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utils.py

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+from pathlib import Path
+import numpy as np
+import matplotlib.pyplot as plt
+from typing import List
+from cleanvision import Imagelab
+from PIL import Image
+from datasets import Dataset, Features, Image as ImageFeature
+
+
+def images_with_deduplication(data_path):
+    imagelab = Imagelab(data_path=data_path)
+
+    # Automatically check for a predefined list of issues within your dataset
+    imagelab.find_issues({"near_duplicates": {}, "exact_duplicates": {}})
+
+    # load names of all images in the dataset
+    image_paths = list(Path(data_path).rglob("*.png"))
+    image_paths = [str(path.resolve()) for path in image_paths]
+    print(f"Number of images before deduplication: {len(image_paths)}")
+
+    duplicate_sets = imagelab.info["near_duplicates"]["sets"]
+    num_duplicates = sum([len(duplicate_set) - 1 for duplicate_set in duplicate_sets])
+
+    for duplicate_set in duplicate_sets:
+        for i in range(len(duplicate_set)):
+            if i > 0:
+                image_name = duplicate_set[i]
+                del image_paths[image_paths.index(image_name)]
+
+    print(f"Number of images after deduplication: {len(image_paths)}")
+    print(f"Number of images removed: {num_duplicates}")
+
+    return image_paths
+
+
+def find_closest_pair(ref_timestamp, search_dir, threshold_ms=100):
+    search_files = list(search_dir.glob("*"))
+    search_timestamps = [int(f.stem.split("_")[0]) for f in search_files]
+
+    diffs = np.abs(np.array(search_timestamps) - ref_timestamp) / 1e6
+    min_idx = np.argmin(diffs)
+
+    if diffs[min_idx] <= threshold_ms:
+        return str(search_files[min_idx])
+    return None
+
+
+def find_image_groups(
+    base_dir, ref_subdir, search_subdirs: List[str], threshold_ms=100
+):
+    base_path = Path(base_dir)
+    ref_dir = base_path / ref_subdir
+    search_dirs = [base_path / subdir for subdir in search_subdirs]
+
+    # deduplicate images from the reference directory
+    ref_dir_files = images_with_deduplication(ref_dir)
+
+    pairs = []
+    for ref_file in ref_dir_files:
+        ref_ts = int(ref_file.split("/")[-1].split("_")[0])
+        image_group = (ref_file,)
+
+        for search_dir in search_dirs:
+            assert search_dir.exists(), f"{search_dir} does not exist"
+
+            match = find_closest_pair(ref_ts, search_dir, threshold_ms)
+            if match:
+                image_group += (match,)
+            else:
+                image_group += (None,)
+
+        pairs.append(image_group)
+
+    return pairs
+
+
+def visualize_images(image_tuple):
+    n = len(image_tuple)
+    fig, axes = plt.subplots(1, n, figsize=(6 * n, 4))
+
+    if n == 1:
+        axes = [axes]
+
+    for ax, img_path in zip(axes, image_tuple):
+        if img_path is None:
+            ax.axis("off")
+            continue
+
+        img = Image.open(img_path)
+
+        if "DEPTH" in str(img_path):
+            ax.imshow(img, cmap="viridis")
+        elif "THERMAL" in str(img_path):
+            ax.imshow(img, cmap="hot")
+        else:
+            img = Image.open(img_path)
+            ax.imshow(img)
+
+        ax.set_title(img_path.split("/")[-2])
+    plt.show()
+
+
+# prepare the dataset for upload to huggingface
+def create_image_dataset(image_tuples):
+    """
+    Create a HuggingFace dataset from a list of image tuples.
+
+    Args:
+        image_tuples: List of tuples, each containing (color, depth, depth_16bit, thermal, thermal_rgb) image paths
+    """
+
+    features = Features(
+        {
+            "color": ImageFeature(decode=True),
+            "depth": ImageFeature(decode=True),
+            "depth_16bit": ImageFeature(decode=True),
+            "thermal": ImageFeature(decode=True),
+            "thermal_rgb": ImageFeature(decode=True),
+        }
+    )
+
+    # Unzip the tuples into separate lists
+    color_imgs, depth_imgs, depth_16bit_imgs, thermal_imgs, thermal_rgb_imgs = zip(
+        *image_tuples
+    )
+
+    dataset_dict = {
+        "color": list(color_imgs),
+        "depth": list(depth_imgs),
+        "depth_16bit": list(depth_16bit_imgs),
+        "thermal": list(thermal_imgs),
+        "thermal_rgb": list(thermal_rgb_imgs),
+    }
+
+    dataset = Dataset.from_dict(dataset_dict, features=features)
+
+    return dataset

workplace.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "from utils import find_image_groups, create_image_dataset\n",
+    "from datasets import load_dataset, concatenate_datasets\n",
+    "\n",
+    "data_paths = [\n",
+    "    \"./2023-07-21_14-08-29\",\n",
+    "    \"./2023-07-21_14-44-56\",\n",
+    "    # \"./2023-07-21_14-51-07\",\n",
+    "    \"./2023-07-22_16-24-27\",\n",
+    "]\n",
+    "datasets = []\n",
+    "\n",
+    "for data_path in data_paths:\n",
+    "    image_groups = find_image_groups(\n",
+    "        data_path, \"RS_COLOR\", [\"RS_DEPTH\", \"RS_DEPTH_16bit\", \"THERMAL\", \"THERMAL_RGB\"], threshold_ms=100\n",
+    "    )\n",
+    "    new_dataset = create_image_dataset(image_groups)\n",
+    "    datasets.append(new_dataset)\n",
+    "    print(f\"Dataset {data_path} created with {len(new_dataset)} samples\")\n",
+    "\n",
+    "existing_dataset = load_dataset(\"hassanjbara/BASEPROD\", split=\"train\")\n",
+    "combined_dataset = concatenate_datasets([existing_dataset, *datasets])\n",
+    "combined_dataset.push_to_hub(\"hassanjbara/BASEPROD\",)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}