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91 lines
3.0 KiB
Python
Executable File
91 lines
3.0 KiB
Python
Executable File
"""
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Creates a Pytorch dataset to load the Pascal VOC dataset
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"""
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import torch
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import os
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import pandas as pd
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from PIL import Image
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class VOCDataset(torch.utils.data.Dataset):
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def __init__(
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self, csv_file, img_dir, label_dir, S=7, B=2, C=20, transform=None,
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):
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self.annotations = pd.read_csv(csv_file)
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self.img_dir = img_dir
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self.label_dir = label_dir
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self.transform = transform
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self.S = S
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self.B = B
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self.C = C
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def __len__(self):
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return len(self.annotations)
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def __getitem__(self, index):
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label_path = os.path.join(self.label_dir, self.annotations.iloc[index, 1])
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boxes = []
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with open(label_path) as f:
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for label in f.readlines():
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class_label, x, y, width, height = [
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float(x) if float(x) != int(float(x)) else int(x)
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for x in label.replace("\n", "").split()
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]
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boxes.append([class_label, x, y, width, height])
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img_path = os.path.join(self.img_dir, self.annotations.iloc[index, 0])
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image = Image.open(img_path)
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boxes = torch.tensor(boxes)
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if self.transform:
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# image = self.transform(image)
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image, boxes = self.transform(image, boxes)
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# Convert To Cells
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label_matrix = torch.zeros((self.S, self.S, self.C + 5 * self.B))
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for box in boxes:
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class_label, x, y, width, height = box.tolist()
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class_label = int(class_label)
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# i,j represents the cell row and cell column
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i, j = int(self.S * y), int(self.S * x)
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x_cell, y_cell = self.S * x - j, self.S * y - i
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"""
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Calculating the width and height of cell of bounding box,
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relative to the cell is done by the following, with
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width as the example:
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width_pixels = (width*self.image_width)
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cell_pixels = (self.image_width)
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Then to find the width relative to the cell is simply:
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width_pixels/cell_pixels, simplification leads to the
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formulas below.
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"""
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width_cell, height_cell = (
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width * self.S,
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height * self.S,
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)
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# If no object already found for specific cell i,j
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# Note: This means we restrict to ONE object
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# per cell!
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if label_matrix[i, j, 20] == 0:
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# Set that there exists an object
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label_matrix[i, j, 20] = 1
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# Box coordinates
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box_coordinates = torch.tensor(
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[x_cell, y_cell, width_cell, height_cell]
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)
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label_matrix[i, j, 21:25] = box_coordinates
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# Set one hot encoding for class_label
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label_matrix[i, j, class_label] = 1
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return image, label_matrix
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