fix fish weight calculation by using correct parameters. output video

FishMeasure/measure/README.md

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+# Fish Weight Prediction using PointNet++
+
+This module uses PointNet++ to predict fish weight from partial point cloud data.
+
+## Overview
+
+We provide two approaches for fish weight prediction:
+
+### Approach 1: Direct Regression (Original)
+Directly predict the absolute weight from a single point cloud.
+
+### Approach 2: Comparison-Based (Recommended)
+Predict the weight **difference** between two point clouds. For a new fish:
+1. Find the closest reference fish by length (from known dataset)
+2. Predict weight difference using the trained model
+3. Calculate: `new_weight = reference_weight + predicted_difference`
+
+**Why comparison-based?**
+- Relative comparisons are often more accurate than absolute predictions
+- Leverages known reference data
+- More robust to incomplete/partial point clouds
+- Better performance with small datasets (~100 samples)
+
+## Workflow
+
+### Approach 1: Direct Regression
+
+1. **Dataset Preparation** (`dataset.py`): Prepare training data from multiple point clouds
+2. **Training** (`train_weight_regression.py`): Train PointNet++ model for weight regression
+3. **Testing/Inference** (`test_weight_regression.py`): Test the trained model on new point clouds
+
+### Approach 2: Comparison-Based (Recommended)
+
+1. **Dataset Preparation** (`dataset.py`): Same as Approach 1
+2. **Training** (`train_weight_comparison.py`): Train PointNet++ model to predict weight differences
+3. **Testing/Inference** (`test_weight_comparison.py`): Test using reference dataset
+
+## Dataset Preparation
+
+The `dataset.py` script processes point clouds from an input folder:
+
+- **Input**: Folder containing multiple point cloud subfolders (e.g., `output_preview/xxxx/cloud/`)
+- **Process**:
+  1. For each subfolder, find all PLY files
+  2. Select the point cloud with the largest length (max x - min x)
+  3. Normalize the point cloud by moving it to the center of origin (centroid = 0)
+  4. Save the normalized PLY file and corresponding weight label to the output folder
+
+**Usage**:
+```bash
+python3 measure/dataset.py --input /path/to/pointclouds --labels /path/to/labels.csv --output /path/to/dataset
+```
+
+**Label CSV Format**:
+```csv
+subfolder_name,weight
+HD1080_SN43186771_16-41-37,0.5
+HD1080_SN43186771_16-41-40,0.6
+...
+```
+
+## Training
+
+The `train_weight_regression.py` script trains a PointNet++ model for weight regression:
+
+- **Model**: PointNet++ (SSG - Single Scale Grouping) adapted for regression
+- **Data Augmentation** (for 150 samples):
+  - Random point sampling (different number of points)
+  - Random rotation around z-axis
+  - Random scaling (small variations)
+  - Random jitter (noise)
+  - Random point dropout
+
+**Usage**:
+```bash
+python3 measure/train_weight_regression.py \
+    --data_path /path/to/dataset \
+    --batch_size 16 \
+    --num_point 1024 \
+    --epoch 200 \
+    --learning_rate 0.001
+```
+
+## Testing/Inference
+
+The `test_weight_regression.py` script performs inference on new point clouds:
+
+**Usage**:
+```bash
+# Test on a single point cloud
+python3 measure/test_weight_regression.py \
+    --model /path/to/checkpoint.pth \
+    --ply /path/to/pointcloud.ply
+
+# Test on a folder of point clouds
+python3 measure/test_weight_regression.py \
+    --model /path/to/checkpoint.pth \
+    --folder /path/to/pointclouds \
+    --output results.json
+```
+
+## Comparison-Based Approach (Recommended)
+
+### Training
+
+Train a model to predict weight differences between point cloud pairs:
+
+```bash
+python3 measure/train_weight_comparison.py \
+    --data_path /path/to/dataset \
+    --reference_folder /path/to/reference/dataset \
+    --batch_size 8 \
+    --num_point 1024 \
+    --epoch 200 \
+    --learning_rate 0.001 \
+    --pair_strategy random  # or 'length_based'
+```
+
+**Pair Strategies:**
+- `random`: Random pairs (more diverse training)
+- `length_based`: Pair based on similar lengths (more realistic comparisons)
+
+### Testing
+
+For a new fish point cloud, find the closest reference and predict weight difference:
+
+```bash
+# Test on a single point cloud
+python3 measure/test_weight_comparison.py \
+    --model /path/to/checkpoint.pth \
+    --reference_folder /path/to/reference/dataset \
+    --ply /path/to/new_fish.ply
+
+# Test on a folder of point clouds
+python3 measure/test_weight_comparison.py \
+    --model /path/to/checkpoint.pth \
+    --reference_folder /path/to/reference/dataset \
+    --folder /path/to/new_fishes \
+    --output results.json
+```
+
+**How it works:**
+1. Loads all reference point clouds (with known weights and lengths)
+2. For each new fish, finds the closest reference by length
+3. Predicts weight difference: `predicted_diff = model(reference_pc, new_pc)`
+4. Calculates weight: `new_weight = reference_weight + predicted_diff`
+
+## File Structure
+
+```
+measure/
+├── README.md                      # This file
+├── dataset.py                     # Dataset preparation script
+├── train_weight_regression.py     # Direct regression training
+├── test_weight_regression.py       # Direct regression inference
+├── train_weight_comparison.py      # Comparison-based training
+├── test_weight_comparison.py       # Comparison-based inference
+├── pointnet2_regression.py         # Direct regression model
+├── pointnet2_comparison.py         # Comparison model
+├── data_loader.py                  # Direct regression data loader
+├── data_loader_comparison.py       # Comparison data loader
+└── data/                           # Data folder (for OCR results, etc.)
+```
+
+## Notes
+
+- **Direct Regression**: Predicts absolute weight from a single point cloud
+- **Comparison-Based**: Predicts weight difference between two point clouds (recommended for small datasets)
+- Point clouds are normalized to the origin before training/inference
+- Data augmentation is crucial given the small dataset size (~100-150 samples)
+- The comparison model uses shared PointNet++ encoder for both point clouds, then concatenates features
+