| Name | out-of-the-box JSON |
| Version |
1.3.0
JSON |
| download |
| home_page | None |
| Summary | "I like to get into boxes." |
| upload_time | 2024-09-20 21:18:27 |
| maintainer | None |
| docs_url | None |
| author | PypayaTech |
| requires_python | <4.0,>=3.8 |
| license | MIT |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# Out of the Box
"I like to get into boxes."
A comprehensive toolkit for handling bounding boxes in various formats, including a GUI for ROI selection. For those who like to think out of the box.
## Features
- Bounding box utilities:
- Support for VOC, COCO, and YOLO formats
- Conversion between formats
- Area calculation
- Intersection over Union (IoU) calculation
- Drawing bounding boxes on images
- Roy app:
- Load any image file supported by PIL
- Draw and adjust bounding box interactively
- Display coordinates in both pixel and normalized formats
- Real-time updates of bounding box parameters
## Installation
```
pip install out-of-the-box
```
## Usage
### Bounding box utilities
```python
img_shape = (500, 500)
img = np.zeros((*img_shape, 3), dtype=np.uint8)
# Create bounding boxes in different formats
voc_box = VOCBox(100, 100, 300, 300)
coco_box = COCOBox(100, 100, 200, 200)
yolo_box = YOLOBox(0.4, 0.4, 0.2, 0.2)
bb_voc = BoundingBox(voc_box, img_shape)
bb_coco = BoundingBox(coco_box, img_shape)
bb_yolo = BoundingBox(yolo_box, img_shape)
# Print bounding boxes
print("VOC bounding box:", bb_voc)
print("COCO bounding box:", bb_coco)
print("YOLO bounding box:", bb_yolo)
# Convert between formats
print("\nFormat conversions:")
print("VOC to COCO:", bb_voc.to_coco())
print("COCO to YOLO:", bb_coco.to_yolo())
print("YOLO to VOC:", bb_yolo.to_voc())
# Normalized and pixel coordinates
print("\nNormalized and pixel coordinates:")
print("VOC (normalized):", bb_voc.to_voc(normalized=True))
print("COCO (pixel):", bb_yolo.to_coco(normalized=False))
# Area calculation
print("\nAreas:")
print("VOC box area:", bb_voc.area)
print("COCO box area:", bb_coco.area)
print("YOLO box area:", bb_yolo.area)
# Intersection and Union
intersection = BoundingBox.intersection(bb_voc, bb_coco)
union = BoundingBox.union(bb_voc, bb_coco)
print("\nIntersection and Union:")
print(f"Intersection between VOC and COCO: {intersection:.2f}")
print(f"Union between VOC and COCO: {union:.2f}")
# IoU calculation
iou = BoundingBox.iou(bb_voc, bb_coco)
print(f"IoU between VOC and COCO boxes: {iou:.2f}")
# Percentage inside
percentage = bb_coco.percentage_inside(bb_voc)
print(f"\nPercentage of COCO box inside VOC box: {percentage:.2%}")
# Center point
print("\nCenter points:")
print("VOC box center:", bb_voc.center)
print("COCO box center:", bb_coco.center)
print("YOLO box center:", bb_yolo.center)
# Contains point
test_point = (200, 200)
print(f"\nPoint {test_point} contained in:")
print("VOC box:", bb_voc.contains_point(test_point))
print("COCO box:", bb_coco.contains_point(test_point))
print("YOLO box:", bb_yolo.contains_point(test_point))
# Overlap percentage
overlap = bb_coco.overlap_percentage(bb_voc)
print(f"\nOverlap percentage of COCO box with VOC box: {overlap:.2%}")
# Draw the bounding boxes on the image
img = bb_voc.draw_on_image(img, color=COLOR_RED, thickness=2, label="VOC")
img = bb_coco.draw_on_image(img, color=COLOR_GREEN, thickness=2, label="COCO")
img = bb_yolo.draw_on_image(img, color=COLOR_BLUE, thickness=2, label="YOLO")
# Display the image
cv2.imshow("Image with bounding boxes", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
### Roy App
To start the ROI selector GUI:
```bash
roy
```
In the GUI:
1. Click "Open Image" to load an image.
2. Draw a bounding box by clicking and dragging on the image.
3. Adjust the box using the entry fields or by dragging.
4. View both pixel and normalized coordinates.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
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"description": "# Out of the Box\n\n\"I like to get into boxes.\"\n\nA comprehensive toolkit for handling bounding boxes in various formats, including a GUI for ROI selection. For those who like to think out of the box.\n\n\n\n## Features\n\n- Bounding box utilities:\n - Support for VOC, COCO, and YOLO formats\n - Conversion between formats\n - Area calculation\n - Intersection over Union (IoU) calculation\n - Drawing bounding boxes on images\n\n- Roy app:\n - Load any image file supported by PIL\n - Draw and adjust bounding box interactively\n - Display coordinates in both pixel and normalized formats\n - Real-time updates of bounding box parameters\n\n## Installation\n\n```\npip install out-of-the-box\n```\n\n## Usage\n\n### Bounding box utilities\n\n```python\nimg_shape = (500, 500)\nimg = np.zeros((*img_shape, 3), dtype=np.uint8)\n\n# Create bounding boxes in different formats\nvoc_box = VOCBox(100, 100, 300, 300)\ncoco_box = COCOBox(100, 100, 200, 200)\nyolo_box = YOLOBox(0.4, 0.4, 0.2, 0.2)\n\nbb_voc = BoundingBox(voc_box, img_shape)\nbb_coco = BoundingBox(coco_box, img_shape)\nbb_yolo = BoundingBox(yolo_box, img_shape)\n\n# Print bounding boxes\nprint(\"VOC bounding box:\", bb_voc)\nprint(\"COCO bounding box:\", bb_coco)\nprint(\"YOLO bounding box:\", bb_yolo)\n\n# Convert between formats\nprint(\"\\nFormat conversions:\")\nprint(\"VOC to COCO:\", bb_voc.to_coco())\nprint(\"COCO to YOLO:\", bb_coco.to_yolo())\nprint(\"YOLO to VOC:\", bb_yolo.to_voc())\n\n# Normalized and pixel coordinates\nprint(\"\\nNormalized and pixel coordinates:\")\nprint(\"VOC (normalized):\", bb_voc.to_voc(normalized=True))\nprint(\"COCO (pixel):\", bb_yolo.to_coco(normalized=False))\n\n# Area calculation\nprint(\"\\nAreas:\")\nprint(\"VOC box area:\", bb_voc.area)\nprint(\"COCO box area:\", bb_coco.area)\nprint(\"YOLO box area:\", bb_yolo.area)\n\n# Intersection and Union\nintersection = BoundingBox.intersection(bb_voc, bb_coco)\nunion = BoundingBox.union(bb_voc, bb_coco)\nprint(\"\\nIntersection and Union:\")\nprint(f\"Intersection between VOC and COCO: {intersection:.2f}\")\nprint(f\"Union between VOC and COCO: {union:.2f}\")\n\n# IoU calculation\niou = BoundingBox.iou(bb_voc, bb_coco)\nprint(f\"IoU between VOC and COCO boxes: {iou:.2f}\")\n\n# Percentage inside\npercentage = bb_coco.percentage_inside(bb_voc)\nprint(f\"\\nPercentage of COCO box inside VOC box: {percentage:.2%}\")\n\n# Center point\nprint(\"\\nCenter points:\")\nprint(\"VOC box center:\", bb_voc.center)\nprint(\"COCO box center:\", bb_coco.center)\nprint(\"YOLO box center:\", bb_yolo.center)\n\n# Contains point\ntest_point = (200, 200)\nprint(f\"\\nPoint {test_point} contained in:\")\nprint(\"VOC box:\", bb_voc.contains_point(test_point))\nprint(\"COCO box:\", bb_coco.contains_point(test_point))\nprint(\"YOLO box:\", bb_yolo.contains_point(test_point))\n\n# Overlap percentage\noverlap = bb_coco.overlap_percentage(bb_voc)\nprint(f\"\\nOverlap percentage of COCO box with VOC box: {overlap:.2%}\")\n\n# Draw the bounding boxes on the image\nimg = bb_voc.draw_on_image(img, color=COLOR_RED, thickness=2, label=\"VOC\")\nimg = bb_coco.draw_on_image(img, color=COLOR_GREEN, thickness=2, label=\"COCO\")\nimg = bb_yolo.draw_on_image(img, color=COLOR_BLUE, thickness=2, label=\"YOLO\")\n\n# Display the image\ncv2.imshow(\"Image with bounding boxes\", img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n```\n\n### Roy App\n\n\n\nTo start the ROI selector GUI:\n\n```bash\nroy\n```\n\nIn the GUI:\n\n1. Click \"Open Image\" to load an image.\n2. Draw a bounding box by clicking and dragging on the image.\n3. Adjust the box using the entry fields or by dragging.\n4. View both pixel and normalized coordinates.\n\n## License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.\n\n",
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