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Add `distance calculation` feature in `vision-eye` (#8616)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Muhammad Rizwan Munawar <chr043416@gmail.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
pull/8619/head
Glenn Jocher 12 months ago committed by GitHub
parent 36408c974c
commit 906b8d31dc
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6 changed files with 94 additions and 5 deletions
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  1. 65
      docs/en/guides/vision-eye.md
  2. 4
      docs/en/reference/models/yolo/model.md
  3. 20
      docs/en/reference/nn/modules/block.md
  4. 4
      docs/en/reference/nn/modules/head.md
  5. 4
      docs/en/reference/nn/tasks.md
  6. 2
      ultralytics/engine/exporter.py

65
docs/en/guides/vision-eye.md
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@ -12,10 +12,10 @@ keywords: Ultralytics, YOLOv8, Object Detection, Object Tracking, IDetection, Vi
## Samples
| VisionEye View | VisionEye View With Object Tracking |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| ![VisionEye View Object Mapping using Ultralytics YOLOv8](https://github.com/RizwanMunawar/ultralytics/assets/62513924/7d593acc-2e37-41b0-ad0e-92b4ffae6647) | ![VisionEye View Object Mapping with Object Tracking using Ultralytics YOLOv8](https://github.com/RizwanMunawar/ultralytics/assets/62513924/fcd85952-390f-451e-8fb0-b82e943af89c) |
| VisionEye View Object Mapping using Ultralytics YOLOv8 | VisionEye View Object Mapping with Object Tracking using Ultralytics YOLOv8 |
| VisionEye View | VisionEye View With Object Tracking | VisionEye View With Distance Calculation |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| ![VisionEye View Object Mapping using Ultralytics YOLOv8](https://github.com/RizwanMunawar/ultralytics/assets/62513924/7d593acc-2e37-41b0-ad0e-92b4ffae6647) | ![VisionEye View Object Mapping with Object Tracking using Ultralytics YOLOv8](https://github.com/RizwanMunawar/ultralytics/assets/62513924/fcd85952-390f-451e-8fb0-b82e943af89c) | ![VisionEye View with Distance Calculation using Ultralytics YOLOv8](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/18c4dafe-a22e-4fa9-a7d4-2bb293562a95) |
| VisionEye View Object Mapping using Ultralytics YOLOv8 | VisionEye View Object Mapping with Object Tracking using Ultralytics YOLOv8 | VisionEye View with Distance Calculation using Ultralytics YOLOv8 |
!!! Example "VisionEye Object Mapping using YOLOv8"
@ -105,6 +105,63 @@ keywords: Ultralytics, YOLOv8, Object Detection, Object Tracking, IDetection, Vi
cap.release()
cv2.destroyAllWindows()
```
=== "VisionEye with Distance Calculation"
```python
import cv2
import math
from ultralytics import YOLO
from ultralytics.utils.plotting import Annotator, colors
model = YOLO("yolov8s.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-distance-calculation.avi', cv2.VideoWriter_fourcc(*'MJPG'), fps, (w, h))
center_point = (0, h)
pixel_per_meter = 10
txt_color, txt_background, bbox_clr = ((0, 0, 0), (255, 255, 255), (255, 0, 255))
while True:
ret, im0 = cap.read()
if not ret:
print("Video frame is empty or video processing has been successfully completed.")
break
annotator = Annotator(im0, line_width=2)
results = model.track(im0, persist=True)
boxes = results[0].boxes.xyxy.cpu()
if results[0].boxes.id is not None:
track_ids = results[0].boxes.id.int().cpu().tolist()
for box, track_id in zip(boxes, track_ids):
annotator.box_label(box, label=str(track_id), color=bbox_clr)
annotator.visioneye(box, center_point)
x1, y1 = int((box[0] + box[2]) // 2), int((box[1] + box[3]) // 2) # Bounding box centroid
distance = (math.sqrt((x1 - center_point[0]) ** 2 + (y1 - center_point[1]) ** 2))/pixel_per_meter
text_size, _ = cv2.getTextSize(f"Distance: {distance:.2f} m", cv2.FONT_HERSHEY_SIMPLEX,1.2, 3)
cv2.rectangle(im0, (x1, y1 - text_size[1] - 10),(x1 + text_size[0] + 10, y1), txt_background, -1)
cv2.putText(im0, f"Distance: {distance:.2f} m",(x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 1.2,txt_color, 3)
out.write(im0)
cv2.imshow("visioneye-distance-calculation", im0)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
out.release()
cap.release()
cv2.destroyAllWindows()
```
### `visioneye` Arguments

4
docs/en/reference/models/yolo/model.md
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@ -14,3 +14,7 @@ keywords: Ultralytics YOLO, YOLO, YOLO model, Model Training, Machine Learning,
## ::: ultralytics.models.yolo.model.YOLO
<br><br>
## ::: ultralytics.models.yolo.model.YOLOWorld
<br><br>

20
docs/en/reference/nn/modules/block.md
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@ -86,3 +86,23 @@ keywords: YOLO, Ultralytics, neural network, nn.modules.block, Proto, HGBlock, S
## ::: ultralytics.nn.modules.block.ResNetLayer
<br><br>
## ::: ultralytics.nn.modules.block.MaxSigmoidAttnBlock
<br><br>
## ::: ultralytics.nn.modules.block.C2fAttn
<br><br>
## ::: ultralytics.nn.modules.block.ImagePoolingAttn
<br><br>
## ::: ultralytics.nn.modules.block.ContrastiveHead
<br><br>
## ::: ultralytics.nn.modules.block.BNContrastiveHead
<br><br>

4
docs/en/reference/nn/modules/head.md
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@ -31,6 +31,10 @@ keywords: Ultralytics, YOLO, Detection, Pose, RTDETRDecoder, nn modules, guides
<br><br>
## ::: ultralytics.nn.modules.head.WorldDetect
<br><br>
## ::: ultralytics.nn.modules.head.RTDETRDecoder
<br><br>

4
docs/en/reference/nn/tasks.md
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@ -39,6 +39,10 @@ keywords: Ultralytics, YOLO, nn tasks, DetectionModel, PoseModel, RTDETRDetectio
<br><br>
## ::: ultralytics.nn.tasks.WorldModel
<br><br>
## ::: ultralytics.nn.tasks.Ensemble
<br><br>

2
ultralytics/engine/exporter.py
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@ -107,7 +107,7 @@ def export_formats():
["TensorFlow Edge TPU", "edgetpu", "_edgetpu.tflite", True, False],
["TensorFlow.js", "tfjs", "_web_model", True, False],
["PaddlePaddle", "paddle", "_paddle_model", True, True],
["ncnn", "ncnn", "_ncnn_model", True, True],
["NCNN", "ncnn", "_ncnn_model", True, True],
]
return pandas.DataFrame(x, columns=["Format", "Argument", "Suffix", "CPU", "GPU"])

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