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PyCharm Code and Docs Inspect fixes v1 (#18461)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Ultralytics Assistant <135830346+UltralyticsAssistant@users.noreply.github.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Muhammad Rizwan Munawar 2025-01-03 01:16:18 +05:00 • committed by GitHub
parent 126867e355
commit 7f1a50e893
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26 changed files with 90 additions and 91 deletions
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4
examples/YOLOv8-ONNXRuntime-Rust/README.md
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@ -87,13 +87,13 @@ cargo run --release -- --cuda --device_id 0 --model <MODEL> --source <SOURCE>
Set `--batch` to do multi-batch-size inference.
If you're using `--trt`, you can also set `--batch-min` and `--batch-max` to explicitly specify min/max/opt batch for dynamic batch input.(https://onnxruntime.ai/docs/execution-providers/TensorRT-ExecutionProvider.html#explicit-shape-range-for-dynamic-shape-input).(Note that the ONNX model should exported with dynamic shapes)
If you're using `--trt`, you can also set `--batch-min` and `--batch-max` to explicitly specify min/max/opt batch for dynamic batch input.(https://onnxruntime.ai/docs/execution-providers/TensorRT-ExecutionProvider.html#explicit-shape-range-for-dynamic-shape-input).(Note that the ONNX model should be exported with dynamic shapes.)
```bash
cargo run --release -- --cuda --batch 2 --model <MODEL> --source <SOURCE>
```
Set `--height` and `--width` to do dynamic image size inference. (Note that the ONNX model should exported with dynamic shapes)
Set `--height` and `--width` to do dynamic image size inference. (Note that the ONNX model should be exported with dynamic shapes.)
```bash
cargo run --release -- --cuda --width 480 --height 640 --model <MODEL> --source <SOURCE>

2
examples/YOLOv8-Region-Counter/readme.md
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@ -80,7 +80,7 @@ Region counting is a computational method utilized to ascertain the quantity of
**2. Is Friendly Region Plotting Supported by the Region Counter?**
The Region Counter offers the capability to create regions in various formats, such as polygons and rectangles. You have the flexibility to modify region attributes, including coordinates, colors, and other details, as demonstrated in the following code:
The Region Counting offers the capability to create regions in various formats, such as polygons and rectangles. You have the flexibility to modify region attributes, including coordinates, colors, and other details, as demonstrated in the following code:
```python
from shapely.geometry import Polygon

4
examples/YOLOv8-Region-Counter/yolov8_region_counter.py
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@ -185,7 +185,7 @@ def run(
region_color = region["region_color"]
region_text_color = region["text_color"]
polygon_coords = np.array(region["polygon"].exterior.coords, dtype=np.int32)
polygon_coordinates = np.array(region["polygon"].exterior.coords, dtype=np.int32)
centroid_x, centroid_y = int(region["polygon"].centroid.x), int(region["polygon"].centroid.y)
text_size, _ = cv2.getTextSize(
@ -203,7 +203,7 @@ def run(
cv2.putText(
frame, region_label, (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, 0.7, region_text_color, line_thickness
)
cv2.polylines(frame, [polygon_coords], isClosed=True, color=region_color, thickness=region_thickness)
cv2.polylines(frame, [polygon_coordinates], isClosed=True, color=region_color, thickness=region_thickness)
if view_img:
if vid_frame_count == 1: