
|
||||
self.heatmap[int(box[1]):int(box[3]), int(box[0]):int(box[2])] += 2
|
||||
|
||||
# Normalize, apply colormap to heatmap and combine with original image
|
||||
heatmap_normalized = cv2.normalize(self.heatmap, None, 0, 255, cv2.NORM_MINMAX)
|
||||
@ -161,7 +234,11 @@ class Heatmap:
|
||||
if self.count_reg_pts is not None:
|
||||
incount_label = 'InCount : ' + f'{self.in_counts}'
|
||||
outcount_label = 'OutCount : ' + f'{self.out_counts}'
|
||||
self.annotator.count_labels(in_count=incount_label, out_count=outcount_label)
|
||||
self.annotator.count_labels(in_count=incount_label,
|
||||
out_count=outcount_label,
|
||||
count_txt_size=self.count_txt_thickness,
|
||||
txt_color=self.count_txt_color,
|
||||
color=self.count_color)
|
||||
|
||||
im0_with_heatmap = cv2.addWeighted(self.im0, 1 - self.heatmap_alpha, heatmap_colored, self.heatmap_alpha, 0)
|
||||
|
||||
|
||||
@ -9,8 +9,7 @@ from ultralytics.utils.plotting import Annotator, colors
|
||||
|
||||
check_requirements('shapely>=2.0.0')
|
||||
|
||||
from shapely.geometry import Polygon
|
||||
from shapely.geometry.point import Point
|
||||
from shapely.geometry import LineString, Point, Polygon
|
||||
|
||||
|
||||
class ObjectCounter:
|
||||
@ -23,10 +22,12 @@ class ObjectCounter:
|
||||
self.is_drawing = False
|
||||
self.selected_point = None
|
||||
|
||||
# Region Information
|
||||
self.reg_pts = None
|
||||
# Region & Line Information
|
||||
self.reg_pts = [(20, 400), (1260, 400)]
|
||||
self.line_dist_thresh = 15
|
||||
self.counting_region = None
|
||||
self.region_color = (255, 255, 255)
|
||||
self.region_color = (255, 0, 255)
|
||||
self.region_thickness = 5
|
||||
|
||||
# Image and annotation Information
|
||||
self.im0 = None
|
||||
@ -40,11 +41,15 @@ class ObjectCounter:
|
||||
self.in_counts = 0
|
||||
self.out_counts = 0
|
||||
self.counting_list = []
|
||||
self.count_txt_thickness = 0
|
||||
self.count_txt_color = (0, 0, 0)
|
||||
self.count_color = (255, 255, 255)
|
||||
|
||||
# Tracks info
|
||||
self.track_history = defaultdict(list)
|
||||
self.track_thickness = 2
|
||||
self.draw_tracks = False
|
||||
self.track_color = (0, 255, 0)
|
||||
|
||||
# Check if environment support imshow
|
||||
self.env_check = check_imshow(warn=True)
|
||||
@ -52,11 +57,17 @@ class ObjectCounter:
|
||||
def set_args(self,
|
||||
classes_names,
|
||||
reg_pts,
|
||||
region_color=None,
|
||||
count_reg_color=(255, 0, 255),
|
||||
line_thickness=2,
|
||||
track_thickness=2,
|
||||
view_img=False,
|
||||
draw_tracks=False):
|
||||
draw_tracks=False,
|
||||
count_txt_thickness=2,
|
||||
count_txt_color=(0, 0, 0),
|
||||
count_color=(255, 255, 255),
|
||||
track_color=(0, 255, 0),
|
||||
region_thickness=5,
|
||||
line_dist_thresh=15):
|
||||
"""
|
||||
Configures the Counter's image, bounding box line thickness, and counting region points.
|
||||
|
||||
@ -65,18 +76,43 @@ class ObjectCounter:
|
||||
view_img (bool): Flag to control whether to display the video stream.
|
||||
reg_pts (list): Initial list of points defining the counting region.
|
||||
classes_names (dict): Classes names
|
||||
region_color (tuple): color for region line
|
||||
track_thickness (int): Track thickness
|
||||
draw_tracks (Bool): draw tracks
|
||||
count_txt_thickness (int): Text thickness for object counting display
|
||||
count_txt_color (RGB color): count text color value
|
||||
count_color (RGB color): count text background color value
|
||||
count_reg_color (RGB color): Color of object counting region
|
||||
track_color (RGB color): color for tracks
|
||||
region_thickness (int): Object counting Region thickness
|
||||
line_dist_thresh (int): Euclidean Distance threshold for line counter
|
||||
"""
|
||||
self.tf = line_thickness
|
||||
self.view_img = view_img
|
||||
self.track_thickness = track_thickness
|
||||
self.draw_tracks = draw_tracks
|
||||
|
||||
# Region and line selection
|
||||
if len(reg_pts) == 2:
|
||||
print('Line Counter Initiated.')
|
||||
self.reg_pts = reg_pts
|
||||
self.counting_region = LineString(self.reg_pts)
|
||||
elif len(reg_pts) == 4:
|
||||
print('Region Counter Initiated.')
|
||||
self.reg_pts = reg_pts
|
||||
self.counting_region = Polygon(self.reg_pts)
|
||||
else:
|
||||
print('Invalid Region points provided, region_points can be 2 or 4')
|
||||
print('Using Line Counter Now')
|
||||
self.counting_region = LineString(self.reg_pts)
|
||||
|
||||
self.names = classes_names
|
||||
self.region_color = region_color if region_color else self.region_color
|
||||
self.track_color = track_color
|
||||
self.count_txt_thickness = count_txt_thickness
|
||||
self.count_txt_color = count_txt_color
|
||||
self.count_color = count_color
|
||||
self.region_color = count_reg_color
|
||||
self.region_thickness = region_thickness
|
||||
self.line_dist_thresh = line_dist_thresh
|
||||
|
||||
def mouse_event_for_region(self, event, x, y, flags, params):
|
||||
"""
|
||||
@ -113,11 +149,14 @@ class ObjectCounter:
|
||||
clss = tracks[0].boxes.cls.cpu().tolist()
|
||||
track_ids = tracks[0].boxes.id.int().cpu().tolist()
|
||||
|
||||
# Annotator Init and region drawing
|
||||
self.annotator = Annotator(self.im0, self.tf, self.names)
|
||||
self.annotator.draw_region(reg_pts=self.reg_pts, color=(0, 255, 0))
|
||||
self.annotator.draw_region(reg_pts=self.reg_pts, color=self.region_color, thickness=self.region_thickness)
|
||||
|
||||
# Extract tracks
|
||||
for box, track_id, cls in zip(boxes, track_ids, clss):
|
||||
self.annotator.box_label(box, label=self.names[cls], color=colors(int(cls), True)) # Draw bounding box
|
||||
self.annotator.box_label(box, label=str(track_id) + ':' + self.names[cls],
|
||||
color=colors(int(cls), True)) # Draw bounding box
|
||||
|
||||
# Draw Tracks
|
||||
track_line = self.track_history[track_id]
|
||||
@ -125,12 +164,14 @@ class ObjectCounter:
|
||||
if len(track_line) > 30:
|
||||
track_line.pop(0)
|
||||
|
||||
# Draw track trails
|
||||
if self.draw_tracks:
|
||||
self.annotator.draw_centroid_and_tracks(track_line,
|
||||
color=(0, 255, 0),
|
||||
color=self.track_color,
|
||||
track_thickness=self.track_thickness)
|
||||
|
||||
# Count objects
|
||||
if len(self.reg_pts) == 4:
|
||||
if self.counting_region.contains(Point(track_line[-1])):
|
||||
if track_id not in self.counting_list:
|
||||
self.counting_list.append(track_id)
|
||||
@ -139,12 +180,27 @@ class ObjectCounter:
|
||||
else:
|
||||
self.in_counts += 1
|
||||
|
||||
incount_label = 'InCount : ' + f'{self.in_counts}'
|
||||
elif len(self.reg_pts) == 2:
|
||||
distance = Point(track_line[-1]).distance(self.counting_region)
|
||||
if distance < self.line_dist_thresh:
|
||||
if track_id not in self.counting_list:
|
||||
self.counting_list.append(track_id)
|
||||
if box[0] < self.counting_region.centroid.x:
|
||||
self.out_counts += 1
|
||||
else:
|
||||
self.in_counts += 1
|
||||
|
||||
incount_label = 'In Count : ' + f'{self.in_counts}'
|
||||
outcount_label = 'OutCount : ' + f'{self.out_counts}'
|
||||
self.annotator.count_labels(in_count=incount_label, out_count=outcount_label)
|
||||
self.annotator.count_labels(in_count=incount_label,
|
||||
out_count=outcount_label,
|
||||
count_txt_size=self.count_txt_thickness,
|
||||
txt_color=self.count_txt_color,
|
||||
color=self.count_color)
|
||||
|
||||
if self.env_check and self.view_img:
|
||||
cv2.namedWindow('Ultralytics YOLOv8 Object Counter')
|
||||
if len(self.reg_pts) == 4: # only add mouse event If user drawn region
|
||||
cv2.setMouseCallback('Ultralytics YOLOv8 Object Counter', self.mouse_event_for_region,
|
||||
{'region_points': self.reg_pts})
|
||||
cv2.imshow('Ultralytics YOLOv8 Object Counter', self.im0)
|
||||
@ -161,6 +217,7 @@ class ObjectCounter:
|
||||
tracks (list): List of tracks obtained from the object tracking process.
|
||||
"""
|
||||
self.im0 = im0 # store image
|
||||
|
||||
if tracks[0].boxes.id is None:
|
||||
return
|
||||
self.extract_and_process_tracks(tracks)
|
||||
|
||||
@ -260,19 +260,41 @@ class Annotator:
|
||||
|
||||
# Object Counting Annotator
|
||||
def draw_region(self, reg_pts=None, color=(0, 255, 0), thickness=5):
|
||||
# Draw region line
|
||||
"""
|
||||
Draw region line
|
||||
Args:
|
||||
reg_pts (list): Region Points (for line 2 points, for region 4 points)
|
||||
color (tuple): Region Color value
|
||||
thickness (int): Region area thickness value
|
||||
"""
|
||||
cv2.polylines(self.im, [np.array(reg_pts, dtype=np.int32)], isClosed=True, color=color, thickness=thickness)
|
||||
|
||||
def draw_centroid_and_tracks(self, track, color=(255, 0, 255), track_thickness=2):
|
||||
# Draw region line
|
||||
"""
|
||||
Draw centroid point and track trails
|
||||
Args:
|
||||
track (list): object tracking points for trails display
|
||||
color (tuple): tracks line color
|
||||
track_thickness (int): track line thickness value
|
||||
"""
|
||||
points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
|
||||
cv2.polylines(self.im, [points], isClosed=False, color=color, thickness=track_thickness)
|
||||
cv2.circle(self.im, (int(track[-1][0]), int(track[-1][1])), track_thickness * 2, color, -1)
|
||||
|
||||
def count_labels(self, in_count=0, out_count=0, color=(255, 255, 255), txt_color=(0, 0, 0)):
|
||||
def count_labels(self, in_count=0, out_count=0, count_txt_size=2, color=(255, 255, 255), txt_color=(0, 0, 0)):
|
||||
"""
|
||||
Plot counts for object counter
|
||||
Args:
|
||||
in_count (int): in count value
|
||||
out_count (int): out count value
|
||||
count_txt_size (int): text size for counts display
|
||||
color (tuple): background color of counts display
|
||||
txt_color (tuple): text color of counts display
|
||||
"""
|
||||
self.tf = count_txt_size
|
||||
tl = self.tf or round(0.002 * (self.im.shape[0] + self.im.shape[1]) / 2) + 1
|
||||
tf = max(tl - 1, 1)
|
||||
gap = int(24 * tl) # Calculate the gap between in_count and out_count based on line_thickness
|
||||
gap = int(24 * tl) # gap between in_count and out_count based on line_thickness
|
||||
|
||||
# Get text size for in_count and out_count
|
||||
t_size_in = cv2.getTextSize(str(in_count), 0, fontScale=tl / 2, thickness=tf)[0]
|
||||
@ -306,14 +328,13 @@ class Annotator:
|
||||
thickness=self.tf,
|
||||
lineType=cv2.LINE_AA)
|
||||
|
||||
# AI GYM Annotator
|
||||
def estimate_pose_angle(self, a, b, c):
|
||||
@staticmethod
|
||||
def estimate_pose_angle(a, b, c):
|
||||
"""Calculate the pose angle for object
|
||||
Args:
|
||||
a (float) : The value of pose point a
|
||||
b (float): The value of pose point b
|
||||
c (float): The value o pose point c
|
||||
|
||||
Returns:
|
||||
angle (degree): Degree value of angle between three points
|
||||
"""
|
||||
@ -325,7 +346,15 @@ class Annotator:
|
||||
return angle
|
||||
|
||||
def draw_specific_points(self, keypoints, indices=[2, 5, 7], shape=(640, 640), radius=2):
|
||||
"""Draw specific keypoints for gym steps counting."""
|
||||
"""
|
||||
Draw specific keypoints for gym steps counting.
|
||||
|
||||
Args:
|
||||
keypoints (list): list of keypoints data to be plotted
|
||||
indices (list): keypoints ids list to be plotted
|
||||
shape (tuple): imgsz for model inference
|
||||
radius (int): Keypoint radius value
|
||||
"""
|
||||
nkpts, ndim = keypoints.shape
|
||||
nkpts == 17 and ndim == 3
|
||||
for i, k in enumerate(keypoints):
|
||||
@ -340,8 +369,17 @@ class Annotator:
|
||||
return self.im
|
||||
|
||||
def plot_angle_and_count_and_stage(self, angle_text, count_text, stage_text, center_kpt, line_thickness=2):
|
||||
"""Plot the pose angle, count value and step stage."""
|
||||
angle_text, count_text, stage_text = f' {angle_text:.2f}', 'Steps : ' + f'{count_text}', f' {stage_text}'
|
||||
"""
|
||||
Plot the pose angle, count value and step stage.
|
||||
|
||||
Args:
|
||||
angle_text (str): angle value for workout monitoring
|
||||
count_text (str): counts value for workout monitoring
|
||||
stage_text (str): stage decision for workout monitoring
|
||||
center_kpt (int): centroid pose index for workout monitoring
|
||||
line_thickness (int): thickness for text display
|
||||
"""
|
||||
angle_text, count_text, stage_text = (f' {angle_text:.2f}', 'Steps : ' + f'{count_text}', f' {stage_text}')
|
||||
font_scale = 0.6 + (line_thickness / 10.0)
|
||||
|
||||
# Draw angle
|
||||
@ -378,18 +416,38 @@ class Annotator:
|
||||
cv2.putText(self.im, stage_text, stage_text_position, 0, font_scale, (0, 0, 0), line_thickness)
|
||||
|
||||
def seg_bbox(self, mask, mask_color=(255, 0, 255), det_label=None, track_label=None):
|
||||
"""Function for drawing segmented object in bounding box shape."""
|
||||
"""
|
||||
Function for drawing segmented object in bounding box shape.
|
||||
|
||||
Args:
|
||||
mask (list): masks data list for instance segmentation area plotting
|
||||
mask_color (tuple): mask foreground color
|
||||
det_label (str): Detection label text
|
||||
track_label (str): Tracking label text
|
||||
"""
|
||||
cv2.polylines(self.im, [np.int32([mask])], isClosed=True, color=mask_color, thickness=2)
|
||||
|
||||
label = f'Track ID: {track_label}' if track_label else det_label
|
||||
text_size, _ = cv2.getTextSize(label, 0, 0.7, 1)
|
||||
|
||||
cv2.rectangle(self.im, (int(mask[0][0]) - text_size[0] // 2 - 10, int(mask[0][1]) - text_size[1] - 10),
|
||||
(int(mask[0][0]) + text_size[0] // 2 + 5, int(mask[0][1] + 5)), mask_color, -1)
|
||||
|
||||
cv2.putText(self.im, label, (int(mask[0][0]) - text_size[0] // 2, int(mask[0][1]) - 5), 0, 0.7, (255, 255, 255),
|
||||
2)
|
||||
|
||||
def visioneye(self, box, center_point, color=(235, 219, 11), pin_color=(255, 0, 255), thickness=2, pins_radius=10):
|
||||
"""Function for pinpoint human-vision eye mapping and plotting."""
|
||||
"""
|
||||
Function for pinpoint human-vision eye mapping and plotting.
|
||||
|
||||
Args:
|
||||
box (list): Bounding box coordinates
|
||||
center_point (tuple): center point for vision eye view
|
||||
color (tuple): object centroid and line color value
|
||||
pin_color (tuple): visioneye point color value
|
||||
thickness (int): int value for line thickness
|
||||
pins_radius (int): visioneye point radius value
|
||||
"""
|
||||
center_bbox = int((box[0] + box[2]) / 2), int((box[1] + box[3]) / 2)
|
||||
cv2.circle(self.im, center_point, pins_radius, pin_color, -1)
|
||||
cv2.circle(self.im, center_bbox, pins_radius, color, -1)
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user