From 00935253ea5cfbedfa49bad62fd178e67b957bd0 Mon Sep 17 00:00:00 2001 From: MihinP Date: Mon, 9 Feb 2026 14:43:41 +1100 Subject: [PATCH] sanitised redundant scripts directory --- docs/partinet_docs.md | 636 ++++++++++++++++++++ scripts/detect.sh | 19 - scripts/download.sh | 21 - scripts/generate-star-file.py | 93 --- scripts/generate-star-file.sh | 15 - scripts/meta/datasets.txt | 35 -- scripts/meta/development_set.txt | 27 - scripts/meta/fix_names_datasets.txt | 13 - scripts/meta/test_set.txt | 8 - scripts/noannot_images.txt | 436 -------------- scripts/preprocess.py | 198 ------ scripts/preprocess_step1.sh | 22 - scripts/preprocess_step2.sh | 26 - scripts/star_file_gen/generate-star-file.py | 107 ---- scripts/star_file_gen/generate-star-file.sh | 21 - scripts/test.sh | 18 - scripts/train_step1.sh | 27 - scripts/train_step2.sh | 14 - scripts/visualise_denoise_results.ipynb | 158 ----- 19 files changed, 636 insertions(+), 1258 deletions(-) create mode 100644 docs/partinet_docs.md delete mode 100644 scripts/detect.sh delete mode 100644 scripts/download.sh delete mode 100644 scripts/generate-star-file.py delete mode 100644 scripts/generate-star-file.sh delete mode 100644 scripts/meta/datasets.txt delete mode 100644 scripts/meta/development_set.txt delete mode 100644 scripts/meta/fix_names_datasets.txt delete mode 100644 scripts/meta/test_set.txt delete mode 100644 scripts/noannot_images.txt delete mode 100755 scripts/preprocess.py delete mode 100644 scripts/preprocess_step1.sh delete mode 100644 scripts/preprocess_step2.sh delete mode 100644 scripts/star_file_gen/generate-star-file.py delete mode 100644 scripts/star_file_gen/generate-star-file.sh delete mode 100644 scripts/test.sh delete mode 100644 scripts/train_step1.sh delete mode 100644 scripts/train_step2.sh delete mode 100644 scripts/visualise_denoise_results.ipynb diff --git a/docs/partinet_docs.md b/docs/partinet_docs.md new file mode 100644 index 0000000..a0a87a9 --- /dev/null +++ b/docs/partinet_docs.md @@ -0,0 +1,636 @@ +# PartiNet – Documentation + +**Version:** 0.2 +**Container Location:** `/stornext/Projects/cryoEM/cryoEM_data/lab_shakeel/perera.m/PartiNet/PartiNet_v0.2.sif` +**Model Weights:** `/stornext/Projects/cryoEM/cryoEM_data/lab_shakeel/perera.m/PartiNet/denoised_micrographs.pt` + +--- + +## Table of Contents + +- [Introduction](#introduction) +- [Getting Started](#getting-started) + - [Prerequisites](#prerequisites) + - [Directory Structure](#directory-structure) + - [Stage 1: Denoise](#stage-1-denoise) + - [Stage 2: Detect](#stage-2-detect) + - [Stage 3: Star](#stage-3-star) + - [Output Files](#output-files) +- [Detailed Stage Documentation](#detailed-stage-documentation) + - [Denoise Stage](#denoise-stage) + - [Detect Stage](#detect-stage) + - [Star Stage](#star-stage) +- [Training](#training) + - [Split Training Data](#split-training-data) + - [Train Dual Detectors (Step 1)](#train-dual-detectors-step-1) + - [Train Adaptive Router (Step 2)](#train-adaptive-router-step-2) + - [Training Output Reference](#training-output-reference) + +--- + +## Introduction + +PartiNet is a powerful command-line tool for particle picking on cryo-EM micrographs. It provides a comprehensive three-stage pipeline designed to clean, identify, and prepare particles from experimental data for subsequent processing. + +### The Three-Stage Pipeline + +**Stage 1: Denoise** – Removes noise and artifacts from raw data using fast heuristic denoising algorithms, improving signal-to-noise ratios. + +**Stage 2: Detect** – Identifies and locates individual particles within cleaned data using a dynamic adaptive architecture. + +**Stage 3: Star** – Prepares particle data for further processing and provides reports on particle populations in your dataset. + +### Key Features + +- **Fast picking** – Leverages state-of-the-art dynamic deep learning models +- **Accurate picking** – Accurately identifies proteins and filters junk +- **Overcome orientation bias** – Identifies rare views of proteins +- **Multi-species identification** – Handles heterogeneous samples without prior box size estimation +- **Batch processing** – Efficient parallel processing capabilities + +--- + +## Getting Started + +This guide walks you through your first PartiNet analysis using the three-stage pipeline. + +### Prerequisites + +Before starting, ensure you have: +- Motion-corrected micrographs in a source directory +- A project directory where outputs will be saved +- GPU access for optimal performance +- Access to the PartiNet container and model weights + +**Load the Apptainer module:** +```shell +module load apptainer +``` + +**Set up environment variables (recommended):** +```shell +export PARTINET_SIF="/stornext/Projects/cryoEM/cryoEM_data/lab_shakeel/perera.m/PartiNet/PartiNet_v0.2.sif" +export PARTINET_WEIGHTS="/stornext/Projects/cryoEM/cryoEM_data/lab_shakeel/perera.m/PartiNet/denoised_micrographs.pt" +``` + +### Directory Structure + +PartiNet expects and creates the following directory structure: + +``` +project_directory/ +├── motion_corrected/ # 📁 Your input micrographs +│ ├── micrograph1.mrc +│ ├── micrograph2.mrc +│ └── ... +├── denoised/ # 🧹 Created by denoise stage +│ ├── micrograph1.mrc +│ ├── micrograph2.mrc +│ └── ... +├── exp/ # 🎯 Created by detect stage +│ ├── labels/ # 📋 Detection coordinates +│ │ ├── micrograph1.txt +│ │ ├── micrograph2.txt +│ │ └── ... +│ ├── micrograph1.png # 🖼️ Micrographs with detections drawn +│ ├── micrograph2.png +│ └── ... +└── partinet_particles.star # ⭐ Final STAR file (created by star stage) +``` + +**Pipeline Flow:** +1. **Input** → `motion_corrected/` (your micrographs) +2. **Stage 1** → `denoised/` (cleaned micrographs) +3. **Stage 2** → `exp*/` (detections + visualizations) +4. **Stage 3** → `*.star` (final particle coordinates) + +### Stage 1: Denoise + +The first stage removes noise from your micrographs and improves signal-to-noise ratios: + +```shell +module load apptainer + +apptainer exec --nv --no-home \ + -B /vast,/stornext \ + $PARTINET_SIF \ + partinet denoise \ + --source /path/to/my_project/motion_corrected \ + --project /path/to/my_project +``` + +**What this does:** +- Reads micrographs from `motion_corrected/` directory +- Applies denoising algorithms +- Saves cleaned micrographs to `denoised/` directory in your project folder + +### Stage 2: Detect + +The detection stage identifies particles in your denoised micrographs: + +```shell +apptainer exec --nv --no-home \ + -B /vast,/stornext \ + $PARTINET_SIF \ + partinet detect \ + --weight $PARTINET_WEIGHTS \ + --source /path/to/my_project/denoised \ + --device 0,1,2,3 \ + --project /path/to/my_project +``` + +**What this creates:** +- `exp/` directory in your project folder +- `exp/labels/` directory containing detection coordinates for each micrograph +- Micrographs with detection boxes drawn on top (saved in `exp/`) + +**Key parameters:** +- `--weight`: Path to trained model weights +- `--conf-thres`: Confidence threshold for detections (0.0 = accept all, default: 0.1) +- `--iou-thres`: Intersection over Union threshold for filtering overlapping detections (default: 0.2) +- `--device`: GPU devices to use (0,1,2,3 = use 4 GPUs) + +### Stage 3: Star + +The final stage converts detections to STAR format and applies confidence filtering: + +```shell +apptainer exec --nv --no-home \ + -B /vast,/stornext \ + $PARTINET_SIF \ + partinet star \ + --labels /path/to/my_project/exp/labels \ + --images /path/to/my_project/denoised \ + --output /path/to/my_project/partinet_particles.star \ + --conf 0.1 +``` + +**What this does:** +- Reads detection labels from `exp/labels/` +- Filters particles based on confidence threshold (0.1 in this example) +- Creates a STAR file ready for further processing in RELION or other software + +### Output Files + +After running all three stages, you'll have: + +1. **Denoised micrographs** (`denoised/`) – Cleaned input for particle detection +2. **Detection visualizations** (`exp/*.png`) – Micrographs with particle boxes drawn +3. **Detection coordinates** (`exp/labels/*.txt`) – Raw detection data +4. **STAR file** (`*.star`) – Final particle coordinates ready for downstream processing + +--- + +## Detailed Stage Documentation + +### Denoise Stage + +Denoising can vastly improve particle picking by helping to increase signal to noise in low-dose micrographs. PartiNet implements a modified heuristic Wiener filter denoiser based on the method from CryoSegNet (Gyawali et al., 2024). PartiNet's implementation introduces multiprocessing, allowing for high-throughput denoising of large datasets. + +#### Parameters + +**Required Parameters:** + +| Parameter | Description | Example | +|-----------|-------------|---------| +| `--source` | Directory containing motion-corrected micrographs in MRC format | `/data/my_project/motion_corrected` | +| `--project` | Parent project directory where all outputs will be saved | `/data/my_project` | + +**Optional Parameters:** + +| Parameter | Type | Default | Description | +|-----------|------|---------|-------------| +| `--num_workers` | int | max available CPUs | Number of CPU workers for processing | +| `--img_format` | string | `png` | Output format for denoised images (`png`, `jpg`, `mrc`) | + +#### Input Requirements + +Your motion-corrected micrographs should ideally meet these criteria: + +- **Format**: single-slice MRC files from RELION or CryoSPARC +- **Motion correction**: Total full frame motion should be **less than 100 pixels** +- **CTF estimation**: CTF fit resolution should be **less than 10 Angstroms** +- **Convergence**: Motion correction and CTF estimation should have converged appropriately + +#### Setup Instructions + +**1. Create Project Directory:** +```shell +mkdir my_project +cd my_project +mkdir motion_corrected +``` + +**2. Transfer Motion-Corrected Micrographs:** + +From CryoSPARC: +```shell +# Using symbolic links (faster, saves space) +ln -s /path/to/cryosparc/project/job_number/motioncorrected/*_fractions_patch_aligned.mrc motion_corrected/ +``` + +From RELION: +```shell +# Link motion-corrected micrographs +ln -s /path/to/relion/project/MotionCorr/job_number/Micrographs/*.mrc motion_corrected/ +``` + +**3. Run Denoising:** +```shell +module load apptainer + +apptainer exec --nv --no-home \ + -B /vast,/stornext \ + $PARTINET_SIF \ + partinet denoise \ + --source /path/to/my_project/motion_corrected \ + --project /path/to/my_project +``` + +#### Output + +After denoising, your project directory will contain: + +``` +my_project/ +├── motion_corrected/ +│ └── [original MRC files] +├── denoised/ +│ ├── micrograph_001_fractions_patch_aligned.png +│ ├── micrograph_002_fractions_patch_aligned.png +│ └── ... +└── partinet_denoise.log +``` + +#### Advanced Usage + +**Custom CPU Configuration:** + +PartiNet automatically optimizes CPU usage. To manually set workers: +```shell +apptainer exec --nv --no-home \ + -B /vast,/stornext \ + $PARTINET_SIF \ + partinet denoise \ + --source /path/to/motion_corrected \ + --project /path/to/project \ + --num_workers 16 +``` + +**Different Output Formats:** + +By default PartiNet outputs denoised images in `png` format. To use MRC format: +```shell +apptainer exec --nv --no-home \ + -B /vast,/stornext \ + $PARTINET_SIF \ + partinet denoise \ + --source /path/to/motion_corrected \ + --project /path/to/project \ + --img_format mrc +``` + +--- + +### Detect Stage + +PartiNet uses a modified version of an adaptive YOLO architecture called *DynamicDet* (Lin et al., 2023) to identify particles in cryo-EM micrographs. This stage provides highly accurate particle detection with customizable confidence and overlap thresholds. + +#### Quick Start + +```shell +module load apptainer + +apptainer exec --nv --no-home \ + -B /vast,/stornext \ + $PARTINET_SIF \ + partinet detect \ + --weight $PARTINET_WEIGHTS \ + --source /path/to/my_project/denoised \ + --device 0,1,2,3 \ + --project /path/to/my_project +``` + +#### Parameters + +**Required Parameters:** + +| Parameter | Description | Example | +|-----------|-------------|---------| +| `--weight` | Path to pre-trained model weights file | `$PARTINET_WEIGHTS` | +| `--source` | Path to directory containing micrographs to process | `/path/to/my_project/denoised` | +| `--project` | Path to project directory where outputs will be saved | `/path/to/my_project` | + +**Customizable Parameters:** + +| Parameter | Type | Description | Default | Notes | +|-----------|------|-------------|---------|--------| +| `--conf-thres` | float | Confidence threshold for detection (0.0-1.0) | `0.1` | Lower = more detections, higher = fewer but more confident | +| `--iou-thres` | float | IoU threshold for non-maximum suppression (0.0-1.0) | `0.2` | Higher = more aggressive overlap removal | +| `--device` | string | GPU devices to use (comma-separated) | `0,1,2,3` | Use `0` for single GPU, `0,1` for two GPUs, etc. | + +#### Parameter Configuration Guide + +**Confidence Threshold (`--conf-thres`):** + +The confidence threshold determines the minimum confidence score required for a detection to be considered valid. It is recommended to set this low (0.0-0.3) during detection and then filter during STAR generation. + +- **0.0**: Accept all detections (maximum recall) +- **0.1**: Balanced approach (recommended starting point) +- **0.3**: Higher precision, may miss some particles + +**IoU Threshold (`--iou-thres`):** + +Recommended starting value: `0.2` + +Controls how aggressively overlapping detections are filtered. Higher values allow more overlapping boxes. + +#### Output + +After detection, your project directory will contain: + +``` +my_project/ +├── denoised/ +├── exp/ +│ ├── labels/ +│ │ ├── micrograph_001.txt +│ │ ├── micrograph_002.txt +│ │ └── ... +│ ├── micrograph_001.png +│ ├── micrograph_002.png +│ └── ... +└── partinet_detect.log +``` + +The `labels/` directory contains YOLO-format text files with detection coordinates. + +--- + +### Star Stage + +The `partinet star` command is the final step in your particle picking pipeline. It converts the particle coordinates detected in the previous stage into a standardized STAR file format that can be used with downstream cryo-EM processing software like RELION, cryoSPARC, or other reconstruction programs. + +#### Quick Start + +```shell +module load apptainer + +apptainer exec --nv --no-home \ + -B /vast,/stornext \ + $PARTINET_SIF \ + partinet star \ + --labels /path/to/my_project/exp/labels \ + --images /path/to/my_project/denoised \ + --output /path/to/my_project/partinet_particles.star \ + --conf 0.1 +``` + +#### Parameters + +| Parameter | Type | Required | Description | +|-----------|------|----------|-------------| +| `--labels` | Path | Yes | Directory containing the particle coordinate files (`.txt` format) from the detection stage | +| `--images` | Path | Yes | Directory containing the denoised micrographs corresponding to the labels | +| `--output` | Path | Yes | Output path for the generated STAR file | +| `--conf` | Float | Yes | Confidence threshold for filtering particle coordinates (0.0-1.0) | + +#### Input Requirements + +At this stage the project directory should contain: +- `motion_corrected/` – Original micrographs +- `denoised/` – Denoised micrographs +- `exp/labels/` – Detection coordinate files +- `partinet_detect.log` – Detection stage log +- `partinet_denoise.log` – Denoise stage log + +#### Confidence Threshold + +Typical range: 0.1-0.3. Choose based on dataset quality and downstream needs. + +- **0.1**: Inclusive picking (more particles, some false positives) +- **0.2**: Balanced approach +- **0.3**: Conservative picking (fewer false positives, may miss some particles) + +#### Output + +The command generates a STAR file (`partinet_particles.star`) containing: +- Particle coordinates (X, Y positions) +- Confidence scores +- Micrograph names +- Image dimensions + +This STAR file can be directly imported into RELION or cryoSPARC for particle extraction and further processing. + +--- + +## Training + +### Split Training Data + +The split command organizes your annotated particle data into training and validation sets, preparing it for PartiNet model training. This step can either convert STAR files from manual picking sessions directly to YOLO format, or split existing YOLO labels into organized train/val directories. + +#### Quick Start + +```shell +module load apptainer + +apptainer exec --nv --no-home \ + -B /vast,/stornext \ + $PARTINET_SIF \ + partinet split \ + --star /path/to/my_project/particles.star \ + --images /path/to/my_project/denoised \ + --output /path/to/my_project/training_data +``` + +#### Parameters + +| Parameter | Description | Example | +|-----------|-------------|---------| +| `--star` | Path to input STAR file from picking | `/path/to/my_project/particles.star` | +| `--images` | Directory containing the micrograph images | `/path/to/my_project/denoised` | +| `--output` | Output directory for organized train/val data | `/path/to/my_project/training_data` | + +#### Output + +After splitting, training data will be organized as: + +``` +training_data/ +├── images/ +│ ├── train/ +│ │ ├── micrograph_001.png +│ │ └── ... +│ └── val/ +│ ├── micrograph_050.png +│ └── ... +├── labels/ +│ ├── train/ +│ │ ├── micrograph_001.txt +│ │ └── ... +│ └── val/ +│ ├── micrograph_050.txt +│ └── ... +├── train.txt +├── val.txt +└── cryo_training.yaml +``` + +--- + +### Train Dual Detectors (Step 1) + +PartiNet's architecture requires a two-step training regime. Step 1 trains the dual detectors. + +#### Quick Start + +```shell +module load apptainer + +apptainer exec --nv --no-home \ + -B /vast,/stornext \ + $PARTINET_SIF \ + partinet train step1 \ + --weight $PARTINET_WEIGHTS \ + --data /path/to/cryo_training.yaml \ + --project /path/to/training_output_step1 +``` + +#### Parameters + +**Required:** +- `--weight`: Path to pre-trained weights +- `--data`: Path to training configuration YAML file +- `--project`: Output directory for training results + +**Optional:** +- `--workers`: Number of data loading workers (default: 8) +- `--device`: GPU devices to use (default: 0,1,2,3) +- `--batch`: Batch size (default: 16) +- `--epochs`: Number of training epochs (default: 300) + +#### Example with Custom Parameters + +```shell +apptainer exec --nv --no-home \ + -B /vast,/stornext \ + $PARTINET_SIF \ + partinet train step1 \ + --weight $PARTINET_WEIGHTS \ + --data /path/to/cryo_training.yaml \ + --project /path/to/training_step1 \ + --batch 32 \ + --epochs 200 \ + --device 0,1 +``` + +--- + +### Train Adaptive Router (Step 2) + +Step 2 trains the adaptive router. The `--weight` parameter must point to a Step 1 checkpoint file (e.g., `last.pt`). + +#### Quick Start + +```shell +module load apptainer + +apptainer exec --nv --no-home \ + -B /vast,/stornext \ + $PARTINET_SIF \ + partinet train step2 \ + --weight /path/to/training_step1/exp/weights/last.pt \ + --data /path/to/cryo_training.yaml \ + --project /path/to/training_output_step2 \ + --epochs 10 +``` + +#### Parameters + +**Required:** +- `--weight`: Path to Step 1 checkpoint file (typically `last.pt`) +- `--data`: Path to training configuration YAML file +- `--project`: Output directory for training results + +**Optional:** +- `--epochs`: Number of training epochs (default: 10, recommended for Step 2) +- `--workers`: Number of data loading workers +- `--device`: GPU devices to use +- `--batch`: Batch size + +--- + +### Training Output Reference + +This section describes the output generated during PartiNet training (both Step 1 and Step 2). + +#### Directory Structure + +A completed training run produces an `exp*/` folder with: + +``` +exp/ +├── cfg.yaml # Model configuration +├── hyp.yaml # Hyperparameters used +├── opt.yaml # Training options +├── LR.png # Learning rate schedule +├── results.png # Training metrics plots +├── results.txt # Training metrics (text format) +├── confusion_matrix.png # Confusion matrix +├── F1_curve.png # F1 score curve +├── P_curve.png # Precision curve +├── R_curve.png # Recall curve +├── PR_curve.png # Precision-Recall curve +├── train_batch*.jpg # Training batch visualizations +├── test_batch*_labels.jpg # Validation labels +├── test_batch*_pred.jpg # Validation predictions +├── events.out.tfevents.* # TensorBoard logs +└── weights/ + ├── best.pt # Best model checkpoint + ├── last.pt # Last epoch checkpoint + └── epoch_*.pt # Periodic checkpoints +``` + +#### Monitoring Training + +**Using TensorBoard:** + +```shell +module load apptainer + +apptainer exec --nv --no-home \ + -B /vast,/stornext \ + $PARTINET_SIF \ + tensorboard --logdir /path/to/your_project_folder +``` + +Then open `http://localhost:6006` in your web browser. + +#### Resuming Training + +To resume training from a checkpoint, use the `last.pt` file: + +```shell +apptainer exec --nv --no-home \ + -B /vast,/stornext \ + $PARTINET_SIF \ + partinet train step1 \ + --weight /path/to/training_step1/exp/weights/last.pt \ + --data /path/to/cryo_training.yaml \ + --project /path/to/training_step1_resumed +``` + +--- + +## References + +- Bepler, T. et al. (2020). Topaz-Denoise: general deep denoising models for cryoEM and cryoET. *Nature Communications*. +- Wagner, T. & Raunser, S. (2020). JANNI: Neural Network Filtering for CryoEM Data. *Communications Biology*. +- Gyawali, P. K. et al. (2024). CryoSegNet: automatic instance segmentation of macromolecules from cryo-EM micrographs. +- Lin, T. et al. (2023). DynamicDet: A Unified Dynamic Architecture for Object Detection. *CVPR 2023*. + +--- + +**Document Version:** 1.0 +**Last Updated:** October 2025 diff --git a/scripts/detect.sh b/scripts/detect.sh deleted file mode 100644 index ef5f642..0000000 --- a/scripts/detect.sh +++ /dev/null @@ -1,19 +0,0 @@ -#!/bin/bash - -#SBATCH --cpus-per-task 16 --mem=499G --gres=gpu:A100:1 -p gpuq -#SBATCH --output=detect.out --job-name=detect -#SBATCH --mail-type=ALL - -python DynamicDet/detect.py \ - --img-size 640 \ - --cfg DynamicDet/cfg/dy-yolov7-step2.yaml \ - --weight path/to/model/weights.pt \ - --source /path/to/denoised/output \ - --dy-thres variable-threshold \ - --num-classes 1 \ - --device 0 \ - --save-txt --save-conf --name detect-output - - - - diff --git a/scripts/download.sh b/scripts/download.sh deleted file mode 100644 index 8b0932b..0000000 --- a/scripts/download.sh +++ /dev/null @@ -1,21 +0,0 @@ -#!/bin/bash - -#SBATCH --cpus-per-task 6 -#SBATCH --mem=5G -#SBATCH -p regular -#SBATCH --output=/vast/scratch/users/iskander.j/download_%a.out -#SBATCH --job-name=download -#SBATCH --array=1-34%10 - -module load topaz - -dataset_name=$(sed -n "$SLURM_ARRAY_TASK_ID"p datasets.txt) -download_path=/vast/scratch/users/iskander.j/PartiNet_data/raw -##/vast/projects/miti2324/PartiNet_data - -mkdir -p $download_path - -cd $download_path - -wget https://calla.rnet.missouri.edu/cryoppp/${dataset_name}.tar.gz -tar -zxvf ${dataset_name}.tar.gz \ No newline at end of file diff --git a/scripts/generate-star-file.py b/scripts/generate-star-file.py deleted file mode 100644 index 357975d..0000000 --- a/scripts/generate-star-file.py +++ /dev/null @@ -1,93 +0,0 @@ -import math -import os - -import pandas as pd -import csv -import cv2 - - -def yolo_to_starfile(yolo_coords, image_width, image_height): - x_center = math.ceil(yolo_coords['x_centre'] * image_width) - y_center = math.ceil(yolo_coords['y_centre'] * image_height) - width = yolo_coords['width'] * image_width - height = yolo_coords['height'] * image_height - - # Calculate diameter - diameter = math.ceil(max(width, height)) - - diameters.append(diameter) - - # Return x_center, y_center, diameter - return x_center, y_center, diameter - - -# Modify the generate_output function to accept DynamicDet DataFrame -def generate_output(labels, filename, star_writer): - for index, row in labels.iterrows(): - # x_centre = row['X-Coordinate'] - # y_centre = row['Y-Coordinate'] - # diameter = row['Diameter'] - - x_centre, y_centre, diameter = yolo_to_starfile(row, img_width, img_height) - # print(x_centre, y_centre, diameter) - - star_writer.writerow([filename, x_centre, y_centre, diameter]) - - -# Read DynamicDet output from txt file -labels_path = "path/to/labels" -images_path = "path/to/denoised_images" -star_out_path = "output/path/file.star" - -conf_thresh = 0.5 - -diameters = list() - -with open(star_out_path, "w") as star_file: - star_writer = csv.writer(star_file, delimiter=' ') - star_writer.writerow([]) - star_writer.writerow(["data_"]) - star_writer.writerow([]) - star_writer.writerow(["loop_"]) - star_writer.writerow(["_rlnMicrographName", "#1"]) - star_writer.writerow(["_rlnCoordinateX", "#2"]) - star_writer.writerow(["_rlnCoordinateY", "#3"]) - star_writer.writerow(["_rlnDiameter", "#4"]) - - i = 1 - for image in os.listdir(images_path): - filename = image.split("/")[-1][:-4] - - print(i) - - i+=1 - - label_file_path = os.path.join(labels_path, str(filename + '.txt')) - - if not os.path.exists(label_file_path): - continue - - # label_file_path = os.path.join(labels_path, str(filename + '.csv')) - # labels = pd.read_csv(label_file_path) - - image = cv2.imread(os.path.join(images_path, image)) - img_width = image.shape[1] - img_height = image.shape[0] - - - custom_headers = ['class', 'x_centre', 'y_centre', 'width', 'height', 'conf'] - - # Read the CSV file with custom headers - labels = pd.read_csv(label_file_path, header=None, names=custom_headers, sep=' ') - - labels = labels[labels['conf'] > conf_thresh] - - # print(labels.head()) - - # label_file_path = os.path.join(labels_path, str(filename + '.csv')) - # labels = pd.read_csv(label_file_path) - - star_filename = str(filename + '.mrc') - - generate_output(labels, star_filename, star_writer) - diff --git a/scripts/generate-star-file.sh b/scripts/generate-star-file.sh deleted file mode 100644 index d8734dc..0000000 --- a/scripts/generate-star-file.sh +++ /dev/null @@ -1,15 +0,0 @@ -#!/bin/bash - -#SBATCH --cpus-per-task 16 --mem=499G --gres=gpu:A30:4 -p gpuq -#SBATCH --output=generate-star-file.out --job-name=generate-star-file.py - -module load miniconda3/latest -conda activate /vast/projects/miti2324/envs/dynamicdet_mamba37/ - -python generate-star-file.py - - - - - - diff --git a/scripts/meta/datasets.txt b/scripts/meta/datasets.txt deleted file mode 100644 index 909f270..0000000 --- a/scripts/meta/datasets.txt +++ /dev/null @@ -1,35 +0,0 @@ -10005 -10017 -10028 -10059 -10061 -10075 -10077 -10081 -10093 -10096 -10184 -10240 -10289 -10291 -10345 -10387 -10389A -10389B -10406 -10444 -10526 -10532 -10576 -10590 -10669 -10671 -10737 -10760 -10816 -10852 -10947 -11051 -11056 -11057 -11183 \ No newline at end of file diff --git a/scripts/meta/development_set.txt b/scripts/meta/development_set.txt deleted file mode 100644 index 6eaa557..0000000 --- a/scripts/meta/development_set.txt +++ /dev/null @@ -1,27 +0,0 @@ -10005 -10028,0 -10059 -10061,0 -10075 -10077 -10096 -10184 -10240 -10289 -10291 -10387 -10406 -10444 -10526 -10576,0 -10590 -10669,0 -10671 -10737 -10760 -10816 -10852 -10947,0 -11051 -11057 -11183 \ No newline at end of file diff --git a/scripts/meta/fix_names_datasets.txt b/scripts/meta/fix_names_datasets.txt deleted file mode 100644 index 3a04712..0000000 --- a/scripts/meta/fix_names_datasets.txt +++ /dev/null @@ -1,13 +0,0 @@ -10389, Contains two datasets (A and B) also in tiff format so needed directory rename -10444,in tiff format so needed directory rename -10526, in tiff format so needed directory rename -10576, in tiff format so needed directory rename -10671, in tiff format so needed directory rename -10737, in tiff format so needed directory rename -10737, missing ground truth, download from https://zenodo.org/records/7934683 -10816, in tiff format so needed directory rename -10852, in tiff format so needed directory rename -11051, in tiff format so needed directory rename -11056, in tiff format so needed directory rename -11057, in tiff format so needed directory rename -10059, deleted image 289,293, 294, 295 as not same size. diff --git a/scripts/meta/test_set.txt b/scripts/meta/test_set.txt deleted file mode 100644 index 74ef710..0000000 --- a/scripts/meta/test_set.txt +++ /dev/null @@ -1,8 +0,0 @@ -10017 -10081 -10093 -10345 -10389A,0 -10389B,0 -10532 -11056 \ No newline at end of file diff --git a/scripts/noannot_images.txt b/scripts/noannot_images.txt deleted file mode 100644 index a08b80e..0000000 --- a/scripts/noannot_images.txt +++ 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-logging.set_verbosity(logging.DEBUG) -FLAGS = flags.FLAGS - -flags.DEFINE_string('datasets_path',f'/vast/scratch/users/{os.getenv("USER")}/PartiNet_data/', 'Path to raw datasets') -flags.DEFINE_string('tag','', 'suffix to add to metadata files') -flags.DEFINE_string('dataset',None, 'Dataset name, should correspond to a directory name inside datasets_path, with recommended structure') -flags.DEFINE_boolean('bounding_box',False,'whether to calculate bounding box') - -def validate_datasetname(path:str): - if not os.path.exists(os.path.join(FLAGS.datasets_path,"raw",path)): - return False - elif not os.path.exists(os.path.join(FLAGS.datasets_path,"raw",path,"denoised_micrographs")): - return False - elif not os.path.exists(os.path.join(FLAGS.datasets_path,"raw",path,"ground_truth/particle_coordinates")): - return False - else: - return True - -def calculate_bounding_box(images_dir:str, coords_dir:str, annot_dir:str): - num_annotated_imgs=0 - print("Getting bounding boxes for", FLAGS.dataset) - # output directory - - if os.path.exists(annot_dir): - if len(os.listdir(annot_dir)) == len(os.listdir(images_dir)): - print(f"{len(os.listdir(annot_dir))} annotated images already exist.") - return len(os.listdir(annot_dir)) - else: - os.makedirs(annot_dir) - ##Annotating, Bounding Box calculation - for image in os.listdir(images_dir): - image_name = (".").join(os.path.basename(image).split('.')[0:-1]) - coords_file = str(image_name + ".csv") - img = cv2.imread(os.path.join(images_dir, image), cv2.IMREAD_ANYCOLOR) - if os.path.exists(os.path.join(coords_dir, coords_file)): - num_annotated_imgs=num_annotated_imgs+1 - pts = pd.read_csv(os.path.join(coords_dir, coords_file)) - with open(os.path.join(annot_dir, f"{image_name}.txt"), "w") as output_file: - for i in range(len(pts)): - x = int(pts["X-Coordinate"][i]) # type: ignore - y = int(pts["Y-Coordinate"][i]) # type: ignore - radius = int(pts["Diameter"][i] / 2) # type: ignore - - # Calculate bounding box coordinates - x_min = x - radius - y_min = y - radius - x_max = x + radius - y_max = y + radius - - # Calculate YOLO coordinates - x_center = ((x_max + x_min) / 2) / img.shape[1] - y_center = ((y_min + y_max) / 2) / img.shape[0] - width = (x_max - x_min) / img.shape[1] - height = (y_max - y_min) / img.shape[0] - - output_line = f"0 {x_center} {y_center} {width} {height}\n" - output_file.write(output_line) - else: - print(f"{coords_file} do not exist.") - with open("meta/noannot_images.txt", "a") as f: - f.write(f"{FLAGS.dataset},{image_name}\n") - - print(f"{num_annotated_imgs} annotated images.") - return num_annotated_imgs - -def write_file_paths_to_file(filename:str, dirname:str, mode:str): - with open(filename, mode) as f: - for root, dirs, files in os.walk(dirname): - for file in files: - f.write(os.path.join(root, file) + '\n') - -def prep_datasplit(images_dir:str, annot_dir:str, set:int): - datasplit_path=os.path.join(FLAGS.datasets_path,"data_split") - coords = os.listdir(annot_dir) - train=0 - val=0 - test=0 - - if set==2: # test set - o_image_path=os.path.join(datasplit_path,"images","test") - o_label_path=os.path.join(datasplit_path,"labels","test") - for idx, file in enumerate(coords): - file_name = file[:-4] - # copying test images/labels - shutil.copy(os.path.join(images_dir, file_name+".jpg"), os.path.join(o_image_path, file_name+".jpg")) - shutil.copy(os.path.join(annot_dir, file_name+".txt"), os.path.join(o_label_path, file_name+".txt")) - - # creating test.txt file - write_file_paths_to_file(os.path.join(datasplit_path, "test.txt"),o_image_path,"w") - test=len(coords) - - elif set == 1: #development set - # splitting data into training and val - train_idx, val_idx = tts(np.arange(0, len(coords), 1), shuffle=True) - train=len(train_idx) - val=len(val_idx) - - - o_image_path=os.path.join(datasplit_path,"images") - o_label_path=os.path.join(datasplit_path,"labels") - - for idx, file in enumerate(coords): - file_name = file[:-4] - if idx in train_idx: - # copying train images - shutil.copy(os.path.join(images_dir, file_name+".jpg"), os.path.join(o_image_path, f"train", file_name+".jpg")) - # copying train labels - shutil.copy(os.path.join(annot_dir, file_name+".txt"), os.path.join(o_label_path, f"train", file_name+".txt")) - - elif idx in val_idx: - # copying val images - shutil.copy(os.path.join(images_dir, file_name+".jpg"), os.path.join(o_image_path, f"val", file_name+".jpg")) - # copying val labels - shutil.copy(os.path.join(annot_dir, file_name+".txt"), os.path.join(o_label_path, f"val", file_name+".txt")) - - - # creating val.txt and train.txt file - write_file_paths_to_file(os.path.join(datasplit_path, "val.txt"),os.path.join(o_image_path, "val"),"w") - write_file_paths_to_file(os.path.join(datasplit_path, "train.txt"),os.path.join(o_image_path, "train"),"w") - - # creating cryo_training.yaml file - training={ - "train": f"{os.path.join(datasplit_path, 'train.txt')}", - "val": f"{os.path.join(datasplit_path, 'val.txt')}", - "nc": 1, - "names": [ 'particle' ] - } - logging.debug(training) - with open(os.path.join(datasplit_path, "cryo_training.yaml"), "w") as f: - json.dump(training,f) - - return train,val,test - -def main(argv): - datasplit_path=os.path.join(FLAGS.datasets_path,"data_split") - Path(os.path.join(datasplit_path,"images","train")).mkdir(parents=True, exist_ok=True) - Path(os.path.join(datasplit_path,"images","val")).mkdir(parents=True, exist_ok=True) - Path(os.path.join(datasplit_path,"images","test")).mkdir(parents=True, exist_ok=True) - - Path(os.path.join(datasplit_path,"labels","train")).mkdir(parents=True, exist_ok=True) - Path(os.path.join(datasplit_path,"labels","val")).mkdir(parents=True, exist_ok=True) - Path(os.path.join(datasplit_path,"labels","test")).mkdir(parents=True, exist_ok=True) - - # micrograph jpegs - images_dir = os.path.join(FLAGS.datasets_path,"raw",FLAGS.dataset,"denoised_micrographs","jpg") - # ground truth coordinates in csv - coords_dir = os.path.join(FLAGS.datasets_path,"raw",FLAGS.dataset,"ground_truth/particle_coordinates") - - logging.info(f"Importing meta files, meta/development_set{FLAGS.tag}.txt and meta/test_set{FLAGS.tag}.txt ") - dvset=pd.read_csv(f"meta/development_set{FLAGS.tag}.txt",names=['name','isnotused']) - tstset=pd.read_csv(f"meta/test_set{FLAGS.tag}.txt",names=['name','isnotused']) - set=0 - - if dvset["name"].astype(str).isin([FLAGS.dataset]).any(): - set=1 #development set - elif tstset["name"].astype(str).isin([FLAGS.dataset]).any(): - set=2 #test set - else: - logging.info(f"Dataset name {FLAGS.dataset} not found in lists. Please check your development and test lists, and try again.") - return - - annot_dir = os.path.join(FLAGS.datasets_path,"raw",FLAGS.dataset,"annotations") - if FLAGS.bounding_box: - - annotated=calculate_bounding_box(images_dir, coords_dir, annot_dir) - logging.debug(f"set {set}, annotated:{annotated}") - else: - if os.path.exists(annot_dir) and (len(os.listdir(annot_dir))>0): - logging.info("annotation file found") - else: - logging.error("Annotation not found, rerun with bounding_box flag to annotate images.") - return - - - train,val,test=prep_datasplit(images_dir, annot_dir, set) - with open("meta/development_set_split.txt", "a") as f: - f.write(f"{FLAGS.dataset},{train},{val},{test},\n") - -if __name__ == '__main__': - - flags.mark_flag_as_required('dataset') - flags.register_validator('dataset', - validate_datasetname, - message='--dataset must correspond to a directory name inside datasets_path, with denoised_micrographs and ground_truth/particle_coordinates directories.') - app.run(main) \ No newline at end of file diff --git a/scripts/preprocess_step1.sh b/scripts/preprocess_step1.sh deleted file mode 100644 index c39b483..0000000 --- a/scripts/preprocess_step1.sh +++ /dev/null @@ -1,22 +0,0 @@ -#!/bin/bash - -#SBATCH --cpus-per-task 6 -#SBATCH --mem=5G -#SBATCH --gres=gpu:A30:1 -## SBATCH --qos=bonus #uncomment to use bonus qos -#SBATCH -p gpuq -#SBATCH --output=/vast/scratch/users/iskander.j/logs/denoise_%A_%a.out -#SBATCH --job-name=denoise -#SBATCH --array=1-35 - -module load topaz - -dataset_name=$(sed -n "$SLURM_ARRAY_TASK_ID"p meta/datasets.txt) -dataset_path=/vast/scratch/users/iskander.j/PartiNet_data -output_dir=${dataset_path}/raw/${dataset_name}/denoised_micrographs/jpg -mkdir -p $output_dir - -echo "Denoising...." -topaz denoise ${dataset_path}/raw/${dataset_name}/micrographs/*.mrc -o $output_dir --format "jpg" -echo "Denoising finished." -echo "Done." \ No newline at end of file diff --git a/scripts/preprocess_step2.sh b/scripts/preprocess_step2.sh deleted file mode 100644 index e9da549..0000000 --- a/scripts/preprocess_step2.sh +++ /dev/null @@ -1,26 +0,0 @@ -#!/bin/bash - -#SBATCH --cpus-per-task 2 -#SBATCH --mem=5G -#SBATCH -p regular -#SBATCH --output=/vast/scratch/users/iskander.j/logs/preprocess_%A_%a.out -#SBATCH --job-name=preprocess -#SBATCH --array=1-35 - -module load topaz - -dataset_name=$(sed -n "$SLURM_ARRAY_TASK_ID"p meta/datasets.txt) -dataset_path=/vast/scratch/users/iskander.j/PartiNet_data_Aug2024 -#echo "Generating bounding box then splitting datasets into train, val, test...." -#./preprocess.py --dataset ${dataset_name} \ -# --datasets_path ${dataset_path} \ -# --bounding_box - -echo "Splitting datasets into train, val, test...." -./preprocess.py --dataset ${dataset_name} \ - --datasets_path ${dataset_path} \ - -echo "Done." - - - diff --git a/scripts/star_file_gen/generate-star-file.py b/scripts/star_file_gen/generate-star-file.py deleted file mode 100644 index d84295c..0000000 --- a/scripts/star_file_gen/generate-star-file.py +++ /dev/null @@ -1,107 +0,0 @@ -from absl import app -from absl import flags -from absl import logging -import math -import os - -import pandas as pd -import csv -import cv2 - -logging.set_verbosity(logging.ERROR) -FLAGS = flags.FLAGS - -flags.DEFINE_string('outputLabels', None, 'Path to micrograph labels') -flags.DEFINE_string('denoisedMRC', None, 'Path to Topaz denoised images') -flags.DEFINE_string('starFile', None, 'Path for output STAR file format for CryoSPARC') -flags.DEFINE_float('conf_thresh', 0.5, 'Desired Confidence for ') - - -def yolo_to_starfile(yolo_coords, image_width, image_height,diameters): - x_center = math.ceil(yolo_coords['x_centre'] * image_width) - y_center = math.ceil(yolo_coords['y_centre'] * image_height) - width = yolo_coords['width'] * image_width - height = yolo_coords['height'] * image_height - - # Calculate diameter - diameter = math.ceil(max(width, height)) - - diameters.append(diameter) - - # Return x_center, y_center, diameter - return x_center, y_center, diameter - - -# Modify the generate_output function to accept DynamicDet DataFrame -def generate_output(labels, filename, star_writer, img_width, img_height, diameters): - for index, row in labels.iterrows(): - # x_centre = row['X-Coordinate'] - # y_centre = row['Y-Coordinate'] - # diameter = row['Diameter'] - - x_centre, y_centre, diameter = yolo_to_starfile(row, img_width, img_height, diameters) - # print(x_centre, y_centre, diameter) - - star_writer.writerow([filename, x_centre, y_centre, diameter]) - -def main(argv): - # Read DynamicDet output from txt file - labels_path = FLAGS.outputLabels - images_path = FLAGS.denoisedMRC - star_out_path = FLAGS.starFile - - conf_thresh = FLAGS.conf_thresh - - diameters = list() - - with open(star_out_path, "w") as star_file: - star_writer = csv.writer(star_file, delimiter=' ') - star_writer.writerow([]) - star_writer.writerow(["data_"]) - star_writer.writerow([]) - star_writer.writerow(["loop_"]) - star_writer.writerow(["_rlnMicrographName", "#1"]) - star_writer.writerow(["_rlnCoordinateX", "#2"]) - star_writer.writerow(["_rlnCoordinateY", "#3"]) - star_writer.writerow(["_rlnDiameter", "#4"]) - - i = 1 - for image in os.listdir(images_path): - filename = image.split("/")[-1][:-4] - - print(i) - - i+=1 - - label_file_path = os.path.join(labels_path, str(filename + '.txt')) - - if not os.path.exists(label_file_path): - continue - - # label_file_path = os.path.join(labels_path, str(filename + '.csv')) - # labels = pd.read_csv(label_file_path) - - image = cv2.imread(os.path.join(images_path, image)) - img_width = image.shape[1] - img_height = image.shape[0] - - - custom_headers = ['class', 'x_centre', 'y_centre', 'width', 'height', 'conf'] - - # Read the CSV file with custom headers - labels = pd.read_csv(label_file_path, header=None, names=custom_headers, sep=' ') - - labels = labels[labels['conf'] > conf_thresh] - - # print(labels.head()) - - # label_file_path = os.path.join(labels_path, str(filename + '.csv')) - # labels = pd.read_csv(label_file_path) - - star_filename = str(filename + '.mrc') - - generate_output(labels, star_filename, star_writer, img_width, img_height, diameters) - - -if __name__ == '__main__': - app.run(main) \ No newline at end of file diff --git a/scripts/star_file_gen/generate-star-file.sh b/scripts/star_file_gen/generate-star-file.sh deleted file mode 100644 index 145a3ad..0000000 --- a/scripts/star_file_gen/generate-star-file.sh +++ /dev/null @@ -1,21 +0,0 @@ -#!/bin/bash - -#SBATCH --cpus-per-task 16 --mem=499G --gres=gpu:A30:4 -p gpuq -#SBATCH --output=generate-star-file.out --job-name=generate-star-file.py - -outputLabels = 'path/to/labels' -denoisedMRC = 'path/to/denoised/mrc' -starFile = 'path/to/star/file' -conf_thresh = 0.5 #default value - - -module load miniconda3/latest -conda activate /vast/projects/miti2324/envs/dynamicdet_mamba37/ - -python generate-star-file.py --outputLabels=$outputLabels --denoisedMRC=$denoisedMRC --starFile=$starFile --conf_thresh=$conf_thresh - - - - - - diff --git a/scripts/test.sh b/scripts/test.sh deleted file mode 100644 index d7da8d2..0000000 --- a/scripts/test.sh +++ /dev/null @@ -1,18 +0,0 @@ -#!/bin/bash - -#SBATCH --cpus-per-task 16 --mem=499G --gres=gpu:A100:2 -p gpuq --qos=bonus -#SBATCH --output=test.out --job-name=test - -module load miniconda3/latest -conda activate /vast/projects/miti2324/envs/dynamicdet_mamba37 - -python /vast/projects/miti2324/cryo_em_dynamic_det/DynamicDet/test.py \ - --img-size 640 --batch-size 1 --device 0 \ - --cfg /vast/projects/miti2324/cryo_em_dynamic_det/DynamicDet/cfg/dy-yolov7-step2.yaml \ - --weight /path/to/train_step2/last.pt \ - --data /path/to/test.yaml \ - --dy-thres 0 --task test --save-txt --save-conf \ - --name output_folder_name - - - \ No newline at end of file diff --git a/scripts/train_step1.sh b/scripts/train_step1.sh deleted file mode 100644 index 9a6c9d1..0000000 --- a/scripts/train_step1.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash - -#SBATCH --job-name=train_step1 -#SBATCH --time 100:00:00 -#SBATCH --cpus-per-task=20 -#SBATCH --mem 400G -#SBATCH --nodes=2 -#SBATCH --partition=gpuq -#SBATCH --gres=gpu:A30:4 -#SBATCH --output=train_step1.out -#SBATCH --qos=bonus - -export MASTER_ADDR=$(hostname -I | grep -o '10.11.\w*.\w*') MASTER_PORT=29500 -echo "MASTER_ADDR=${MASTER_ADDR}, MASTER_PORT=${MASTER_PORT}" -export NCCL_DEBUG=WARN NCCL_SOCKET_IFNAME=bond1.1330 NCCL_IB_DISABLE=1 - -srun torchrun --rdzv_id $RANDOM --rdzv_backend c10d \ - --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --nproc_per_node 4 --nnodes 2 \ - DynamicDet/train_step1.py \ - --workers 16 --device 0,1,2,3 --sync-bn --batch-size 16 \ - --epochs 100 \ - --img 640 \ - --cfg DynamicDet/cfg/dy-yolov7-step1.yaml \ - --weight '' \ - --data /path/to/cryo_training.yaml \ - --hyp DynamicDet/hyp/hyp.scratch.p5.yaml \ - --name train_step1 --save_period 10 diff --git a/scripts/train_step2.sh b/scripts/train_step2.sh deleted file mode 100644 index f836245..0000000 --- a/scripts/train_step2.sh +++ /dev/null @@ -1,14 +0,0 @@ -#!/bin/bash - -#SBATCH --cpus-per-task 16 --mem=499G --gres=gpu:A100:1 -p gpuq -#SBATCH --output=train_step2.out --job-name=train_step2 -#SBATCH --mail-type=ALL -#SBATCH --mail-user=aggarwal.m@wehi.edu.au - -python DynamicDet/train_step2.py \ - --workers 4 --device 0 --batch-size 1 --epochs 10 --img 640 --adam \ - --cfg DynamicDet/cfg/dy-yolov7-step2.yaml \ - --weight ./runs/train/train_step1/weights/epoch_049.pt \ - --data /vast/scratch/users/jain.o/data/cryo_training_22/cryo_training.yaml \ - --hyp DynamicDet/hyp/hyp.finetune.dynamic.adam.yaml \ - --name train_step2_epoch_049 diff --git a/scripts/visualise_denoise_results.ipynb b/scripts/visualise_denoise_results.ipynb deleted file mode 100644 index 0d94d27..0000000 --- a/scripts/visualise_denoise_results.ipynb +++ /dev/null @@ -1,158 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.patches import Circle\n", - "from PIL import Image\n", - "import glob\n", - "import os" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from topaz.utils.data.loader import load_image\n", - "import topaz.mrc as mrc" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "##Set Paths and filenames\n", - "name = 'stack_0289_2x_dfpt'\n", - "raw_path='/vast/scratch/users/iskander.j/PartiNet_data/10059/micrographs/'\n", - "denoised_path='/vast/scratch/users/iskander.j/PartiNet_data/10059/denoised_micrographs/jpg/'" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "cannot reshape array of size 6946528 into shape (1,3710,3838)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# load the raw micrograph\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmic_raw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mload_image\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m+\u001b[0m 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\u001b[0mheader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# , order='F')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 143\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnz\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 144\u001b[0m \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mreshape\u001b[0;34m(*args, **kwargs)\u001b[0m\n", - "\u001b[0;32m/vast/projects/miti2324/envs/topaz_env/lib/python3.6/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36mreshape\u001b[0;34m(a, newshape, order)\u001b[0m\n\u001b[1;32m 297\u001b[0m [5, 6]])\n\u001b[1;32m 298\u001b[0m \"\"\"\n\u001b[0;32m--> 299\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'reshape'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 301\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/vast/projects/miti2324/envs/topaz_env/lib/python3.6/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mbound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 59\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[0;31m# A TypeError occurs if the object does have such a method in its\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 6946528 into shape (1,3710,3838)" - ] - } - ], - "source": [ - "\n", - "# load the raw micrograph\n", - "mic_raw = np.array(load_image( os.path.join(raw_path,name + '.mrc')), copy=False)\n", - "# load the denoised micrograph\n", - "mic_dn = np.array(load_image( os.path.join(denoised_path, name + '.jpg')), copy=False)\n", - "\n", - "# scale them for visualization\n", - "mu = mic_dn.mean()\n", - "std = mic_dn.std()\n", - "\n", - "mic_raw = (mic_raw - mu)/std\n", - "mic_dn = (mic_dn - mu)/std\n", - "\n", - "_,ax = plt.subplots(1,2,figsize=(24,12))\n", - "\n", - "ax[0].imshow(mic_raw, vmin=-4, vmax=4, cmap='Greys_r')\n", - "ax[1].imshow(mic_dn, vmin=-4, vmax=4, cmap='Greys_r')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "## look at a detail of the micrograph\n", - "crop = (2000,3000,3500,4500)\n", - "crop=(2000,3000,0,2000)\n", - "_,ax = plt.subplots(1,2,figsize=(24,12))\n", - "\n", - "ax[0].imshow(mic_raw[crop[0]:crop[1],crop[2]:crop[3]], vmin=-4, vmax=4, cmap='Greys_r')\n", - "ax[1].imshow(mic_dn[crop[0]:crop[1],crop[2]:crop[3]], vmin=-4, vmax=4, cmap='Greys_r')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "py3_11", - "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": "3.11.0" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -}