Update README.md
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README.md
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@@ -62,16 +62,13 @@ import matplotlib.pyplot as plt
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class ARC:
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def __init__(self):
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bag = r'Class_A_austral.bag' # path of the bag file
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self.start_frame = 0 # start from the this frame to allow the sensor to adjust
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# ROI coordinates are determined from the depth images (checked visually and fixed)
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self.x1_roi, self.x2_roi = 250, 1280
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self.y1_roi, self.y2_roi = 0, 470
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self.delta = 5 # to discard steaks occluded along the borders of the image
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self.area_tresh = 80
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self.bag = bag
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self.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
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# load and run model
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dist = math.sqrt(math.pow(point1[0] - point2[0], 2) + math.pow(point1[1] - point2[1],2) + math.pow(point1[2] - point2[2], 2))
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return round(dist*100, 2) # multiply by 100 to convert m to cm
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```
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if __name__ == '__main__':
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ARC().video()
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## 🗂️ Data Instances
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<figure style="display:flex; gap:10px; flex-wrap:wrap; justify-content:center;">
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class ARC:
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def __init__(self):
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bag = r'Class_A_austral.bag' # path of the bag file
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self.start_frame = 0 # start from the this frame to allow the sensor to adjust
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# ROI coordinates are determined from the depth images (checked visually and fixed)
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self.x1_roi, self.x2_roi = 250, 1280
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self.y1_roi, self.y2_roi = 0, 470
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self.delta = 5 # to discard steaks occluded along the borders of the image
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self.area_tresh = 80
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self.bag = bag
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self.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
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# load and run model
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dist = math.sqrt(math.pow(point1[0] - point2[0], 2) + math.pow(point1[1] - point2[1],2) + math.pow(point1[2] - point2[2], 2))
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return round(dist*100, 2) # multiply by 100 to convert m to cm
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if __name__ == '__main__':
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ARC().video()
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```
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## 🗂️ Data Instances
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<figure style="display:flex; gap:10px; flex-wrap:wrap; justify-content:center;">
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