### Installation Steps

Source: https://github.com/baidut/paq2piq/blob/master/README.md

Clone the repository, set up a virtual environment, and install dependencies.

```bash
git clone https://github.com/baidut/paq2piq
cd paq2piq
virtualenv -p python3.6 env
source ./env/bin/activate
pip install -r requirements.txt
```

--------------------------------

### Run Minimal PyTorch Example

Source: https://github.com/baidut/paq2piq/blob/master/demo.ipynb

This Python code snippet initializes the inference model and predicts quality from a downloaded image file.

```python
from paq2piq_standalone import *

model = InferenceModel(RoIPoolModel(), 'RoIPoolModel.pth')
model.blk_size = (3, 5)
model.predict_from_file("Picture1.jpg")
```

--------------------------------

### Install Project Dependencies

Source: https://github.com/baidut/paq2piq/blob/master/demo.ipynb

Install all required Python packages listed in the 'requirements.txt' file.

```bash
!pip install -r requirements.txt
```

--------------------------------

### Load Image and Perform Inference

Source: https://github.com/baidut/paq2piq/blob/master/demo.ipynb

Loads an image and performs inference using a specified model. Ensure PIL and the paq2piq library are installed, and the model weights file is available.

```python
from PIL import Image
from paq2piq.inference_model import *

image = Image.open("images/Picture1.jpg")
model = InferenceModel(RoIPoolModel(), 'RoIPoolModel.pth')
output = model.predict_from_pil_image(image)
```

--------------------------------

### Getting Image Score with Pure PyTorch

Source: https://github.com/baidut/paq2piq/blob/master/README.md

Use the CLI to get a quality score for a single image.

```bash
python cli.py get-image-score --path_to_model_state $PATH_TO_MODEL \
--path_to_image test_image.jpg
```

--------------------------------

### Get Image Score via CLI

Source: https://github.com/baidut/paq2piq/blob/master/demo.ipynb

Executes the `get-image-score` command using the CLI, specifying the model state and image path. This is a practical example of using the command-line tool.

```bash
!python cli.py get-image-score --path_to_model_state RoIPoolModel.pth \
--path_to_image images/Picture1.jpg
```

--------------------------------

### Adapt Model for New Image Domain (Get Normalization Params)

Source: https://github.com/baidut/paq2piq/blob/master/demo.ipynb

This snippet adapts the inference model using images from a specified directory to obtain new normalization parameters. Ensure the 'paq2piq_standalone' module and a pre-trained model are available.

```python
model = InferenceModel(RoIPoolModel(), 'RoIPoolModel.pth')
model.adapt_from_dir('CLIVE/Images')
```

--------------------------------

### Visualize Image, Blended Output, and Local Scores

Source: https://github.com/baidut/paq2piq/blob/master/demo.ipynb

Displays the original image, the blended output, and the local scores using Matplotlib. Ensure Matplotlib is installed.

```python
import matplotlib.pyplot as plt
fig, axes = plt.subplots(1, 3, figsize=(12, 8 * 3))

axes[0].imshow(image)
axes[1].imshow(blended)
axes[2].imshow(output['local_scores'], cmap='gray', vmin=0, vmax=100)
```

--------------------------------

### Tracking Training Progress with TensorBoard

Source: https://github.com/baidut/paq2piq/blob/master/README.md

Launch TensorBoard to monitor training progress. Ensure you are in the correct directory.

```bash
tensorboard --logdir .
```

--------------------------------

### Display CLI Help

Source: https://github.com/baidut/paq2piq/blob/master/demo.ipynb

Shows the general help message for the paq2piq command-line interface. This is useful for understanding available commands.

```bash
!python cli.py --help
```

--------------------------------

### Download Pre-trained Model (again)

Source: https://github.com/baidut/paq2piq/blob/master/demo.ipynb

Download the pre-trained RoIPoolModel again. This command is identical to the one in the first snippet.

```bash
!wget -O RoIPoolModel.pth -N https://github.com/baidut/PaQ-2-PiQ/releases/download/v1.0/RoIPoolModel-fit.10.bs.120.pth
```

--------------------------------

### Download Model and Test Image

Source: https://github.com/baidut/paq2piq/blob/master/demo.ipynb

Use these commands to download the pre-trained RoIPoolModel and a sample image for testing.

```bash
# download the model
!wget -O RoIPoolModel.pth -N https://github.com/baidut/PaQ-2-PiQ/releases/download/v1.0/RoIPoolModel-fit.10.bs.120.pth
```

```bash
# download a test image
!wget -N https://github.com/baidut/PaQ-2-PiQ/releases/download/v1.0/Picture1.jpg
```

```bash
# download the standalone version of code
!wget -N https://raw.githubusercontent.com/baidut/PaQ-2-PiQ_GAE/master/paq2piq_standalone.py
```

--------------------------------

### Change Directory to Cloned Repository

Source: https://github.com/baidut/paq2piq/blob/master/demo.ipynb

Navigate into the cloned 'paq2piq' directory.

```python
import os
os.chdir('paq2piq/')
```

--------------------------------

### Training Model with Pure PyTorch

Source: https://github.com/baidut/paq2piq/blob/master/README.md

Execute the training script using configured environment variables.

```bash
python cli.py train_model --path_to_save_csv $PATH_TO_CSV \
                                --path_to_images $PATH_TO_IMAGES \
                                --batch_size $BATCH_SIZE \
                                --num_workers $NUM_WORKERS \
                                --num_epoch $NUM_EPOCH \
                                --init_lr $INIT_LR \
                                --experiment_dir_name $EXPERIMENT_DIR_NAME
```

--------------------------------

### Clone PaQ-2-PiQ Repository

Source: https://github.com/baidut/paq2piq/blob/master/demo.ipynb

Clone the PaQ-2-PiQ GitHub repository to your local machine.

```bash
!git clone https://github.com/baidut/paq2piq/
```

--------------------------------

### Training with PyTorch Lightning

Source: https://github.com/baidut/paq2piq/blob/master/README.md

Instantiate a PyTorch Lightning module and train it using the Trainer.

```python
from pytorch_lightning_module import *
module = RoIPoolLightningModule()
trainer = pl.Trainer(gpus=[0])    
trainer.fit(module)
```

--------------------------------

### Display `get-image-score` CLI Help

Source: https://github.com/baidut/paq2piq/blob/master/demo.ipynb

Shows the help message for the `get-image-score` command, detailing required options like model path and image path.

```bash
!python cli.py get-image-score --help
```

--------------------------------

### Inference from File

Source: https://github.com/baidut/paq2piq/blob/master/README.md

Load a pre-trained model and predict image quality from a file path.

```python
model = InferenceModel(RoIPoolModel(), 'models/RoIPoolModel.pth')
output = model.predict_from_file("images/Picture1.jpg")
```

--------------------------------

### Download and Extract CLIVE Database

Source: https://github.com/baidut/paq2piq/blob/master/demo.ipynb

Download the CLIVE database and extract its contents for use in model adaptation.

```bash
# download CLIVE database
!wget -N  https://github.com/baidut/PaQ-2-PiQ/releases/download/v1.0/CLIVE.tgz
!tar zxvf CLIVE.tgz
```

--------------------------------

### Setting Environment Variables for Pure PyTorch Training

Source: https://github.com/baidut/paq2piq/blob/master/README.md

Configure environment variables for model path, image data, batch size, and training epochs.

```bash
export PYTHONPATH=.
export PATH_TO_MODEL=models/RoIPoolModel.pth
export PATH_TO_IMAGES=/storage/DATA/images/
export PATH_TO_CSV=/storage/DATA/FLIVE/
export BATCH_SIZE=16
export NUM_WORKERS=2
export NUM_EPOCH=50
export INIT_LR=0.0001
export EXPERIMENT_DIR_NAME=/storage/experiment_n0001
```

--------------------------------

### Visualize Category Distribution (Initial)

Source: https://github.com/baidut/paq2piq/blob/master/demo.ipynb

This code visualizes the distribution of categories using pandas and matplotlib after loading the model. It requires the 'paq2piq_standalone' module and a pre-trained model.

```python
import pandas as pd
from paq2piq_standalone import *
model = InferenceModel(RoIPoolModel(), 'RoIPoolModel.pth')
pd.Series(categories).value_counts().reindex(model.categories).plot(kind='bar')
```

--------------------------------

### Blend Inference Output with Image

Source: https://github.com/baidut/paq2piq/blob/master/demo.ipynb

Combines the original image with the inference output for visualization. Requires the `blend_output` function from `paq2piq.common`.

```python
from paq2piq.common import blend_output
blended = blend_output(image, output, vmin=None, vmax=None)
```

--------------------------------

### Validating Model with Pure PyTorch

Source: https://github.com/baidut/paq2piq/blob/master/README.md

Run the validation script to evaluate the model on validation and test datasets.

```bash
python cli.py validate_model --path_to_model_state $PATH_TO_MODEL \
                                    --path_to_save_csv $PATH_TO_CSV \
                                    --path_to_images $PATH_TO_IMAGES \
                                    --batch_size $BATCH_SIZE \
                                    --num_workers $NUM_EPOCH
```

--------------------------------

### Output Dictionary Structure

Source: https://github.com/baidut/paq2piq/blob/master/README.md

Explains the structure of the output dictionary from model predictions.

```python
output['global_score'] # a float scale number indicating the predicted global quality
output['local_scores']  # a 20x20 numpy array indicating the predicted  local quality scores
output['category']  # From low to high quality: 'Bad', 'Poor', 'Fair', 'Good', 'Excellent'
```

--------------------------------

### Adapt Model and Predict with Custom Parameters

Source: https://github.com/baidut/paq2piq/blob/master/demo.ipynb

This code adapts the model using custom normalization parameters and a modified N_std value, then predicts categories for images in a directory. It requires the 'paq2piq_standalone' module and pandas.

```python
from paq2piq_standalone import *
import pandas as pd

model = InferenceModel(RoIPoolModel(), 'RoIPoolModel.pth')
model.norm_params = (73, 9.257462, 3.1872356)
model.N_std = 2
new_categories = [model.predict_from_file(f)['category'] for f in img_files_in('CLIVE/Images')]
pd.Series(new_categories).value_counts().reindex(model.categories).plot(kind='bar')
```

--------------------------------

### Adapt Model with New Peak and Spread

Source: https://github.com/baidut/paq2piq/blob/master/demo.ipynb

This snippet demonstrates adapting the model with a specific 'new_peak' value and 'N_std' to influence classification results, then predicts categories for images and visualizes the distribution. Requires 'paq2piq_standalone' and pandas.

```python
from paq2piq_standalone import *
import pandas as pd

model = InferenceModel(RoIPoolModel(), 'RoIPoolModel.pth')
model.norm_params = (73, 9.257462, 3.1872356)
model.new_peak = 50 
model.N_std = 2.5 # the smaller, more spread-out 
new_categories = [model.predict_from_file(f)['category'] for f in img_files_in('CLIVE/Images')]
pd.Series(new_categories).value_counts().reindex(model.categories).plot(kind='bar')
```

--------------------------------

### Inference from PIL Image

Source: https://github.com/baidut/paq2piq/blob/master/README.md

Load a pre-trained model and predict image quality from a PIL Image object.

```python
model = InferenceModel(RoIPoolModel(), 'models/RoIPoolModel.pth')
image = Image.open("images/Picture1.jpg")
output = model.predict_from_pil_image(image)
```

--------------------------------

### Check Local Scores Type

Source: https://github.com/baidut/paq2piq/blob/master/demo.ipynb

Verifies the data type of the local scores. This helps in understanding the structure of the output.

```python
type(output['local_scores'])
```

--------------------------------

### Access Global Score

Source: https://github.com/baidut/paq2piq/blob/master/demo.ipynb

Retrieves the global quality score from the inference output. This is a direct access to a key in the output dictionary.

```python
output['global_score']
```

--------------------------------

### Check Local Scores Shape

Source: https://github.com/baidut/paq2piq/blob/master/demo.ipynb

Determines the dimensions of the local scores array. This is useful for understanding the spatial resolution of the scores.

```python
output['local_scores'].shape
```

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