Metadata-Version: 2.1
Name: deformacion_lib
Version: 0.1.0
Summary: Library for deformation analysis in tensile tests and 3D structures
Author: Martin Esteban, Oier Labeaga, Malen Ubiria, Eneko Urrutia
Author-email: martin.esteban@alumni.mondragon.edu, oier.labeaga@alumni.mondragon.edu, malen.ubiria@alumni.mondragon.edu, eneko.urrutia@alumni.mondragon.edu
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: opencv-python>=4.5.0
Requires-Dist: numpy>=1.19.0
Requires-Dist: matplotlib>=3.3.0
Requires-Dist: pandas>=1.1.0
Requires-Dist: Pillow>=8.0.0
Requires-Dist: imageio>=2.9.0

# deformacion_lib

Python library for deformation analysis in materials through image and video processing.

## Installation
```bash
pip install deformacion_lib
```

## Features

1. **Tensile test analysis**: Measures strain and elongation in tensile tests
2. **3D structure analysis**: Analyzes deformation in 3D printed structures

---

## Experimental Setup

### General Requirements (Both Experiments)

#### Camera Settings
- **High resolution**: Use the highest resolution available on your device
- **High frame rate**: Use slow-motion mode if available (more frames = better analysis)
- **Fixed camera**: Use a tripod or stable support - DO NOT move the camera during the experiment
- **Close framing**: Frame as close as possible to minimize cropping later while maintaining full view of the deformation area
- **Centered focus**: Keep the deformation area centered both vertically and horizontally

#### Lighting
- **Uniform lighting**: Ensure even illumination across the sample
- **Minimize shadows**: Reduce shadows as much as possible, especially on the structure
- **Light shadows**: If shadows are unavoidable, make them as light as possible (less dark = better detection)
- **No reflections**: Avoid reflections on the sample surface

#### Background
- **High contrast**: Use a background that contrasts strongly with the sample
  - Dark sample → Light background
  - Light sample → Dark background

#### File Format
- **Video**: Must be `.mp4` format
- **Photos**: Any format accepted, but `.jpg` or `.png` preferred

#### Timing
- **Accurate timing**: Use a stopwatch or timer to measure the exact experiment duration for the `tiempo_experimento` parameter

---

### Tensile Test Specific Setup

#### Sample Preparation
- **Two reference lines**: Paint or mark two **thick, dark, horizontal lines** on the sample
- **High visibility**: Lines must be clearly visible and well-defined
- **Perfectly horizontal**: Use a level to ensure lines are horizontal
- **Measure L0**: Use a caliper or precise ruler to measure the initial distance between the two lines (`L0_mm`)

#### Camera Positioning
- **Centered**: Keep the deformation area centered in the frame
- **Full view**: Ensure both reference lines are fully visible throughout the test

---

### 3D Structure Specific Setup

#### Sample Preparation
- **Dark edges**: Paint the entire perimeter/edges of the structure with a **very dark color** (black preferred)
- **Clean edges**: Ensure edges are well-defined and visible
- **Measure total distance**: Measure the total base width of the structure (the distance between your two palms if you were holding it fully stretched)
  - This includes the 5mm left wall
  - Use a caliper for precision

#### Camera Positioning
- **Centered**: Keep the structure centered in the frame
- **Wall visibility**: Ensure at least a large portion of both walls (left and right) are visible
  - The entire left wall does NOT need to be visible, but a significant part should be
- **Full deformation area**: Frame the region where deformation will occur

---

## Usage

### 1. Tensile Test Analysis
```python
from deformacion_lib.tensile import ejecutar_analisis_tensile

# For video
ejecutar_analisis_tensile(
    modo="video",
    nombre_archivo="tensile_test.mp4",
    tiempo_experimento=91.5,  # seconds (measured with stopwatch)
    L0_mm=35.0,              # initial distance between lines (mm)
    n_frames=100,
    ext_imagen=".tif",
    duracion_gifs=2.0
)

# For photo sequence
ejecutar_analisis_tensile(
    modo="foto",
    nombre_archivo="image_folder",
    tiempo_experimento=91.5,
    L0_mm=35.0,
    ext_imagen=".jpg",
    duracion_gifs=2.0
)
```

**Parameters:**
- `modo`: "video" or "foto"
- `nombre_archivo`: Video filename or image folder name
- `tiempo_experimento`: Total experiment duration (seconds)
- `L0_mm`: Initial distance between the two black reference lines (mm)
- `n_frames`: Number of frames to extract from video (only if modo="video")
- `ext_imagen`: Image extension (default: ".tif")
- `duracion_gifs`: Duration of generated GIFs (seconds, default: 2.0)

**Output:**
- `Results (Tensile Test Program)/analysis/`: Strain and elongation graphs + CSV data
- `Results (Tensile Test Program)/visual/`: Animated GIFs

---

### 2. 3D Structure Analysis
```python
from deformacion_lib.estructura3d import ejecutar_analisis_3d

# For video
ejecutar_analisis_3d(
    modo="video",
    nombre_archivo="structure_test.mp4",
    tiempo_experimento=0.23,      # seconds
    distancia_total_mm=39.0,      # total base width including 5mm left wall
    n_frames=150,
    ext_imagen=".png",
    duracion_gifs=2.0
)

# For photo sequence
ejecutar_analisis_3d(
    modo="foto",
    nombre_archivo="image_folder",
    tiempo_experimento=0.23,
    distancia_total_mm=39.0,
    ext_imagen=".png",
    duracion_gifs=2.0
)
```

**Parameters:**
- `modo`: "video" or "foto"
- `nombre_archivo`: Video filename or image folder name
- `tiempo_experimento`: Total experiment duration (seconds)
- `distancia_total_mm`: Total base width of the structure (includes 5mm left wall)
- `n_frames`: Number of frames to extract from video (only if modo="video")
- `ext_imagen`: Image extension (default: ".png")
- `duracion_gifs`: Duration of generated GIFs (seconds, default: 2.0)

**Output:**
- `Results (3D Printed Body Program)/Analysis/`: Deformation graphs and CSV data
- `Results (3D Printed Body Program)/Visualization/`: GIFs and visual comparisons

---

## Requirements

- Python >= 3.8
- opencv-python >= 4.5.0
- numpy >= 1.19.0
- matplotlib >= 3.3.0
- pandas >= 1.1.0
- Pillow >= 8.0.0
- imageio >= 2.9.0

## License

MIT License

## Authors

- Martin Esteban
- Oier Labeaga
- Malen Ubiria
- Eneko Urrutia
