Object detection¶

In [ ]:
!pip install ultralytics

Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
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Installing collected packages: sentry-sdk, thop, ultralytics
Successfully installed sentry-sdk-1.18.0 thop-0.1.1.post2209072238 ultralytics-8.0.61
In [ ]:
from ultralytics import YOLO
In [ ]:
model = YOLO('yolov8n.pt')

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General pipeline¶

Bounding boxes for each cell¶

How to define default boxes:¶

  • Manually (user make it by hands)
  • From train dataset (clusterise training boxes and take most popular)

Standard output per cell¶

Real life case¶

Correction prediction¶

How to choose optimal box¶

In [ ]:
from PIL import Image
!wget -nv "https://www.learnopencv.com/wp-content/uploads/2021/01/person-segmentation.jpeg"

2023-04-03 18:04:24 URL:http://learnopencv.com/wp-content/uploads/2021/01/person-segmentation.jpeg [95431/95431] -> "person-segmentation.jpeg" [1]
In [ ]:
img = Image.open("/content/person-segmentation.jpeg")
plt.imshow(img)
plt.axis("off")
Out[ ]:
(-0.5, 1534.5, 1023.5, -0.5)
In [ ]:
results = model('/content/person-segmentation.jpeg')

image 1/1 /content/person-segmentation.jpeg: 448x640 5 persons, 1 dog, 241.2ms
Speed: 1.1ms preprocess, 241.2ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 640)
In [ ]:
names = results[0].names
In [ ]:
labels = results[0].boxes.cls
labels_name = [names[label.item()] for label in labels]
labels_name
Out[ ]:
['person', 'person', 'person', 'dog', 'person', 'person']
In [ ]:
import matplotlib.pyplot as plt
%matplotlib inline
In [ ]:
fig, ax = plt.subplots()
ax.imshow(img)
for label_name,box in zip(labels_name,results[0].boxes.xywh):
  x,y,w,h = box
  x = x - w/2
  y = y - h/2
  c = "r" if label_name == "person" else "b"
  rect = plt.Rectangle((x,y), width=w, height=h, linewidth=2, edgecolor=c, facecolor='none')
  ax.add_patch(rect)
for label_name,box in zip(labels_name,results[0].boxes.xywh):
  x,y,w,h = box
  x = x - w/2
  y = y - h/2 - 20
  plt.text(x,y,label_name,fontsize=12,fontweight="bold")
plt.axis("off")
plt.show()
In [ ]: