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https://github.com/aladdinpersson/Machine-Learning-Collection.git
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51 lines
2.5 KiB
Python
51 lines
2.5 KiB
Python
import pandas as pd
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import numpy as np
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import config
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import matplotlib.pyplot as plt
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from torch.utils.data import DataLoader, Dataset
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class FacialKeypointDataset(Dataset):
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def __init__(self, csv_file, train=True, transform=None):
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super().__init__()
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self.data = pd.read_csv(csv_file)
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self.category_names = ['left_eye_center_x', 'left_eye_center_y', 'right_eye_center_x', 'right_eye_center_y', 'left_eye_inner_corner_x', 'left_eye_inner_corner_y', 'left_eye_outer_corner_x', 'left_eye_outer_corner_y', 'right_eye_inner_corner_x', 'right_eye_inner_corner_y', 'right_eye_outer_corner_x', 'right_eye_outer_corner_y', 'left_eyebrow_inner_end_x', 'left_eyebrow_inner_end_y', 'left_eyebrow_outer_end_x', 'left_eyebrow_outer_end_y', 'right_eyebrow_inner_end_x', 'right_eyebrow_inner_end_y', 'right_eyebrow_outer_end_x', 'right_eyebrow_outer_end_y', 'nose_tip_x', 'nose_tip_y', 'mouth_left_corner_x', 'mouth_left_corner_y', 'mouth_right_corner_x', 'mouth_right_corner_y', 'mouth_center_top_lip_x', 'mouth_center_top_lip_y', 'mouth_center_bottom_lip_x', 'mouth_center_bottom_lip_y']
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self.transform = transform
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self.train = train
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def __len__(self):
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return self.data.shape[0]
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def __getitem__(self, index):
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if self.train:
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image = np.array(self.data.iloc[index, 30].split()).astype(np.float32)
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labels = np.array(self.data.iloc[index, :30].tolist())
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labels[np.isnan(labels)] = -1
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else:
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image = np.array(self.data.iloc[index, 1].split()).astype(np.float32)
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labels = np.zeros(30)
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ignore_indices = labels == -1
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labels = labels.reshape(15, 2)
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if self.transform:
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image = np.repeat(image.reshape(96, 96, 1), 3, 2).astype(np.uint8)
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augmentations = self.transform(image=image, keypoints=labels)
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image = augmentations["image"]
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labels = augmentations["keypoints"]
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labels = np.array(labels).reshape(-1)
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labels[ignore_indices] = -1
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return image, labels.astype(np.float32)
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if __name__ == "__main__":
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ds = FacialKeypointDataset(csv_file="data/train_4.csv", train=True, transform=config.train_transforms)
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loader = DataLoader(ds, batch_size=1, shuffle=True, num_workers=0)
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for idx, (x, y) in enumerate(loader):
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plt.imshow(x[0][0].detach().cpu().numpy(), cmap='gray')
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plt.plot(y[0][0::2].detach().cpu().numpy(), y[0][1::2].detach().cpu().numpy(), "go")
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plt.show()
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