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Simplify Solutions Docs code examples (#17493)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Muhammad Rizwan Munawar 2024-11-12 14:57:00 +05:00 • committed by GitHub
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133
docs/en/guides/analytics.md
View file

@ -45,6 +45,12 @@ This guide provides a comprehensive overview of three fundamental types of [data
# generate the pie chart
yolo solutions analytics analytics_type="pie" show=True
# generate the bar plots
yolo solutions analytics analytics_type="bar" show=True
# generate the area plots
yolo solutions analytics analytics_type="area" show=True
```
=== "Python"
@ -56,9 +62,9 @@ This guide provides a comprehensive overview of three fundamental types of [data
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
out = cv2.VideoWriter(
"ultralytics_analytics.avi",
cv2.VideoWriter_fourcc(*"MJPG"),
@ -66,128 +72,15 @@ This guide provides a comprehensive overview of three fundamental types of [data
(1920, 1080), # This is fixed
)
# Init analytics
analytics = solutions.Analytics(
analytics_type="line",
show=True,
)
frame_count = 0
while cap.isOpened():
success, im0 = cap.read()
if success:
frame_count += 1
im0 = analytics.process_data(im0, frame_count) # update analytics graph every frame
out.write(im0) # write the video file
else:
break
cap.release()
out.release()
cv2.destroyAllWindows()
```
=== "Pie Chart"
```python
import cv2
from ultralytics import solutions
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))
out = cv2.VideoWriter(
"ultralytics_analytics.avi",
cv2.VideoWriter_fourcc(*"MJPG"),
fps,
(1920, 1080), # This is fixed
)
analytics = solutions.Analytics(
analytics_type="pie",
show=True,
)
frame_count = 0
while cap.isOpened():
success, im0 = cap.read()
if success:
frame_count += 1
im0 = analytics.process_data(im0, frame_count) # update analytics graph every frame
out.write(im0) # write the video file
else:
break
cap.release()
out.release()
cv2.destroyAllWindows()
```
=== "Bar Plot"
```python
import cv2
from ultralytics import solutions
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))
out = cv2.VideoWriter(
"ultralytics_analytics.avi",
cv2.VideoWriter_fourcc(*"MJPG"),
fps,
(1920, 1080), # This is fixed
)
analytics = solutions.Analytics(
analytics_type="bar",
show=True,
)
frame_count = 0
while cap.isOpened():
success, im0 = cap.read()
if success:
frame_count += 1
im0 = analytics.process_data(im0, frame_count) # update analytics graph every frame
out.write(im0) # write the video file
else:
break
cap.release()
out.release()
cv2.destroyAllWindows()
```
=== "Area chart"
```python
import cv2
from ultralytics import solutions
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))
out = cv2.VideoWriter(
"ultralytics_analytics.avi",
cv2.VideoWriter_fourcc(*"MJPG"),
fps,
(1920, 1080), # This is fixed
)
analytics = solutions.Analytics(
analytics_type="area",
show=True,
show=True, # Display the output
analytics_type="line", # Pass the analytics type, could be "pie", "bar" or "area".
model="yolo11n.pt", # Path to the YOLO11 model file
# classes=[0, 2], # If you want to count specific classes i.e person and car with COCO pretrained model.
)
# Process video
frame_count = 0
while cap.isOpened():
success, im0 = cap.read()

1
docs/en/guides/distance-calculation.md
View file

@ -55,6 +55,7 @@ Measuring the gap between two objects is known as distance calculation within a
# Init distance-calculation obj
distance = solutions.DistanceCalculation(model="yolo11n.pt", show=True)
# Process video
while cap.isOpened():
success, im0 = cap.read()
if not success:

166
docs/en/guides/heatmaps.md
View file

@ -47,6 +47,9 @@ A heatmap generated with [Ultralytics YOLO11](https://github.com/ultralytics/ult
# Pass a custom colormap
yolo solutions heatmap colormap=cv2.COLORMAP_INFERNO
# Heatmaps + object counting
yolo solutions heatmap region=[(20, 400), (1080, 404), (1080, 360), (20, 360)]
```
=== "Python"
@ -63,161 +66,24 @@ A heatmap generated with [Ultralytics YOLO11](https://github.com/ultralytics/ult
# Video writer
video_writer = cv2.VideoWriter("heatmap_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
# Init heatmap
heatmap = solutions.Heatmap(
show=True,
model="yolo11n.pt",
colormap=cv2.COLORMAP_PARULA,
)
while cap.isOpened():
success, im0 = cap.read()
if not success:
print("Video frame is empty or video processing has been successfully completed.")
break
im0 = heatmap.generate_heatmap(im0)
video_writer.write(im0)
cap.release()
video_writer.release()
cv2.destroyAllWindows()
```
=== "Line Counting"
```python
import cv2
from ultralytics import solutions
cap = cv2.VideoCapture("Path/to/video/file.mp4")
assert cap.isOpened(), "Error reading video file"
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
# Video writer
video_writer = cv2.VideoWriter("heatmap_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
# line for object counting
line_points = [(20, 400), (1080, 404)]
# In case you want to apply object counting + heatmaps, you can pass region points.
# region_points = [(20, 400), (1080, 404)] # Define line points
# region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)] # Define region points
# region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360), (20, 400)] # Define polygon points
# Init heatmap
heatmap = solutions.Heatmap(
show=True,
model="yolo11n.pt",
colormap=cv2.COLORMAP_PARULA,
region=line_points,
)
while cap.isOpened():
success, im0 = cap.read()
if not success:
print("Video frame is empty or video processing has been successfully completed.")
break
im0 = heatmap.generate_heatmap(im0)
video_writer.write(im0)
cap.release()
video_writer.release()
cv2.destroyAllWindows()
```
=== "Polygon Counting"
```python
import cv2
from ultralytics import solutions
cap = cv2.VideoCapture("Path/to/video/file.mp4")
assert cap.isOpened(), "Error reading video file"
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
# Video writer
video_writer = cv2.VideoWriter("heatmap_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
# Define polygon points
region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360), (20, 400)]
# Init heatmap
heatmap = solutions.Heatmap(
show=True,
model="yolo11n.pt",
colormap=cv2.COLORMAP_PARULA,
region=region_points,
)
while cap.isOpened():
success, im0 = cap.read()
if not success:
print("Video frame is empty or video processing has been successfully completed.")
break
im0 = heatmap.generate_heatmap(im0)
video_writer.write(im0)
cap.release()
video_writer.release()
cv2.destroyAllWindows()
```
=== "Region Counting"
```python
import cv2
from ultralytics import solutions
cap = cv2.VideoCapture("Path/to/video/file.mp4")
assert cap.isOpened(), "Error reading video file"
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
# Video writer
video_writer = cv2.VideoWriter("heatmap_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
# Define region points
region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
# Init heatmap
heatmap = solutions.Heatmap(
show=True,
model="yolo11n.pt",
colormap=cv2.COLORMAP_PARULA,
region=region_points,
)
while cap.isOpened():
success, im0 = cap.read()
if not success:
print("Video frame is empty or video processing has been successfully completed.")
break
im0 = heatmap.generate_heatmap(im0)
video_writer.write(im0)
cap.release()
video_writer.release()
cv2.destroyAllWindows()
```
=== "Specific Classes"
```python
import cv2
from ultralytics import solutions
cap = cv2.VideoCapture("Path/to/video/file.mp4")
assert cap.isOpened(), "Error reading video file"
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
# Video writer
video_writer = cv2.VideoWriter("heatmap_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
# Init heatmap
heatmap = solutions.Heatmap(
show=True,
model="yolo11n.pt",
classes=[0, 2],
show=True, # Display the output
model="yolo11n.pt", # Path to the YOLO11 model file
colormap=cv2.COLORMAP_PARULA, # Colormap of heatmap
# region=region_points, # If you want to do object counting with heatmaps, you can pass region_points
# classes=[0, 2], # If you want to generate heatmap for specific classes i.e person and car.
# show_in=True, # Display in counts
# show_out=True, # Display out counts
# line_width=2, # Adjust the line width for bounding boxes and text display
)
# Process video
while cap.isOpened():
success, im0 = cap.read()
if not success:

163
docs/en/guides/object-counting.md
View file

@ -73,165 +73,22 @@ Object counting with [Ultralytics YOLO11](https://github.com/ultralytics/ultraly
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)]
# region_points = [(20, 400), (1080, 400)] # For line counting
region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)] # For rectangle region counting
# region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360), (20, 400)] # For polygon region counting
# Video writer
video_writer = cv2.VideoWriter("object_counting_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
# Init Object Counter
counter = solutions.ObjectCounter(
show=True,
region=region_points,
model="yolo11n.pt",
)
# Process video
while cap.isOpened():
success, im0 = cap.read()
if not success:
print("Video frame is empty or video processing has been successfully completed.")
break
im0 = counter.count(im0)
video_writer.write(im0)
cap.release()
video_writer.release()
cv2.destroyAllWindows()
```
=== "OBB Object Counting"
```python
import cv2
from ultralytics import solutions
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 or region points
line_points = [(20, 400), (1080, 400)]
# Video writer
video_writer = cv2.VideoWriter("object_counting_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
# Init Object Counter
counter = solutions.ObjectCounter(
show=True,
region=line_points,
model="yolo11n-obb.pt",
)
# Process video
while cap.isOpened():
success, im0 = cap.read()
if not success:
print("Video frame is empty or video processing has been successfully completed.")
break
im0 = counter.count(im0)
video_writer.write(im0)
cap.release()
video_writer.release()
cv2.destroyAllWindows()
```
=== "Count in Polygon"
```python
import cv2
from ultralytics import solutions
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), (20, 400)]
# Video writer
video_writer = cv2.VideoWriter("object_counting_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
# Init Object Counter
counter = solutions.ObjectCounter(
show=True,
region=region_points,
model="yolo11n.pt",
)
# Process video
while cap.isOpened():
success, im0 = cap.read()
if not success:
print("Video frame is empty or video processing has been successfully completed.")
break
im0 = counter.count(im0)
video_writer.write(im0)
cap.release()
video_writer.release()
cv2.destroyAllWindows()
```
=== "Count in Line"
```python
import cv2
from ultralytics import solutions
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
line_points = [(20, 400), (1080, 400)]
# Video writer
video_writer = cv2.VideoWriter("object_counting_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
# Init Object Counter
counter = solutions.ObjectCounter(
show=True,
region=line_points,
model="yolo11n.pt",
)
# Process video
while cap.isOpened():
success, im0 = cap.read()
if not success:
print("Video frame is empty or video processing has been successfully completed.")
break
im0 = counter.count(im0)
video_writer.write(im0)
cap.release()
video_writer.release()
cv2.destroyAllWindows()
```
=== "Specific Classes"
```python
import cv2
from ultralytics import solutions
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("object_counting_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
# Init Object Counter
counter = solutions.ObjectCounter(
show=True,
model="yolo11n.pt",
classes=[0, 1],
show=True, # Display the output
region=region_points, # Pass region points
model="yolo11n.pt", # model="yolo11n-obb.pt" for object counting using YOLO11 OBB model.
# classes=[0, 2], # If you want to count specific classes i.e person and car with COCO pretrained model.
# show_in=True, # Display in counts
# show_out=True, # Display out counts
# line_width=2, # Adjust the line width for bounding boxes and text display
)
# Process video

52
docs/en/guides/queue-management.md
View file

@ -60,53 +60,23 @@ Queue management using [Ultralytics YOLO11](https://github.com/ultralytics/ultra
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("queue_management.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
queue_region = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
# Define queue region points
queue_region = [(20, 400), (1080, 404), (1080, 360), (20, 360)] # Define queue region points
# queue_region = [(20, 400), (1080, 404), (1080, 360), (20, 360), (20, 400)] # Define queue polygon points
# Init Queue Manager
queue = solutions.QueueManager(
model="yolo11n.pt",
region=queue_region,
)
while cap.isOpened():
success, im0 = cap.read()
if success:
out = queue.process_queue(im0)
video_writer.write(im0)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
continue
print("Video frame is empty or video processing has been successfully completed.")
break
cap.release()
cv2.destroyAllWindows()
```
=== "Queue Manager Specific Classes"
```python
import cv2
from ultralytics import solutions
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("queue_management.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
queue_region = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
queue = solutions.QueueManager(
model="yolo11n.pt",
classes=3,
show=True, # Display the output
model="yolo11n.pt", # Path to the YOLO11 model file
region=queue_region, # Pass queue region points
# classes=[0, 2], # If you want to count specific classes i.e person and car with COCO pretrained model.
# line_width=2, # Adjust the line width for bounding boxes and text display
)
# Process video
while cap.isOpened():
success, im0 = cap.read()

12
docs/en/guides/speed-estimation.md
View file

@ -61,16 +61,24 @@ keywords: Ultralytics YOLO11, speed estimation, object tracking, computer vision
from ultralytics import solutions
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_management.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
# Define speed region points
speed_region = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
speed = solutions.SpeedEstimator(model="yolo11n.pt", region=speed_region, show=True)
speed = solutions.SpeedEstimator(
show=True, # Display the output
model="yolo11n-pose.pt", # Path to the YOLO11 model file.
region=speed_region, # Pass region points
# classes=[0, 2], # If you want to estimate speed of specific classes.
# line_width=2, # Adjust the line width for bounding boxes and text display
)
# Process video
while cap.isOpened():
success, im0 = cap.read()

12
docs/en/guides/streamlit-live-inference.md
View file

@ -40,6 +40,12 @@ Streamlit makes it simple to build and deploy interactive web applications. Comb
!!! example "Streamlit Application"
=== "CLI"
```bash
yolo streamlit-predict
```
=== "Python"
```python
@ -50,12 +56,6 @@ Streamlit makes it simple to build and deploy interactive web applications. Comb
### Make sure to run the file using command `streamlit run <file-name.py>`
```
=== "CLI"
```bash
yolo streamlit-predict
```
This will launch the Streamlit application in your default web browser. You will see the main title, subtitle, and the sidebar with configuration options. Select your desired YOLO11 model, set the confidence and NMS thresholds, and click the "Start" button to begin the real-time object detection.
You can optionally supply a specific model in Python:

36
docs/en/guides/workouts-monitoring.md
View file

@ -60,40 +60,18 @@ Monitoring workouts through pose estimation with [Ultralytics YOLO11](https://gi
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 = solutions.AIGym(
model="yolo11n-pose.pt",
show=True,
kpts=[6, 8, 10],
)
while cap.isOpened():
success, im0 = cap.read()
if not success:
print("Video frame is empty or video processing has been successfully completed.")
break
im0 = gym.monitor(im0)
cv2.destroyAllWindows()
```
=== "Workouts Monitoring with Save Output"
```python
import cv2
from ultralytics import solutions
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("workouts.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
# Init AIGym
gym = solutions.AIGym(
show=True,
kpts=[6, 8, 10],
show=True, # Display the frame
kpts=[6, 8, 10], # keypoints index of person for monitoring specific exercise, by default it's for pushup
model="yolo11n-pose.pt", # Path to the YOLO11 pose estimation model file
# line_width=2, # Adjust the line width for bounding boxes and text display
)
# Process video
while cap.isOpened():
success, im0 = cap.read()
if not success: