Initial media depth project backup

Depth-Anything-V1-main/metric_depth/zoedepth/data/ibims.py

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+# MIT License
+
+# Copyright (c) 2022 Intelligent Systems Lab Org
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+# File author: Shariq Farooq Bhat
+
+import os
+
+import numpy as np
+import torch
+from PIL import Image
+from torch.utils.data import DataLoader, Dataset
+from torchvision import transforms as T
+
+
+class iBims(Dataset):
+    def __init__(self, config):
+        root_folder = config.ibims_root
+        with open(os.path.join(root_folder, "imagelist.txt"), 'r') as f:
+            imglist = f.read().split()
+
+        samples = []
+        for basename in imglist:
+            img_path = os.path.join(root_folder, 'rgb', basename + ".png")
+            depth_path = os.path.join(root_folder, 'depth', basename + ".png")
+            valid_mask_path = os.path.join(
+                root_folder, 'mask_invalid', basename+".png")
+            transp_mask_path = os.path.join(
+                root_folder, 'mask_transp', basename+".png")
+
+            samples.append(
+                (img_path, depth_path, valid_mask_path, transp_mask_path))
+
+        self.samples = samples
+        # self.normalize = T.Normalize(
+        #     mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        self.normalize = lambda x : x
+
+    def __getitem__(self, idx):
+        img_path, depth_path, valid_mask_path, transp_mask_path = self.samples[idx]
+
+        img = np.asarray(Image.open(img_path), dtype=np.float32) / 255.0
+        depth = np.asarray(Image.open(depth_path),
+                           dtype=np.uint16).astype('float')*50.0/65535
+
+        mask_valid = np.asarray(Image.open(valid_mask_path))
+        mask_transp = np.asarray(Image.open(transp_mask_path))
+
+        # depth = depth * mask_valid * mask_transp
+        depth = np.where(mask_valid * mask_transp, depth, -1)
+
+        img = torch.from_numpy(img).permute(2, 0, 1)
+        img = self.normalize(img)
+        depth = torch.from_numpy(depth).unsqueeze(0)
+        return dict(image=img, depth=depth, image_path=img_path, depth_path=depth_path, dataset='ibims')
+
+    def __len__(self):
+        return len(self.samples)
+
+
+def get_ibims_loader(config, batch_size=1, **kwargs):
+    dataloader = DataLoader(iBims(config), batch_size=batch_size, **kwargs)
+    return dataloader