Update heatmaps solution (#16720)
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
This commit is contained in:
Muhammad Rizwan Munawar
GitHub
parent
e56f9140fb
commit
4884311991
6 changed files with 139 additions and 350 deletions
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@ -41,10 +41,9 @@ A heatmap generated with [Ultralytics YOLO11](https://github.com/ultralytics/ult
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```python
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import cv2
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from ultralytics import YOLO, solutions
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from ultralytics import solutions
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model = YOLO("yolo11n.pt")
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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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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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
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@ -52,11 +51,10 @@ A heatmap generated with [Ultralytics YOLO11](https://github.com/ultralytics/ult
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video_writer = cv2.VideoWriter("heatmap_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
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# Init heatmap
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heatmap_obj = solutions.Heatmap(
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heatmap = solutions.Heatmap(
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show=True,
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model="yolo11n.pt",
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colormap=cv2.COLORMAP_PARULA,
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view_img=True,
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shape="circle",
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names=model.names,
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)
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while cap.isOpened():
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@ -64,9 +62,7 @@ A heatmap generated with [Ultralytics YOLO11](https://github.com/ultralytics/ult
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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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im0 = heatmap.generate_heatmap(im0)
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video_writer.write(im0)
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cap.release()
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@ -79,25 +75,24 @@ A heatmap generated with [Ultralytics YOLO11](https://github.com/ultralytics/ult
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```python
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import cv2
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from ultralytics import YOLO, solutions
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from ultralytics import solutions
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model = YOLO("yolo11n.pt")
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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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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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
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# Video writer
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video_writer = cv2.VideoWriter("heatmap_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
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line_points = [(20, 400), (1080, 404)] # line for object counting
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# line for object counting
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line_points = [(20, 400), (1080, 404)]
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# Init heatmap
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heatmap_obj = solutions.Heatmap(
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heatmap = solutions.Heatmap(
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show=True,
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model="yolo11n.pt",
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colormap=cv2.COLORMAP_PARULA,
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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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names=model.names,
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region=line_points,
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)
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while cap.isOpened():
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@ -105,9 +100,7 @@ A heatmap generated with [Ultralytics YOLO11](https://github.com/ultralytics/ult
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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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im0 = heatmap.generate_heatmap(im0)
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video_writer.write(im0)
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cap.release()
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@ -120,10 +113,9 @@ A heatmap generated with [Ultralytics YOLO11](https://github.com/ultralytics/ult
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```python
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import cv2
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from ultralytics import YOLO, solutions
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from ultralytics import solutions
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model = YOLO("yolo11n.pt")
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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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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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
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@ -134,12 +126,11 @@ A heatmap generated with [Ultralytics YOLO11](https://github.com/ultralytics/ult
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region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360), (20, 400)]
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# Init heatmap
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heatmap_obj = solutions.Heatmap(
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heatmap = solutions.Heatmap(
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show=True,
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model="yolo11n.pt",
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colormap=cv2.COLORMAP_PARULA,
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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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names=model.names,
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region=region_points,
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)
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while cap.isOpened():
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@ -147,9 +138,7 @@ A heatmap generated with [Ultralytics YOLO11](https://github.com/ultralytics/ult
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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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im0 = heatmap.generate_heatmap(im0)
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video_writer.write(im0)
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cap.release()
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@ -162,10 +151,9 @@ A heatmap generated with [Ultralytics YOLO11](https://github.com/ultralytics/ult
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```python
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import cv2
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from ultralytics import YOLO, solutions
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from ultralytics import solutions
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model = YOLO("yolo11n.pt")
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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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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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
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@ -176,12 +164,11 @@ A heatmap generated with [Ultralytics YOLO11](https://github.com/ultralytics/ult
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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 = solutions.Heatmap(
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heatmap = solutions.Heatmap(
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show=True,
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model="yolo11n.pt",
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colormap=cv2.COLORMAP_PARULA,
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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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names=model.names,
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region=region_points,
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)
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while cap.isOpened():
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@ -189,9 +176,7 @@ A heatmap generated with [Ultralytics YOLO11](https://github.com/ultralytics/ult
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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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im0 = heatmap.generate_heatmap(im0)
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video_writer.write(im0)
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cap.release()
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@ -199,54 +184,25 @@ A heatmap generated with [Ultralytics YOLO11](https://github.com/ultralytics/ult
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cv2.destroyAllWindows()
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```
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=== "Im0"
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```python
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import cv2
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from ultralytics import YOLO, solutions
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model = YOLO("yolo11n.pt") # YOLO11 custom/pretrained model
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im0 = cv2.imread("path/to/image.png") # path to image file
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h, w = im0.shape[:2] # image height and width
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# Heatmap Init
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heatmap_obj = solutions.Heatmap(
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colormap=cv2.COLORMAP_PARULA,
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view_img=True,
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shape="circle",
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names=model.names,
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)
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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.imwrite("ultralytics_output.png", im0)
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```
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=== "Specific Classes"
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```python
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import cv2
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from ultralytics import YOLO, solutions
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from ultralytics import solutions
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model = YOLO("yolo11n.pt")
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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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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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
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# Video writer
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video_writer = cv2.VideoWriter("heatmap_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
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classes_for_heatmap = [0, 2] # classes for heatmap
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# Init heatmap
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heatmap_obj = solutions.Heatmap(
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colormap=cv2.COLORMAP_PARULA,
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view_img=True,
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shape="circle",
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names=model.names,
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heatmap = solutions.Heatmap(
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show=True,
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model="yolo11n.pt",
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classes=[0, 2],
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)
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while cap.isOpened():
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@ -254,9 +210,7 @@ A heatmap generated with [Ultralytics YOLO11](https://github.com/ultralytics/ult
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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, classes=classes_for_heatmap)
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im0 = heatmap_obj.generate_heatmap(im0, tracks)
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im0 = heatmap.generate_heatmap(im0)
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video_writer.write(im0)
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cap.release()
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@ -266,21 +220,14 @@ A heatmap generated with [Ultralytics YOLO11](https://github.com/ultralytics/ult
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### Arguments `Heatmap()`
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| Name | Type | Default | Description |
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| ------------------ | ---------------- | ------------------ | ----------------------------------------------------------------- |
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| `names` | `list` | `None` | Dictionary of class names. |
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| `colormap` | `int` | `cv2.COLORMAP_JET` | Colormap to use for the heatmap. |
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| `view_img` | `bool` | `False` | Whether to display the image with the heatmap overlay. |
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| `view_in_counts` | `bool` | `True` | Whether to display the count of objects entering the region. |
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| `view_out_counts` | `bool` | `True` | Whether to display the count of objects exiting the region. |
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| `count_reg_pts` | `list` or `None` | `None` | Points defining the counting region (either a line or a polygon). |
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| `count_txt_color` | `tuple` | `(0, 0, 0)` | Text color for displaying counts. |
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| `count_bg_color` | `tuple` | `(255, 255, 255)` | Background color for displaying counts. |
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| `count_reg_color` | `tuple` | `(255, 0, 255)` | Color for the counting region. |
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| `region_thickness` | `int` | `5` | Thickness of the region line. |
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| `line_dist_thresh` | `int` | `15` | Distance threshold for line-based counting. |
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| `line_thickness` | `int` | `2` | Thickness of the lines used in drawing. |
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| `shape` | `str` | `"circle"` | Shape of the heatmap blobs ('circle' or 'rect'). |
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| Name | Type | Default | Description |
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| ------------ | ------ | ------------------ | ----------------------------------------------------------------- |
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| `colormap` | `int` | `cv2.COLORMAP_JET` | Colormap to use for the heatmap. |
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| `show` | `bool` | `False` | Whether to display the image with the heatmap overlay. |
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| `show_in` | `bool` | `True` | Whether to display the count of objects entering the region. |
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| `show_out` | `bool` | `True` | Whether to display the count of objects exiting the region. |
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| `region` | `list` | `None` | Points defining the counting region (either a line or a polygon). |
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| `line_width` | `int` | `2` | Thickness of the lines used in drawing. |
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### Arguments `model.track`
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@ -328,18 +275,16 @@ Yes, Ultralytics YOLO11 supports object tracking and heatmap generation concurre
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```python
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import cv2
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from ultralytics import YOLO, solutions
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from ultralytics import solutions
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model = YOLO("yolo11n.pt")
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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heatmap_obj = solutions.Heatmap(colormap=cv2.COLORMAP_PARULA, view_img=True, shape="circle", names=model.names)
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heatmap = solutions.Heatmap(colormap=cv2.COLORMAP_PARULA, show=True, model="yolo11n.pt")
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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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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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im0 = heatmap.generate_heatmap(im0)
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cv2.imshow("Heatmap", im0)
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if cv2.waitKey(1) & 0xFF == ord("q"):
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break
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@ -361,19 +306,16 @@ You can visualize specific object classes by specifying the desired classes in t
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```python
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import cv2
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from ultralytics import YOLO, solutions
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from ultralytics import solutions
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model = YOLO("yolo11n.pt")
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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heatmap_obj = solutions.Heatmap(colormap=cv2.COLORMAP_PARULA, view_img=True, shape="circle", names=model.names)
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heatmap = solutions.Heatmap(show=True, model="yolo11n.pt", classes=[0, 2])
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classes_for_heatmap = [0, 2] # Classes to visualize
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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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break
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tracks = model.track(im0, persist=True, show=False, classes=classes_for_heatmap)
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im0 = heatmap_obj.generate_heatmap(im0, tracks)
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im0 = heatmap.generate_heatmap(im0)
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cv2.imshow("Heatmap", im0)
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if cv2.waitKey(1) & 0xFF == ord("q"):
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break
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@ -19,8 +19,8 @@ def test_major_solutions():
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cap = cv2.VideoCapture("solutions_ci_demo.mp4")
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assert cap.isOpened(), "Error reading video file"
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region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
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# counter = solutions.ObjectCounter(reg_pts=region_points, names=names, view_img=False)
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heatmap = solutions.Heatmap(colormap=cv2.COLORMAP_PARULA, names=names, view_img=False)
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counter = solutions.ObjectCounter(region=region_points, model="yolo11n.pt", show=False)
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heatmap = solutions.Heatmap(colormap=cv2.COLORMAP_PARULA, model="yolo11n.pt", show=False)
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speed = solutions.SpeedEstimator(reg_pts=region_points, names=names, view_img=False)
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queue = solutions.QueueManager(names=names, reg_pts=region_points, view_img=False)
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while cap.isOpened():
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@ -29,8 +29,8 @@ def test_major_solutions():
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break
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original_im0 = im0.copy()
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tracks = model.track(im0, persist=True, show=False)
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# _ = counter.start_counting(original_im0.copy(), tracks)
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_ = heatmap.generate_heatmap(original_im0.copy(), tracks)
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_ = counter.count(original_im0.copy())
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_ = heatmap.generate_heatmap(original_im0.copy())
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_ = speed.estimate_speed(original_im0.copy(), tracks)
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_ = queue.process_queue(original_im0.copy(), tracks)
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cap.release()
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@ -10,7 +10,7 @@ show: True # Flag to control whether to display output image or not
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show_in: True # Flag to display objects moving *into* the defined region
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show_out: True # Flag to display objects moving *out of* the defined region
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classes: # To count specific classes
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up_angle: 145.0 # workouts up_angle for counts, 145.0 is default value
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down_angle: 90 # workouts down_angle for counts, 90 is default value
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kpts: [6, 8, 10] # keypoints for workouts monitoring
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up_angle: 145.0 # Workouts up_angle for counts, 145.0 is default value
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down_angle: 90 # Workouts down_angle for counts, 90 is default value
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kpts: [6, 8, 10] # Keypoints for workouts monitoring
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colormap: # Colormap for heatmap
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@ -1,249 +1,93 @@
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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from collections import defaultdict
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import cv2
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import numpy as np
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from ultralytics.utils.checks import check_imshow, check_requirements
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from ultralytics.solutions.object_counter import ObjectCounter # Import object counter class
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from ultralytics.utils.plotting import Annotator
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check_requirements("shapely>=2.0.0")
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from shapely.geometry import LineString, Point, Polygon
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class Heatmap:
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class Heatmap(ObjectCounter):
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"""A class to draw heatmaps in real-time video stream based on their tracks."""
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def __init__(
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self,
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names,
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colormap=cv2.COLORMAP_JET,
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view_img=False,
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view_in_counts=True,
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view_out_counts=True,
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count_reg_pts=None,
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count_txt_color=(0, 0, 0),
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count_bg_color=(255, 255, 255),
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count_reg_color=(255, 0, 255),
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region_thickness=5,
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line_dist_thresh=15,
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line_thickness=2,
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shape="circle",
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):
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"""Initializes the heatmap class with default values for Visual, Image, track, count and heatmap parameters."""
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# Visual information
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self.annotator = None
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self.view_img = view_img
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self.shape = shape
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def __init__(self, **kwargs):
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"""Initializes function for heatmap class with default values."""
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super().__init__(**kwargs)
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self.initialized = False
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self.names = names # Classes names
|
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self.initialized = False # bool variable for heatmap initialization
|
||||
if self.region is not None: # check if user provided the region coordinates
|
||||
self.initialize_region()
|
||||
|
||||
# Image information
|
||||
self.im0 = None
|
||||
self.tf = line_thickness
|
||||
self.view_in_counts = view_in_counts
|
||||
self.view_out_counts = view_out_counts
|
||||
# store colormap
|
||||
self.colormap = cv2.COLORMAP_PARULA if self.CFG["colormap"] is None else self.CFG["colormap"]
|
||||
|
||||
# Heatmap colormap and heatmap np array
|
||||
self.colormap = colormap
|
||||
self.heatmap = None
|
||||
|
||||
# Predict/track information
|
||||
self.boxes = []
|
||||
self.track_ids = []
|
||||
self.clss = []
|
||||
self.track_history = defaultdict(list)
|
||||
|
||||
# Region & Line Information
|
||||
self.counting_region = None
|
||||
self.line_dist_thresh = line_dist_thresh
|
||||
self.region_thickness = region_thickness
|
||||
self.region_color = count_reg_color
|
||||
|
||||
# Object Counting Information
|
||||
self.in_counts = 0
|
||||
self.out_counts = 0
|
||||
self.count_ids = []
|
||||
self.class_wise_count = {}
|
||||
self.count_txt_color = count_txt_color
|
||||
self.count_bg_color = count_bg_color
|
||||
self.cls_txtdisplay_gap = 50
|
||||
|
||||
# Check if environment supports imshow
|
||||
self.env_check = check_imshow(warn=True)
|
||||
|
||||
# Region and line selection
|
||||
self.count_reg_pts = count_reg_pts
|
||||
print(self.count_reg_pts)
|
||||
if self.count_reg_pts is not None:
|
||||
if len(self.count_reg_pts) == 2:
|
||||
print("Line Counter Initiated.")
|
||||
self.counting_region = LineString(self.count_reg_pts)
|
||||
elif len(self.count_reg_pts) >= 3:
|
||||
print("Polygon Counter Initiated.")
|
||||
self.counting_region = Polygon(self.count_reg_pts)
|
||||
else:
|
||||
print("Invalid Region points provided, region_points must be 2 for lines or >= 3 for polygons.")
|
||||
print("Using Line Counter Now")
|
||||
self.counting_region = LineString(self.count_reg_pts)
|
||||
|
||||
# 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):
|
||||
def heatmap_effect(self, box):
|
||||
"""
|
||||
Extracts results from the provided data.
|
||||
Efficient calculation of heatmap area and effect location for applying colormap.
|
||||
|
||||
Args:
|
||||
tracks (list): List of tracks obtained from the object tracking process.
|
||||
box (list): Bounding Box coordinates data [x0, y0, x1, y1]
|
||||
"""
|
||||
if tracks[0].boxes.id is not None:
|
||||
self.boxes = tracks[0].boxes.xyxy.cpu()
|
||||
self.clss = tracks[0].boxes.cls.tolist()
|
||||
self.track_ids = tracks[0].boxes.id.int().tolist()
|
||||
x0, y0, x1, y1 = map(int, box)
|
||||
radius_squared = (min(x1 - x0, y1 - y0) // 2) ** 2
|
||||
|
||||
def generate_heatmap(self, im0, tracks):
|
||||
# Create a meshgrid with region of interest (ROI) for vectorized distance calculations
|
||||
xv, yv = np.meshgrid(np.arange(x0, x1), np.arange(y0, y1))
|
||||
|
||||
# Calculate squared distances from the center
|
||||
dist_squared = (xv - ((x0 + x1) // 2)) ** 2 + (yv - ((y0 + y1) // 2)) ** 2
|
||||
|
||||
# Create a mask of points within the radius
|
||||
within_radius = dist_squared <= radius_squared
|
||||
|
||||
# Update only the values within the bounding box in a single vectorized operation
|
||||
self.heatmap[y0:y1, x0:x1][within_radius] += 2
|
||||
|
||||
def generate_heatmap(self, im0):
|
||||
"""
|
||||
Generate heatmap based on tracking data.
|
||||
Generate heatmap for each frame using Ultralytics.
|
||||
|
||||
Args:
|
||||
im0 (nd array): Image
|
||||
tracks (list): List of tracks obtained from the object tracking process.
|
||||
im0 (ndarray): Input image array for processing
|
||||
Returns:
|
||||
im0 (ndarray): Processed image for further usage
|
||||
"""
|
||||
self.im0 = im0
|
||||
self.heatmap = np.zeros_like(im0, dtype=np.float32) * 0.99 if not self.initialized else self.heatmap
|
||||
self.initialized = True # Initialize heatmap only once
|
||||
|
||||
# Initialize heatmap only once
|
||||
if not self.initialized:
|
||||
self.heatmap = np.zeros((int(self.im0.shape[0]), int(self.im0.shape[1])), dtype=np.float32)
|
||||
self.initialized = True
|
||||
self.annotator = Annotator(im0, line_width=self.line_width) # Initialize annotator
|
||||
self.extract_tracks(im0) # Extract tracks
|
||||
|
||||
self.heatmap *= 0.99 # decay factor
|
||||
# Iterate over bounding boxes, track ids and classes index
|
||||
for box, track_id, cls in zip(self.boxes, self.track_ids, self.clss):
|
||||
# Draw bounding box and counting region
|
||||
self.heatmap_effect(box)
|
||||
|
||||
self.extract_results(tracks)
|
||||
self.annotator = Annotator(self.im0, self.tf, None)
|
||||
|
||||
if self.track_ids:
|
||||
# Draw counting region
|
||||
if self.count_reg_pts is not None:
|
||||
self.annotator.draw_region(
|
||||
reg_pts=self.count_reg_pts, color=self.region_color, thickness=self.region_thickness
|
||||
)
|
||||
|
||||
for box, cls, track_id in zip(self.boxes, self.clss, self.track_ids):
|
||||
# Store class info
|
||||
if self.names[cls] not in self.class_wise_count:
|
||||
self.class_wise_count[self.names[cls]] = {"IN": 0, "OUT": 0}
|
||||
|
||||
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]
|
||||
track_line.append((float((box[0] + box[2]) / 2), float((box[1] + box[3]) / 2)))
|
||||
if len(track_line) > 30:
|
||||
track_line.pop(0)
|
||||
if self.region is not None:
|
||||
self.annotator.draw_region(reg_pts=self.region, color=(104, 0, 123), thickness=self.line_width * 2)
|
||||
self.store_tracking_history(track_id, box) # Store track history
|
||||
self.store_classwise_counts(cls) # store classwise counts in dict
|
||||
|
||||
# Store tracking previous position and perform object counting
|
||||
prev_position = self.track_history[track_id][-2] if len(self.track_history[track_id]) > 1 else None
|
||||
self.count_objects(self.track_line, box, track_id, prev_position, cls) # Perform object counting
|
||||
|
||||
if self.count_reg_pts is not None:
|
||||
# Count objects in any polygon
|
||||
if len(self.count_reg_pts) >= 3:
|
||||
is_inside = self.counting_region.contains(Point(track_line[-1]))
|
||||
|
||||
if prev_position is not None and is_inside and track_id not in self.count_ids:
|
||||
self.count_ids.append(track_id)
|
||||
|
||||
if (box[0] - prev_position[0]) * (self.counting_region.centroid.x - prev_position[0]) > 0:
|
||||
self.in_counts += 1
|
||||
self.class_wise_count[self.names[cls]]["IN"] += 1
|
||||
else:
|
||||
self.out_counts += 1
|
||||
self.class_wise_count[self.names[cls]]["OUT"] += 1
|
||||
|
||||
# Count objects using line
|
||||
elif len(self.count_reg_pts) == 2:
|
||||
if prev_position is not None and track_id not in self.count_ids:
|
||||
distance = Point(track_line[-1]).distance(self.counting_region)
|
||||
if distance < self.line_dist_thresh and track_id not in self.count_ids:
|
||||
self.count_ids.append(track_id)
|
||||
|
||||
if (box[0] - prev_position[0]) * (
|
||||
self.counting_region.centroid.x - prev_position[0]
|
||||
) > 0:
|
||||
self.in_counts += 1
|
||||
self.class_wise_count[self.names[cls]]["IN"] += 1
|
||||
else:
|
||||
self.out_counts += 1
|
||||
self.class_wise_count[self.names[cls]]["OUT"] += 1
|
||||
|
||||
else:
|
||||
for box, cls in zip(self.boxes, self.clss):
|
||||
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
|
||||
|
||||
if self.count_reg_pts is not None:
|
||||
labels_dict = {}
|
||||
|
||||
for key, value in self.class_wise_count.items():
|
||||
if value["IN"] != 0 or value["OUT"] != 0:
|
||||
if not self.view_in_counts and not self.view_out_counts:
|
||||
continue
|
||||
elif not self.view_in_counts:
|
||||
labels_dict[str.capitalize(key)] = f"OUT {value['OUT']}"
|
||||
elif not self.view_out_counts:
|
||||
labels_dict[str.capitalize(key)] = f"IN {value['IN']}"
|
||||
else:
|
||||
labels_dict[str.capitalize(key)] = f"IN {value['IN']} OUT {value['OUT']}"
|
||||
|
||||
if labels_dict is not None:
|
||||
self.annotator.display_analytics(self.im0, labels_dict, self.count_txt_color, self.count_bg_color, 10)
|
||||
self.display_counts(im0) if self.region is not None else None # Display the counts on the frame
|
||||
|
||||
# Normalize, apply colormap to heatmap and combine with original image
|
||||
heatmap_normalized = cv2.normalize(self.heatmap, None, 0, 255, cv2.NORM_MINMAX)
|
||||
heatmap_colored = cv2.applyColorMap(heatmap_normalized.astype(np.uint8), self.colormap)
|
||||
self.im0 = cv2.addWeighted(self.im0, 0.5, heatmap_colored, 0.5, 0)
|
||||
im0 = (
|
||||
im0
|
||||
if self.track_data.id is None
|
||||
else cv2.addWeighted(
|
||||
im0,
|
||||
0.5,
|
||||
cv2.applyColorMap(
|
||||
cv2.normalize(self.heatmap, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8), self.colormap
|
||||
),
|
||||
0.5,
|
||||
0,
|
||||
)
|
||||
)
|
||||
|
||||
if self.env_check and self.view_img:
|
||||
self.display_frames()
|
||||
|
||||
return self.im0
|
||||
|
||||
def display_frames(self):
|
||||
"""Display frame."""
|
||||
cv2.imshow("Ultralytics Heatmap", self.im0)
|
||||
|
||||
if cv2.waitKey(1) & 0xFF == ord("q"):
|
||||
return
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
classes_names = {0: "person", 1: "car"} # example class names
|
||||
heatmap = Heatmap(classes_names)
|
||||
self.display_output(im0) # display output with base class function
|
||||
return im0 # return output image for more usage
|
||||
|
|
|
|||
|
|
@ -19,8 +19,7 @@ class ObjectCounter(BaseSolution):
|
|||
self.out_count = 0 # Counter for objects moving outward
|
||||
self.counted_ids = [] # List of IDs of objects that have been counted
|
||||
self.classwise_counts = {} # Dictionary for counts, categorized by object class
|
||||
|
||||
self.initialize_region() # Setup region and counting areas
|
||||
self.region_initialized = False # Bool variable for region initialization
|
||||
|
||||
self.show_in = self.CFG["show_in"]
|
||||
self.show_out = self.CFG["show_out"]
|
||||
|
|
@ -99,6 +98,10 @@ class ObjectCounter(BaseSolution):
|
|||
Returns
|
||||
im0 (ndarray): The processed image for more usage
|
||||
"""
|
||||
if not self.region_initialized:
|
||||
self.initialize_region()
|
||||
self.region_initialized = True
|
||||
|
||||
self.annotator = Annotator(im0, line_width=self.line_width) # Initialize annotator
|
||||
self.extract_tracks(im0) # Extract tracks
|
||||
|
||||
|
|
@ -107,21 +110,20 @@ class ObjectCounter(BaseSolution):
|
|||
) # Draw region
|
||||
|
||||
# Iterate over bounding boxes, track ids and classes index
|
||||
if self.track_data is not None and self.track_data.id is not None:
|
||||
for box, track_id, cls in zip(self.boxes, self.track_ids, self.clss):
|
||||
# Draw bounding box and counting region
|
||||
self.annotator.box_label(box, label=self.names[cls], color=colors(track_id, True))
|
||||
self.store_tracking_history(track_id, box) # Store track history
|
||||
self.store_classwise_counts(cls) # store classwise counts in dict
|
||||
for box, track_id, cls in zip(self.boxes, self.track_ids, self.clss):
|
||||
# Draw bounding box and counting region
|
||||
self.annotator.box_label(box, label=self.names[cls], color=colors(track_id, True))
|
||||
self.store_tracking_history(track_id, box) # Store track history
|
||||
self.store_classwise_counts(cls) # store classwise counts in dict
|
||||
|
||||
# Draw centroid of objects
|
||||
self.annotator.draw_centroid_and_tracks(
|
||||
self.track_line, color=colors(int(track_id), True), track_thickness=self.line_width
|
||||
)
|
||||
# Draw centroid of objects
|
||||
self.annotator.draw_centroid_and_tracks(
|
||||
self.track_line, color=colors(int(track_id), True), track_thickness=self.line_width
|
||||
)
|
||||
|
||||
# store previous position of track for object counting
|
||||
prev_position = self.track_history[track_id][-2] if len(self.track_history[track_id]) > 1 else None
|
||||
self.count_objects(self.track_line, box, track_id, prev_position, cls) # Perform object counting
|
||||
# store previous position of track for object counting
|
||||
prev_position = self.track_history[track_id][-2] if len(self.track_history[track_id]) > 1 else None
|
||||
self.count_objects(self.track_line, box, track_id, prev_position, cls) # Perform object counting
|
||||
|
||||
self.display_counts(im0) # Display the counts on the frame
|
||||
self.display_output(im0) # display output with base class function
|
||||
|
|
|
|||
|
|
@ -57,7 +57,8 @@ class BaseSolution:
|
|||
self.clss = self.track_data.cls.cpu().tolist()
|
||||
self.track_ids = self.track_data.id.int().cpu().tolist()
|
||||
else:
|
||||
LOGGER.warning("WARNING ⚠️ tracks none, no keypoints will be considered.")
|
||||
LOGGER.warning("WARNING ⚠️ no tracks found!")
|
||||
self.boxes, self.clss, self.track_ids = [], [], []
|
||||
|
||||
def store_tracking_history(self, track_id, box):
|
||||
"""
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue