added kaggle comp solution for facial keypoint

ML/Kaggles/Facial Keypoint Detection Competition/dataset.py

@@ -0,0 +1,50 @@
+import pandas as pd
+import numpy as np
+import config
+import matplotlib.pyplot as plt
+from torch.utils.data import DataLoader, Dataset
+
+
+class FacialKeypointDataset(Dataset):
+    def __init__(self, csv_file, train=True, transform=None):
+        super().__init__()
+        self.data = pd.read_csv(csv_file)
+        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']
+        self.transform = transform
+        self.train = train
+
+    def __len__(self):
+        return self.data.shape[0]
+
+    def __getitem__(self, index):
+        if self.train:
+            image = np.array(self.data.iloc[index, 30].split()).astype(np.float32)
+            labels = np.array(self.data.iloc[index, :30].tolist())
+            labels[np.isnan(labels)] = -1
+        else:
+            image = np.array(self.data.iloc[index, 1].split()).astype(np.float32)
+            labels = np.zeros(30)
+
+        ignore_indices = labels == -1
+        labels = labels.reshape(15, 2)
+
+        if self.transform:
+            image = np.repeat(image.reshape(96, 96, 1), 3, 2).astype(np.uint8)
+            augmentations = self.transform(image=image, keypoints=labels)
+            image = augmentations["image"]
+            labels = augmentations["keypoints"]
+
+        labels = np.array(labels).reshape(-1)
+        labels[ignore_indices] = -1
+
+        return image, labels.astype(np.float32)
+
+
+if __name__ == "__main__":
+    ds = FacialKeypointDataset(csv_file="data/train_4.csv", train=True, transform=config.train_transforms)
+    loader = DataLoader(ds, batch_size=1, shuffle=True, num_workers=0)
+
+    for idx, (x, y) in enumerate(loader):
+        plt.imshow(x[0][0].detach().cpu().numpy(), cmap='gray')
+        plt.plot(y[0][0::2].detach().cpu().numpy(), y[0][1::2].detach().cpu().numpy(), "go")
+        plt.show()