new things

.gitignore

@@ -0,0 +1,9 @@
+# folders
+/images/*
+/global/*
+
+# files
+cross_pairs_exhaustive.py
+make_pairs_exhaustive.py
+make_pairs_sequentially.py
+report.py

README.md

@@ -91,30 +91,33 @@ pip install nerfstudio
 COLMAP install
 
 ```bash
-conda install conda-forge::colmap
+conda install -y conda-forge::colmap
 ```
 
+GLOMAP install
 
-## Reproduce results
-
-### 1. Download the files 
+```bash
+conda install -y conda-forge::glomap
+```
 
-Once you downlaoded this repository you should be ready to go, you just need to unzip the folders
+ffmpeg for processing images
 
-### 2. Process individual videos sequences
+```bash
+conda install conda-forge::ffmpeg
+```
 
-bash
-python prepare_data.py
+## Reproduce results
 
+### 1. Pre-Process individual videos sequences
 
+```bash
+python prepare_data.py
+```
 
-### 3. Reconstruct the campus hierarchically
+### 2. Reconstruct the campus hierarchically
 
-first run the core.sh script. This will reconstruct a base structure, upon which all the other reconstructions will be added to.
+first run the `run_core.sh` script. This will reconstruct a base structure, upon which all the other reconstructions will be added to.
 This is the most important one.
 
-
 For the other scenes, for now just copy the run_template.sh file and modify according to the current scane name and file.
-Usually I run line by line to make sure everything is correct, but theoretically you could just run the entire file.
-
-TESTING LFS
+Usually I run line by line to make sure everything is correct, but theoretically you could just run the entire file.

install.sh

@@ -8,6 +8,8 @@ pip install ninja git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindi
 
 pip install nerfstudio
 
-conda install conda-forge::colmap
+conda install -y conda-forge::colmap
 
-conda install conda-forge::glomap
+conda install -y conda-forge::glomap
+
+conda install conda-forge::ffmpeg

prepare_data.py

@@ -1,4 +1,5 @@
 import os
+import json
 import itertools
 import subprocess
 
@@ -160,8 +161,7 @@ def generate_multi_camera_pairs(camera_images, window_size=20):
     # Pad shorter camera lists with None to match the longest list
     for camera, images in camera_images.items():
         if len(images) < max_images:
-            camera_images[camera] = images + [None] * (max_images - len(images))
-    print(camera_images)
+            camera_images[camera] = images + [None] * (max_images - len(images))    
     
     image_pairs = []
     
@@ -234,6 +234,16 @@ def count_images(output_directory):
         if os.path.isfile(os.path.join(output_directory, f)) and os.path.splitext(f)[1].lower() in image_extensions
     ])
 
+def get_video_duration(video_path):
+    result = subprocess.run([
+        "ffprobe", "-v", "error",
+        "-show_entries", "format=duration",
+        "-of", "json", video_path
+    ], stdout=subprocess.PIPE)
+    duration = float(json.loads(result.stdout)["format"]["duration"])
+    return duration
+
+
 
 def generate_images(out_dir, scene_name, config):
 
@@ -272,20 +282,36 @@ def generate_images(out_dir, scene_name, config):
     
             # Create output images directory if it doesn't exist
             output_images_dir = f"{out_dir}/{scene_name}/cam{folder_name}"
-            os.makedirs(output_images_dir, exist_ok=True)
-            
-            skip_n_frames = config[scene_name]['skip_start_frames']
+            os.makedirs(output_images_dir, exist_ok=True)                    
+
+            duration = get_video_duration(video_file)
+            skip_start = config[scene_name].get('skip_start_seconds', 0)
+            skip_end = config[scene_name].get('skip_end_seconds', 0)
+            duration_to_keep = duration - skip_start - skip_end
     
             # Construct the ffmpeg command
+            # ffmpeg_command = [
+            #     "ffmpeg",
+            #     "-hwaccel", "auto",
+            #     "-ss", f"{skip_start_seconds}",  # Skip n frames at 2fps (n/2 seconds)
+            #     "-i", video_file,
+            #     "-vf", "fps=2,scale=iw:ih",
+            #     "-q:v", "2",
+            #     "-start_number", f"{prev_max_id}",
+            #     f"{output_images_dir}/%06d_{camera_correspondances[folder_name]}.jpg"
+            # ]
+
+            # Build ffmpeg command
             ffmpeg_command = [
                 "ffmpeg",
                 "-hwaccel", "auto",
-                "-ss", f"{skip_n_frames/2}",  # Skip n frames at 2fps (n/2 seconds)
+                "-ss", f"{skip_start}",
                 "-i", video_file,
+                "-t", f"{duration_to_keep}",
                 "-vf", "fps=2,scale=iw:ih",
                 "-q:v", "2",
                 "-start_number", f"{prev_max_id}",
-                f"{output_images_dir}/%06d_{camera_correspondances[folder_name]}.jpg"
+                f"{output_images_dir}/%06d{camera_correspondances[folder_name]}.jpg"
             ]
     
             print(f"Running ffmpeg command for folder: {folder_name}")
@@ -326,7 +352,6 @@ def generate_sequential_pairs(folder_path, output_file_pairs_sequential, scene_n
     # Generate exhaustive list of pairs of images
     print(f"Generating image pairs for scene: {scene_name}")
     images_pairs_sequential = generate_multi_camera_pairs(camera_images)
-    print(images_pairs_sequential)
 
     # Save pairs to file
     save_pairs_to_file(images_pairs_sequential, output_file_pairs_sequential, scene)
@@ -406,7 +431,8 @@ def process_scene(scene_name, config, out_dir="./images"):
 # Scene configurations
 config = {
     "campus_core": {
-        'skip_start_frames': 0,
+        'skip_start_seconds': 5,
+        'skip_end_seconds': 20,
         'src_scene': "campus_core",
         'src_cam': "core01",
         'target_scene': "mid_library",

run_core.sh

@@ -11,10 +11,7 @@ echo "Running feature matching..."
 colmap matches_importer --database_path ./global/database_core.db --match_list_path ./images/campus_core/image_pairs_exhaustive.txt
 
 echo "Reconstructing 3D model..."
-glomap mapper --database_path ./global/database_core.db --image_path ./images --output_path ./campus_core
-
-ENDIN=$(date +"%H:%M:%S")
-curl -X POST -H 'Content-Type: application/json' -d "{\"chat_id\": \"777722458\", \"text\": \"FINE COLMAP\", \"disable_notification\": true}" https://api.telegram.org/bot$TELEGRAM_BOT_TOKEN/sendMessage
+colmap mapper --database_path ./global/database_core.db --image_path ./images --output_path ./global/campus_core --image_list_path ./images/campus_core/image_list.txt
 
 echo "Showing result"
-colmap gui --import_path ./campus_core/0 --database_path ./global/database_core.db --image_path ./images
+colmap gui --import_path ./global/campus_core/0 --database_path ./global/database_core.db --image_path ./images