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ultralytics 8.0.237 cv2.CAP_PROP fix and in_counts and out_counts displays (#7380)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: ayush chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: Muhammad Rizwan Munawar <chr043416@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: 曾逸夫（Zeng Yifu） <41098760+Zengyf-CVer@users.noreply.github.com>
This commit is contained in:
Glenn Jocher 2024-01-07 19:06:57 +01:00 • committed by GitHub
parent 71fe5e919d
commit 8c2b2f56b2
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15 changed files with 108 additions and 90 deletions
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5
docs/en/guides/distance-calculation.md
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@ -33,12 +33,13 @@ Measuring the gap between two objects is known as distance calculation within a
cap = cv2.VideoCapture("path/to/video/file.mp4")
assert cap.isOpened(), "Error reading video file"
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
# Video writer
video_writer = cv2.VideoWriter("distance_calculation.avi",
cv2.VideoWriter_fourcc(*'mp4v'),
int(cap.get(5)),
(int(cap.get(3)), int(cap.get(4))))
fps,
(w, h))
# Init distance-calculation obj
dist_obj = distance_calculation.DistanceCalculation()

46
docs/en/guides/heatmaps.md
View file

@ -50,18 +50,19 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
model = YOLO("yolov8n.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")
assert cap.isOpened(), "Error reading video file"
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
# Video writer
video_writer = cv2.VideoWriter("heatmap_output.avi",
cv2.VideoWriter_fourcc(*'mp4v'),
int(cap.get(5)),
(int(cap.get(3)), int(cap.get(4))))
fps,
(w, h))
# Init heatmap
heatmap_obj = heatmap.Heatmap()
heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA ,
imw=cap.get(4), # should same as cap height
imh=cap.get(3), # should same as cap width
heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA,
imw=w,
imh=h,
view_img=True,
shape="circle")
@ -90,20 +91,21 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
model = YOLO("yolov8n.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")
assert cap.isOpened(), "Error reading video file"
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
# Video writer
video_writer = cv2.VideoWriter("heatmap_output.avi",
cv2.VideoWriter_fourcc(*'mp4v'),
int(cap.get(5)),
(int(cap.get(3)), int(cap.get(4))))
fps,
(w, h))
line_points = [(256, 409), (694, 532)] # line for object counting
# Init heatmap
heatmap_obj = heatmap.Heatmap()
heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA ,
imw=cap.get(4), # should same as cap height
imh=cap.get(3), # should same as cap width
heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA,
imw=w,
imh=h,
view_img=True,
shape="circle",
count_reg_pts=line_points)
@ -132,21 +134,22 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
model = YOLO("yolov8n.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")
assert cap.isOpened(), "Error reading video file"
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
# Video writer
video_writer = cv2.VideoWriter("heatmap_output.avi",
cv2.VideoWriter_fourcc(*'mp4v'),
int(cap.get(5)),
(int(cap.get(3)), int(cap.get(4))))
fps,
(w, h))
# Define region points
region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
# Init heatmap
heatmap_obj = heatmap.Heatmap()
heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA ,
imw=cap.get(4), # should same as cap height
imh=cap.get(3), # should same as cap width
heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA,
imw=w,
imh=h,
view_img=True,
shape="circle",
count_reg_pts=region_points)
@ -178,7 +181,7 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
# Heatmap Init
heatmap_obj = heatmap.Heatmap()
heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA ,
heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA,
imw=im0.shape[0], # should same as im0 width
imh=im0.shape[1], # should same as im0 height
view_img=True,
@ -199,20 +202,21 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
model = YOLO("yolov8n.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")
assert cap.isOpened(), "Error reading video file"
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
# Video writer
video_writer = cv2.VideoWriter("heatmap_output.avi",
cv2.VideoWriter_fourcc(*'mp4v'),
int(cap.get(5)),
(int(cap.get(3)), int(cap.get(4))))
fps,
(w, h))
classes_for_heatmap = [0, 2] # classes for heatmap
# Init heatmap
heatmap_obj = heatmap.Heatmap()
heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA ,
imw=cap.get(4), # should same as cap height
imh=cap.get(3), # should same as cap width
heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA,
imw=w,
imh=h,
view_img=True,
shape="circle")

10
docs/en/guides/instance-segmentation-and-tracking.md
View file

@ -34,10 +34,9 @@ There are two types of instance segmentation tracking available in the Ultralyti
model = YOLO("yolov8n-seg.pt")
names = model.model.names
cap = cv2.VideoCapture("path/to/video/file.mp4")
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
out = cv2.VideoWriter('instance-segmentation.avi',
cv2.VideoWriter_fourcc(*'MJPG'),
30, (int(cap.get(3)), int(cap.get(4))))
out = cv2.VideoWriter('instance-segmentation.avi', cv2.VideoWriter_fourcc(*'MJPG'), fps, (w, h))
while True:
ret, im0 = cap.read()
@ -80,10 +79,9 @@ There are two types of instance segmentation tracking available in the Ultralyti
model = YOLO("yolov8n-seg.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
out = cv2.VideoWriter('instance-segmentation-object-tracking.avi',
cv2.VideoWriter_fourcc(*'MJPG'),
30, (int(cap.get(3)), int(cap.get(4))))
out = cv2.VideoWriter('instance-segmentation-object-tracking.avi', cv2.VideoWriter_fourcc(*'MJPG'), fps, (w, h))
while True:
ret, im0 = cap.read()

17
docs/en/guides/object-counting.md
View file

@ -45,6 +45,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
model = YOLO("yolov8n.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")
assert cap.isOpened(), "Error reading video file"
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
# Define region points
region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
@ -52,8 +53,8 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
# Video writer
video_writer = cv2.VideoWriter("object_counting_output.avi",
cv2.VideoWriter_fourcc(*'mp4v'),
int(cap.get(5)),
(int(cap.get(3)), int(cap.get(4))))
fps,
(w, h))
# Init Object Counter
counter = object_counter.ObjectCounter()
@ -87,6 +88,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
model = YOLO("yolov8n.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")
assert cap.isOpened(), "Error reading video file"
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
# Define line points
line_points = [(20, 400), (1080, 400)]
@ -94,8 +96,8 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
# Video writer
video_writer = cv2.VideoWriter("object_counting_output.avi",
cv2.VideoWriter_fourcc(*'mp4v'),
int(cap.get(5)),
(int(cap.get(3)), int(cap.get(4))))
fps,
(w, h))
# Init Object Counter
counter = object_counter.ObjectCounter()
@ -128,6 +130,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
model = YOLO("yolov8n.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")
assert cap.isOpened(), "Error reading video file"
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
line_points = [(20, 400), (1080, 400)] # line or region points
classes_to_count = [0, 2] # person and car classes for count
@ -135,8 +138,8 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
# Video writer
video_writer = cv2.VideoWriter("object_counting_output.avi",
cv2.VideoWriter_fourcc(*'mp4v'),
int(cap.get(5)),
(int(cap.get(3)), int(cap.get(4))))
fps,
(w, h))
# Init Object Counter
counter = object_counter.ObjectCounter()
@ -170,6 +173,8 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
| Name | Type | Default | Description |
|---------------------|-------------|----------------------------|-----------------------------------------------|
| view_img | `bool` | `False` | Display frames with counts |
| view_in_counts | `bool` | `True` | Display incounts only on video frame |
| view_out_counts | `bool` | `True` | Display outcounts only on video frame |
| 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 |

5
docs/en/guides/speed-estimation.md
View file

@ -36,12 +36,13 @@ Speed estimation is the process of calculating the rate of movement of an object
cap = cv2.VideoCapture("path/to/video/file.mp4")
assert cap.isOpened(), "Error reading video file"
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
# Video writer
video_writer = cv2.VideoWriter("speed_estimation.avi",
cv2.VideoWriter_fourcc(*'mp4v'),
int(cap.get(5)),
(int(cap.get(3)), int(cap.get(4))))
fps,
(w, h))
line_pts = [(0, 360), (1280, 360)]

12
docs/en/guides/vision-eye.md
View file

@ -28,11 +28,11 @@ keywords: Ultralytics, YOLOv8, Object Detection, Object Tracking, IDetection, Vi
model = YOLO("yolov8n.pt")
names = model.model.names
cap = cv2.VideoCapture("path/to/video/file.mp4")
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
out = cv2.VideoWriter('visioneye-pinpoint.avi', cv2.VideoWriter_fourcc(*'MJPG'),
30, (int(cap.get(3)), int(cap.get(4))))
out = cv2.VideoWriter('visioneye-pinpoint.avi', cv2.VideoWriter_fourcc(*'MJPG'), fps, (w, h))
center_point = (-10, int(cap.get(4)))
center_point = (-10, h)
while True:
ret, im0 = cap.read()
@ -69,11 +69,11 @@ keywords: Ultralytics, YOLOv8, Object Detection, Object Tracking, IDetection, Vi
model = YOLO("yolov8n.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
out = cv2.VideoWriter('visioneye-pinpoint.avi', cv2.VideoWriter_fourcc(*'MJPG'),
30, (int(cap.get(3)), int(cap.get(4))))
out = cv2.VideoWriter('visioneye-pinpoint.avi', cv2.VideoWriter_fourcc(*'MJPG'), fps, (w, h))
center_point = (-10, int(cap.get(4)))
center_point = (-10, h)
while True:
ret, im0 = cap.read()

6
docs/en/guides/workouts-monitoring.md
View file

@ -34,6 +34,7 @@ Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://gi
model = YOLO("yolov8n-pose.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")
assert cap.isOpened(), "Error reading video file"
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
gym_object = ai_gym.AIGym() # init AI GYM module
gym_object.set_args(line_thickness=2,
@ -63,11 +64,12 @@ Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://gi
model = YOLO("yolov8n-pose.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")
assert cap.isOpened(), "Error reading video file"
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
video_writer = cv2.VideoWriter("workouts.avi",
cv2.VideoWriter_fourcc(*'mp4v'),
int(cap.get(5)),
(int(cap.get(3)), int(cap.get(4))))
fps,
(w, h))
gym_object = ai_gym.AIGym() # init AI GYM module
gym_object.set_args(line_thickness=2,