origin

digit_depth/train/__init__.py

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+from .color2normal_dataset import Color2NormalDataset
+from .mlp_model import MLP

digit_depth/train/color2normal_dataset.py

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+import pandas as pd
+import torch
+from torch.utils.data import Dataset
+
+
+class Color2NormalDataset(Dataset):
+    def __init__(self, csv):
+        self.data = pd.read_csv(csv)
+    def __len__(self):
+        return self.data['X'].count()
+
+    def __getitem__(self,idx):
+        x = self.data['X'][idx]/120
+        y = self.data['Y'][idx]/160
+        r = self.data['R'][idx]/255
+        g = self.data['G'][idx]/255
+        b = self.data['B'][idx]/255
+        nx = self.data['Nx'][idx]/255
+        ny = self.data['Ny'][idx]/255
+        nz= self.data['Nz'][idx]/255
+        return torch.tensor((x, y, r, g, b), dtype=torch.float32), torch.tensor((nx, ny, nz), dtype=torch.float32)

digit_depth/train/mlp_model.py

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+
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class MLP(nn.Module):
+    dropout_p = 0.05
+
+    def __init__(
+            self, input_size=5, output_size=3, hidden_size=32):
+        super().__init__()
+
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.fc2 = nn.Linear(hidden_size, hidden_size)
+        self.fc3 = nn.Linear(hidden_size, hidden_size)
+        self.fc4 = nn.Linear(hidden_size, output_size)
+        self.drop = nn.Dropout(p=self.dropout_p)
+
+    def forward(self, x):
+        x = F.relu(self.fc1(x))
+        x = self.drop(x)
+        x = F.relu(self.fc2(x))
+        x = self.drop(x)
+        x = self.fc3(x)
+        x = self.drop(x)
+        x = self.fc4(x)
+        return x

digit_depth/train/prepost_mlp.py

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+
+import cv2
+import torch
+import numpy as np
+import pandas as pd
+import copy
+seed = 42
+torch.seed = seed
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+
+def preproc_mlp(image) -> torch.Tensor:
+    """ Preprocess image for input to model.
+
+    Args: image: OpenCV image in BGR format
+    Return: tensor of shape (R*C,5) where R=320 and C=240 for DIGIT images
+    5-columns are: X,Y,R,G,B
+
+    """
+    img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    img = img / 255.0
+    xy_coords = np.flip(np.column_stack(np.where(np.all(img >= 0, axis=2))), axis=1)
+    rgb = np.reshape(img, (np.prod(img.shape[:2]), 3))
+    pixel_numbers = np.expand_dims(np.arange(1, xy_coords.shape[0] + 1), axis=1)
+    value_base = np.hstack([pixel_numbers, xy_coords, rgb])
+    df_base = pd.DataFrame(value_base, columns=['pixel_number', 'X', 'Y', 'R', 'G', 'B'])
+    df_base['X'] = df_base['X'] / 240
+    df_base['Y'] = df_base['Y'] / 320
+    del df_base['pixel_number']
+    test_tensor = torch.tensor(df_base[['X', 'Y', 'R', 'G', 'B']].values, dtype=torch.float32).to(device)
+    return test_tensor
+
+
+def post_proc_mlp(model_output: torch.Tensor):
+    """ Postprocess model output to get normal map.
+
+    Args: model_output: torch.Tensor of shape (1,3)
+    Return: two torch.Tensor of shape (1,3)
+
+    """
+    test_np = model_output.reshape(320, 240, 3)
+    normal = copy.deepcopy(test_np)  # surface normal image
+    test_np = torch.tensor(test_np,
+                           dtype=torch.float32)  # convert to torch tensor for later processing in gradient computation
+    test_np = test_np.permute(2, 0, 1)  # swap axes to (3,320,240)
+    test_np = test_np # convert to uint8 for visualization
+    return test_np, normal
+
+