Exp 02 time-lapse videos

env.yml

@@ -9,4 +9,6 @@ dependencies:
   - pathlib
   - imageio
   - scikit-learn
+  - scikit-video
+  - ffmpeg
   - jupyterlab

make_h5s_by_frame_index_sorghum_20250225.v002.ipynb

@@ -0,0 +1,189 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import h5py\n",
+    "from pathlib import Path\n",
+    "import imageio as iio\n",
+    "import re\n",
+    "import logging"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set up logging\n",
+    "logging.basicConfig(level=logging.INFO)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def natural_sort(l):\n",
+    "    \"\"\"Sort a list of strings in a way that considers numerical values within the strings.\n",
+    "    \n",
+    "    For example, natural_sort([\"img2.png\", \"img10.png\", \"img1.png\"])\n",
+    "    will return [\"img1.png\", \"img2.png\", \"img10.png\"].\n",
+    "    \n",
+    "    Args:\n",
+    "        l (list): List of strings to sort.\n",
+    "    \n",
+    "    Returns:\n",
+    "        list: List of sorted strings.\n",
+    "    \"\"\"\n",
+    "    l = [x.as_posix() if isinstance(x, Path) else x for x in l]\n",
+    "    convert = lambda text: int(text) if text.isdigit() else text.lower()\n",
+    "    alphanum_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)]\n",
+    "    return sorted(l, key=alphanum_key)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "base_src_dir = \"H:/users/eberrigan/20250225_Elohim_Bello_Exp02_timelapse_images/Sorghum\" # Adjust to your source directory\n",
+    "base_dst_dir = \"H:/users/eberrigan/20250225_Elohim_Bello_Exp02_timelapse_images/Sorghum_time_lapse_videos_20250225/h5s_preds_by_frame\"  # Adjust to your destination directory\n",
+    "genotypes = range(1,7) # 1 to 6\n",
+    "img_numbers = range(1, 73)  # 1 to 72\n",
+    "days = range(1, 17)  # 1 to 16\n",
+    "replicates = range(1, 7) # 1 to 6\n",
+    "overwrite = False # When overwrite=True existing files are overwritten\n",
+    "logging.info(f\"genotypes: {genotypes}\")\n",
+    "logging.info(f\"image numbers: {img_numbers}\")\n",
+    "logging.info(f\"days: {days}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Elizabeth\\AppData\\Local\\Temp\\ipykernel_29232\\4202304162.py:55: DeprecationWarning: Starting with ImageIO v3 the behavior of this function will switch to that of iio.v3.imread. To keep the current behavior (and make this warning disappear) use `import imageio.v2 as imageio` or call `imageio.v2.imread` directly.\n",
+      "  img = iio.imread(img_path)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This dataset is organized as genotype -> replicate -> timepoint -> image\n",
+    "# genotypes are numbered 1 to 6\n",
+    "# replicates are numbered 1 to 6\n",
+    "# timepoints are numbered 1 to 16\n",
+    "# images are numbered 1 to 72\n",
+    "\n",
+    "base_src_dir = Path(base_src_dir)\n",
+    "base_dst_dir = Path(base_dst_dir)\n",
+    "\n",
+    "# Create the destination directory if it doesn't exist\n",
+    "base_dst_dir.mkdir(parents=True, exist_ok=True)\n",
+    "logging.info(f\"Destination directory: {base_dst_dir}\")\n",
+    "\n",
+    "# Iterate over genotypes and replicates\n",
+    "for genotype in genotypes:\n",
+    "    for replicate in replicates:\n",
+    "        h5_name = base_dst_dir / f\"g{genotype}_r{replicate}.h5\"\n",
+    "        logging.info(f\"\\nProcessing genotype {genotype}, replicate {replicate}\")\n",
+    "\n",
+    "        if not overwrite and h5_name.exists():\n",
+    "            logging.info(f\"Skipping {h5_name} as it already exists and overwrite is False.\")\n",
+    "            continue\n",
+    "\n",
+    "        images = []\n",
+    "\n",
+    "        # Iterate over days\n",
+    "        for day in days:\n",
+    "            day_dir = base_src_dir / str(genotype) / str(replicate) / str(day)\n",
+    "            logging.info(f\"Day directory: {day_dir}\")\n",
+    "            if not day_dir.exists():\n",
+    "                continue\n",
+    "            \n",
+    "            # Collect image paths for the current day\n",
+    "            day_images = []\n",
+    "            missing_images = False\n",
+    "            for img_number in img_numbers:\n",
+    "                img_path = day_dir / f\"{img_number}.png\"\n",
+    "                if img_path.exists():\n",
+    "                    day_images.append(img_path)\n",
+    "                else:\n",
+    "                    logging.warning(f\"Missing image: {img_path}\")\n",
+    "                    missing_images = True\n",
+    "\n",
+    "            # Skip this day if any images are missing\n",
+    "            if missing_images:\n",
+    "                logging.warning(f\"Skipping day {day} for genotype {genotype}, replicate {replicate} due to missing images.\")\n",
+    "                continue\n",
+    "            \n",
+    "            # Sort the images for the current day\n",
+    "            day_images = natural_sort(day_images)\n",
+    "            \n",
+    "            # Read and append images to the list\n",
+    "            for img_path in day_images:\n",
+    "                try:\n",
+    "                    img = iio.imread(img_path)\n",
+    "                    images.append(img)\n",
+    "                except Exception as e:\n",
+    "                    logging.error(f\"Error reading {img_path}: {e}\")\n",
+    "                    continue\n",
+    "\n",
+    "        if images:\n",
+    "            try:\n",
+    "                vol = np.stack(images, axis=0)  # Stack images to create a volume (slices, height, width)\n",
+    "\n",
+    "                # Save as h5\n",
+    "                with h5py.File(h5_name, \"w\") as f:\n",
+    "                    f.create_dataset(\n",
+    "                        \"vol\",\n",
+    "                        data=np.expand_dims(vol, axis=-1),  # Add channel dimension\n",
+    "                        compression=\"gzip\",  # Using gzip compression\n",
+    "                        compression_opts=1  # Higher compression level\n",
+    "                    )\n",
+    "                logging.info(f\"Time-lapse video for genotype {genotype}, replicate {replicate} saved successfully as {h5_name}.\")\n",
+    "            except ValueError as e:\n",
+    "                logging.error(f\"Error stacking images for {h5_name}: {e}\")\n",
+    "        else:\n",
+    "            logging.info(f\"No images found for genotype {genotype}, replicate {replicate}. Skipping.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "time_lapse_videos",
+   "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": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}