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Repository: amoudgl/pygoturn
Branch: master
Commit: 1785ca7106fc
Files: 19
Total size: 2.4 MB

Directory structure:
gitextract_y95ebmd8/

├── .gitignore
├── LICENSE
├── README.md
├── data/
│   ├── OTB/
│   │   └── Man/
│   │       ├── cfg.mat
│   │       └── groundtruth_rect.txt
│   ├── download.sh
│   ├── setup.sh
│   └── untar.sh
├── notebooks/
│   └── VisualizeBatches.ipynb
├── requirements.txt
└── src/
    ├── boundingbox.py
    ├── datasets.py
    ├── demo.py
    ├── evaluate.py
    ├── goturn.py
    ├── helper.py
    ├── model.py
    ├── test.py
    └── train.py

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FILE CONTENTS
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================================================
FILE: .gitignore
================================================
*.pyc


================================================
FILE: LICENSE
================================================
MIT License

Copyright (c) 2018 Abhinav Moudgil

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


================================================
FILE: README.md
================================================
# PyTorch GOTURN tracker

This is the PyTorch implementation of GOTURN visual tracker (Held. et. al, ECCV 2016). GOTURN is one of the key trackers which proposed an alternative deep learning approach to object tracking by learning a comparator function.

![](images/goturn.png)

### Why PyTorch implementation?
Although author's original [C++ Caffe implementation](https://github.com/davheld/GOTURN) and [this](https://github.com/nrupatunga/PY-GOTURN) Python Caffe implementation are well-documented, I feel a PyTorch implementation would be more readable and much easier to adapt for further research. Hence, this is my humble attempt to reproduce GOTURN from scratch in PyTorch which includes data loading, training and inference. I hope this is a useful contribution to the vision community.

## Highlights

* Supports **PyTorch 1.0 and Python3**.
* **Reproduces GOTURN** end to end in PyTorch including training and inference.
* Provides **pretrained PyTorch GOTURN** model.
* **Fast:** Tracks target objects at 100+ fps.
* **Benchmark:** Evaluation on OTB50 and OTB100.

## Output

![](images/pygoturn_output.gif)

## Environment

PyTorch 1.0 and Python3 recommended.

```
numpy==1.14.5
torch==1.0.0
opencv-python==4.0.0.21
torchvision==0.2.1
tensorboardX==1.6
```
To install all the packages, do `pip3 install -r requirements.txt`.

## Demo

### [Download pretrained model](https://drive.google.com/file/d/1szpx3J-hfSrBEi_bze3d0PjSfQwNij7X/view?usp=sharing)

Navigate to `pygoturn/src` and do:

```
python3 demo.py -w /path/to/pretrained/model
```

Images with bounding box predictions will be saved in `pygoturn/result` directory.

Arguments:

`-w / --model-weights`: Path to a PyTorch pretrained model checkpoint.   
`-d / --data-directory`: Path to a tracking sequence which follows [OTB format](http://cvlab.hanyang.ac.kr/tracker_benchmark/datasets.html).   
`-s / --save-directory`: Directory to save sequence images with predicted bounding boxes.   

## Benchmark

To evaluate PyTorchGOTURN on OTB50 and OTB100, follow the steps below:

- Install [got10k toolkit](https://github.com/got-10k/toolkit).
     ```
     pip install --upgrade got10k
     ```
- [Download pretrained model](https://drive.google.com/file/d/1szpx3J-hfSrBEi_bze3d0PjSfQwNij7X/view?usp=sharing).
- Edit OTB dataset path and model path appropriately in `src/evaluate.py`. The script will automatically download OTB dataset at the path provided.
- Run evaluation script:
    ```
    python3 evaluate.py
    ```

## Performance

| Dataset | AUC | Precision  |
| -------|:--------:| -----:|
| OTB50  | 0.401 | 0.548 |
| OTB100 | 0.405 |  0.550 |

As per [foolwood/benchmark_results](https://github.com/foolwood/benchmark_results), the original Caffe GOTURN yields AUC: 0.427 and Precision: 0.572 on OTB100. I feel this minor difference in performance is due to difference in the way ImageNet models are trained in Caffe and PyTorch like input normalization, layer specific learning rates etc. In this repository, I followed exact GOTURN hyperparameters which may not be the best for PyTorch. I feel with some hyperparameter tuning, GOTURN performance can be reproduced with an end-to-end PyTorch model.

Feel free to contribute to this project, if you have any improvements!


## Fast inference

In order to benchmark results for a tracking sequence or do fast inference, run the following command:
```
python3 test.py -w ../checkpoints/pytorch_goturn.pth.tar -d ../data/OTB/Man
```

**Arguments:**

`-w / --model-weights`: Path to a PyTorch pretrained model checkpoint.  
`-d / --data-directory`: Path to a tracking sequence which follows [OTB format](http://cvlab.hanyang.ac.kr/tracker_benchmark/datasets.html).       

## Training

Please follow the steps below for data preparation and training a pygoturn model from scratch.

![](images/pygoturn_loss.jpg)

### Prepare training data
Navidate to `pygoturn/data`.

Either use `download.sh` script to automatically download all datasets or manually download them from the links below in `pygoturn/data`:
- [ILSVRC2014_DET_train.tar](http://image-net.org/image/ilsvrc2014/ILSVRC2014_DET_train.tar) (47GB)
- [ILSVRC2014_DET_bbox_train.tgz](http://image-net.org/image/ilsvrc2014/ILSVRC2014_DET_bbox_train.tgz) (15MB)
- [alov300++_frames.zip](http://isis-data.science.uva.nl/alov/alov300++_frames.zip) (10GB)
- [alov300++GT_txtFiles.zip](http://isis-data.science.uva.nl/alov/alov300++GT_txtFiles.zip) (300KB)

Once you have all the above files in `pygoturn/data`, use `pygoturn/data/setup.sh` script to setup datasets in the way pygoturn training script `/src/train.py` expects OR follow the manual steps below:

- Untar `ILSVRC2014_DET_train.tar`. You'll have a directory `ILSVRC2014_DET_train` containing multiple tar files.
- First, delete all the tar files in `ILSVRC2014_DET_train` directory which start with name `ILSVRC2013`. This is an important step to reproduce the *exact* same number of ImageNet training samples (239283) as described in GOTURN paper.
- Untar all the remaining tar files in `ILSVRC2014_DET_train`. When done, *delete* all `*.tar` files. Since there are several `tar` files to untar, you can use `data/untar.sh` script. Just copy `untar.sh` to `ILSVRC2014_DET_train` directory and do: `./untar.sh`. Delete `untar.sh` from `data/ILSVRC2014_DET_train` when you are done.
- Untar `ILSVRC2014_DET_bbox_train.tgz`.
- Unzip `alov300++_frames.zip` and `alov300++GT_txtFiles.zip`.

Once you finish data preparation, make sure that you have the following directories:

```
data/ILSVRC2014_DET_train
data/ILSVRC2014_DET_bbox_train
data/imagedata++
data/alov300++_rectangleAnnotation_full
```

### Kick off training!
Navigate to `pygoturn/src` and run the following command to train GOTURN with default parameters:

```
python3 train.py
```
All the parameters for GOTURN training can be passed as arguments. View `pygoturn/src/train.py` for more details regarding arguments.


## Citation

If you find this code useful in your research, please cite:

```
@inproceedings{held2016learning,
  title={Learning to Track at 100 FPS with Deep Regression Networks},
  author={Held, David and Thrun, Sebastian and Savarese, Silvio},
  booktitle={European Conference Computer Vision (ECCV)},
  year={2016}
}
```

## Acknowledgements

- I'd like to thank the original authors for releasing a clean C++ implementation [[davheld/GOTURN](https://github.com/davheld/GOTURN)] and it was heavily referenced to tune hyperparameters appropriately.   
- This python caffe implementation [[nrupatunga/PY-GOTURN](https://github.com/nrupatunga/PY-GOTURN)] was pretty useful to understand GOTURN batch formation procedure. I borrowed some of its parts and adapted it to Pytorch.


## License

MIT


================================================
FILE: data/OTB/Man/groundtruth_rect.txt
================================================
69,48,26,39
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71,50,26,39


================================================
FILE: data/download.sh
================================================
#!/bin/bash
echo 'Downloading ImageNet training images...'
wget http://image-net.org/image/ilsvrc2014/ILSVRC2014_DET_train.tar
echo 'Downloading ImageNet training bounding boxes...'
wget http://image-net.org/image/ilsvrc2014/ILSVRC2014_DET_bbox_train.tgz
echo 'Downloading ALOV dataset...'
wget http://isis-data.science.uva.nl/alov/alov300++_frames.zip
wget http://isis-data.science.uva.nl/alov/alov300++GT_txtFiles.zip
echo 'Done'

================================================
FILE: data/setup.sh
================================================
#!/bin/bash

# setup imagenet images 
tar -xvf ILSVRC2014_DET_train.tgz
cp ./untar.sh ./ILSVRC2014_DET_train/ && cd ./ILSVRC2014_DET_train/
# delete ILSVRC2013* data to exactly match GOTURN training images 
rm -rf ILSVRC2013*
./untar.sh
rm -rf *.tar
rm untar.sh && cd ..

# setup imagenet bounding boxes
tar -xvf ILSVRC2014_DET_bbox_train.tgz

# setup alov images + bounding boxes
unzip alov300++_frames.zip
unzip alov300++GT_txtFiles.zip


================================================
FILE: data/untar.sh
================================================
while read -r line
do
    tar -xvf $line
done < <(find . -name "*.tar")

================================================
FILE: notebooks/VisualizeBatches.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from datasets import ALOVDataset, ILSVRC2014_DET_Dataset\n",
    "from torchvision import transforms\n",
    "from helper import *\n",
    "import argparse\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load ALOV and ImageNet datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "use_gpu = torch.cuda.is_available()\n",
    "kSaveModel = 20000 # save model after every 20000 steps\n",
    "kGeneratedExamplesPerImage = 10; # generate 10 synthetic samples per image in a dataset\n",
    "transform = transforms.Compose([Normalize(), ToTensor()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parsing ALOV dataset...\n",
      "ALOV dataset parsing done.\n",
      "Total number of annotations in ALOV Dataset = 16023\n"
     ]
    }
   ],
   "source": [
    "alov = ALOVDataset('../data/alov300/imagedata++/',\n",
    "                   '../data/alov300/alov300++_rectangleAnnotation_full/',\n",
    "                   transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parsing ImageNet dataset...\n",
      "ImageNet dataset parsing done.\n",
      "Total number of annotations in ImageNet Dataset = 239283\n"
     ]
    }
   ],
   "source": [
    "lambda_shift_frac = 5\n",
    "lambda_scale_frac = 15\n",
    "min_scale = -0.4\n",
    "max_scale = 0.4\n",
    "imagenet = ILSVRC2014_DET_Dataset('../data/imagenet_img/',\n",
    "                                   '../data/imagenet_bbox/',\n",
    "                                   transform,\n",
    "                                   lambda_shift_frac,\n",
    "                                   lambda_scale_frac,\n",
    "                                   min_scale,\n",
    "                                   max_scale)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize batch of 1 true + 10 synthetic (random crop) samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\" Given a dataset, returns pytorch tensor batch of 11 samples \"\"\" \n",
    "def make_tensor_samples(dataset, args):\n",
    "    transform = transforms.Compose([ToTensor()])\n",
    "    idx = np.random.randint(dataset.len, size=1)[0]\n",
    "    orig_sample = dataset.get_orig_sample(idx) # unscaled original sample (single image and bb)\n",
    "    true_sample = dataset.get_sample(idx) # cropped scaled sample (two frames and bb)\n",
    "    true_tensor = transform(true_sample)\n",
    "\n",
    "    origimg = orig_sample['image']\n",
    "    origbb = orig_sample['bb']\n",
    "    x1_batch = torch.Tensor(kGeneratedExamplesPerImage + 1, 3, 227, 227)\n",
    "    x2_batch = torch.Tensor(kGeneratedExamplesPerImage + 1, 3, 227, 227)\n",
    "    y_batch = torch.Tensor(kGeneratedExamplesPerImage + 1, 4)\n",
    "\n",
    "    # initialize batch with the true sample\n",
    "    x1_batch[0,:,:,:] = true_tensor['previmg']\n",
    "    x2_batch[0,:,:,:] = true_tensor['currimg']\n",
    "    y_batch[0,:] = true_tensor['currbb']\n",
    "\n",
    "    for i in range(kGeneratedExamplesPerImage):\n",
    "        sample = {'image': origimg, 'bb': origbb}\n",
    "        prevbb = random_crop(sample,\n",
    "                             lambda_scale_frac,\n",
    "                             lambda_shift_frac,\n",
    "                             min_scale,\n",
    "                             max_scale)\n",
    "\n",
    "        # Crop previous image with height and width twice the prev bounding box height and width\n",
    "        # Scale the cropped image to (227,227,3)\n",
    "        crop_curr = transforms.Compose([CropCurr()])\n",
    "        scale = Rescale((227,227))\n",
    "        transform_prev = transforms.Compose([CropPrev(), scale])\n",
    "        prev_img = transform_prev({'image':origimg, 'bb':origbb})['image']\n",
    "        # Crop current image with height and width twice the prev bounding box height and width\n",
    "        # Scale the cropped image to (227,227,3)\n",
    "        curr_obj = crop_curr({'image':origimg, 'prevbb':prevbb, 'currbb':origbb})\n",
    "        curr_obj = scale(curr_obj)\n",
    "        curr_img = curr_obj['image']\n",
    "        currbb = curr_obj['bb']\n",
    "        currbb = np.array(currbb)\n",
    "        sample = {'previmg': prev_img,\n",
    "                'currimg': curr_img,\n",
    "                'currbb' : currbb\n",
    "                }\n",
    "        sample = transform(sample)\n",
    "        x1_batch[i+1,:,:,:] = sample['previmg']\n",
    "        x2_batch[i+1,:,:,:] = sample['currimg']\n",
    "        y_batch[i+1,:] = sample['currbb']\n",
    "\n",
    "    return x1_batch, x2_batch, y_batch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize a batch (11 samples) from ALOV dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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
Download .txt
gitextract_y95ebmd8/

├── .gitignore
├── LICENSE
├── README.md
├── data/
│   ├── OTB/
│   │   └── Man/
│   │       ├── cfg.mat
│   │       └── groundtruth_rect.txt
│   ├── download.sh
│   ├── setup.sh
│   └── untar.sh
├── notebooks/
│   └── VisualizeBatches.ipynb
├── requirements.txt
└── src/
    ├── boundingbox.py
    ├── datasets.py
    ├── demo.py
    ├── evaluate.py
    ├── goturn.py
    ├── helper.py
    ├── model.py
    ├── test.py
    └── train.py
Download .txt
SYMBOL INDEX (73 symbols across 8 files)

FILE: src/boundingbox.py
  function sample_rand_uniform (line 6) | def sample_rand_uniform():
  function sample_exp_two_sides (line 11) | def sample_exp_two_sides(lambda_):
  class BoundingBox (line 23) | class BoundingBox:
    method __init__ (line 25) | def __init__(self, x1, y1, x2, y2):
    method print_bb (line 34) | def print_bb(self):
    method get_bb_list (line 41) | def get_bb_list(self):
    method get_center_x (line 44) | def get_center_x(self):
    method get_center_y (line 47) | def get_center_y(self):
    method compute_output_height (line 50) | def compute_output_height(self):
    method compute_output_width (line 56) | def compute_output_width(self):
    method edge_spacing_x (line 62) | def edge_spacing_x(self):
    method edge_spacing_y (line 68) | def edge_spacing_y(self):
    method unscale (line 74) | def unscale(self, image):
    method uncenter (line 88) | def uncenter(self, raw_image, search_location, edge_spacing_x, edge_sp...
    method recenter (line 94) | def recenter(self, search_loc, edge_spacing_x, edge_spacing_y, bbox_gt...
    method scale (line 102) | def scale(self, image):
    method get_width (line 116) | def get_width(self):
    method get_height (line 119) | def get_height(self):
    method shift (line 122) | def shift(self, image, lambda_scale_frac, lambda_shift_frac, min_scale...

FILE: src/datasets.py
  class ALOVDataset (line 16) | class ALOVDataset(Dataset):
    method __init__ (line 19) | def __init__(self, root_dir, target_dir, transform=None, input_size=227):
    method __len__ (line 35) | def __len__(self):
    method __getitem__ (line 38) | def __getitem__(self, idx):
    method _parse_data (line 44) | def _parse_data(self, root_dir, target_dir):
    method get_sample (line 82) | def get_sample(self, idx):
    method get_orig_sample (line 131) | def get_orig_sample(self, idx, i=1):
    method get_bb (line 142) | def get_bb(self, ann):
    method show (line 155) | def show(self, idx, is_current=1):
    method show_sample (line 174) | def show_sample(self, idx):
  class ILSVRC2014_DET_Dataset (line 196) | class ILSVRC2014_DET_Dataset(Dataset):
    method __init__ (line 199) | def __init__(self, image_dir,
    method __getitem__ (line 211) | def __getitem__(self, idx):
    method __len__ (line 217) | def __len__(self):
    method get_bb (line 220) | def get_bb(self, bbox_filepath):
    method get_sample (line 235) | def get_sample(self, idx):
    method get_orig_sample (line 258) | def get_orig_sample(self, idx):
    method filter_ann (line 269) | def filter_ann(self, sz, ann):
    method _parse_data (line 286) | def _parse_data(self, image_dir, bbox_dir):
    method display_object (line 317) | def display_object(self, idx):
    method show_sample (line 332) | def show_sample(self, idx):

FILE: src/demo.py
  function axis_aligned_iou (line 21) | def axis_aligned_iou(boxA, boxB):
  function save (line 51) | def save(im, bb, gt_bb, idx):
  function main (line 65) | def main(args):

FILE: src/goturn.py
  class TrackerGOTURN (line 11) | class TrackerGOTURN(Tracker):
    method __init__ (line 26) | def __init__(self, net_path=None, **kargs):
    method init (line 50) | def init(self, image, box):
    method update (line 64) | def update(self, image):
    method _get_rect (line 96) | def _get_rect(self, sample):

FILE: src/helper.py
  class Rescale (line 13) | class Rescale(object):
    method __init__ (line 19) | def __init__(self, output_size):
    method __call__ (line 23) | def __call__(self, sample, opts):
  class NormalizeToTensor (line 42) | class NormalizeToTensor(object):
    method __call__ (line 45) | def __call__(self, sample):
  function bgr2rgb (line 64) | def bgr2rgb(image):
  function shift_crop_training_sample (line 71) | def shift_crop_training_sample(sample, bb_params):
  function crop_sample (line 110) | def crop_sample(sample):
  function cropPadImage (line 137) | def cropPadImage(bbox_tight, image):
  function computeCropPadImageLocation (line 176) | def computeCropPadImageLocation(bbox_tight, image):

FILE: src/model.py
  class GoNet (line 6) | class GoNet(nn.Module):
    method __init__ (line 25) | def __init__(self):
    method weight_init (line 45) | def weight_init(self):
    method forward (line 55) | def forward(self, x, y):

FILE: src/test.py
  class GOTURN (line 23) | class GOTURN:
    method __init__ (line 25) | def __init__(self, root_dir, model_path, device):
    method __getitem__ (line 72) | def __getitem__(self, idx):
    method _get_sample (line 79) | def _get_sample(self, idx):
    method get_rect (line 96) | def get_rect(self, sample):
    method test (line 115) | def test(self):

FILE: src/train.py
  function main (line 69) | def main():
  function get_training_batch (line 128) | def get_training_batch(num_running_batch, running_batch, dataset):
  function make_transformed_samples (line 157) | def make_transformed_samples(dataset, args):
  function train_model (line 201) | def train_model(model, datasets, criterion, optimizer):
  function save_checkpoint (line 318) | def save_checkpoint(state, filename='checkpoint.pth.tar'):
Condensed preview — 19 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (2,500K chars).
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  {
    "path": ".gitignore",
    "chars": 6,
    "preview": "*.pyc\n"
  },
  {
    "path": "LICENSE",
    "chars": 1072,
    "preview": "MIT License\n\nCopyright (c) 2018 Abhinav Moudgil\n\nPermission is hereby granted, free of charge, to any person obtaining a"
  },
  {
    "path": "README.md",
    "chars": 6718,
    "preview": "# PyTorch GOTURN tracker\n\nThis is the PyTorch implementation of GOTURN visual tracker (Held. et. al, ECCV 2016). GOTURN "
  },
  {
    "path": "data/OTB/Man/groundtruth_rect.txt",
    "chars": 1684,
    "preview": "69,48,26,39\n70,50,26,39\n71,50,26,39\n71,50,26,39\n71,50,26,39\n71,50,26,39\n71,50,26,39\n71,50,26,39\n71,50,26,39\n71,50,26,39\n"
  },
  {
    "path": "data/download.sh",
    "chars": 431,
    "preview": "#!/bin/bash\necho 'Downloading ImageNet training images...'\nwget http://image-net.org/image/ilsvrc2014/ILSVRC2014_DET_tra"
  },
  {
    "path": "data/setup.sh",
    "chars": 439,
    "preview": "#!/bin/bash\n\n# setup imagenet images \ntar -xvf ILSVRC2014_DET_train.tgz\ncp ./untar.sh ./ILSVRC2014_DET_train/ && cd ./IL"
  },
  {
    "path": "data/untar.sh",
    "chars": 71,
    "preview": "while read -r line\ndo\n    tar -xvf $line\ndone < <(find . -name \"*.tar\")"
  },
  {
    "path": "notebooks/VisualizeBatches.ipynb",
    "chars": 2427697,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n "
  },
  {
    "path": "requirements.txt",
    "chars": 88,
    "preview": "numpy==1.14.5\ntorch==1.0.0\nopencv-python==4.0.0.21\ntorchvision==0.2.1\ntensorboardX==1.6\n"
  },
  {
    "path": "src/boundingbox.py",
    "chars": 7323,
    "preview": "# from helper import sample_exp_two_sides, sample_rand_uniform\nimport random\nimport math\n\n\ndef sample_rand_uniform():\n  "
  },
  {
    "path": "src/datasets.py",
    "chars": 13832,
    "preview": "from __future__ import print_function, division\nimport os\nimport xml.etree.ElementTree as ET\nimport warnings\n\nimport num"
  },
  {
    "path": "src/demo.py",
    "chars": 3255,
    "preview": "import os\nimport argparse\n\nimport torch\nimport cv2\n\nfrom test import GOTURN\n\nargs = None\nparser = argparse.ArgumentParse"
  },
  {
    "path": "src/evaluate.py",
    "chars": 695,
    "preview": "from __future__ import absolute_import\n\nfrom got10k.experiments import ExperimentOTB\n\nfrom goturn import TrackerGOTURN\n\n"
  },
  {
    "path": "src/goturn.py",
    "chars": 4322,
    "preview": "import torch\nimport numpy as np\nimport cv2\nfrom torchvision import transforms\nfrom got10k.trackers import Tracker\n\nfrom "
  },
  {
    "path": "src/helper.py",
    "chars": 8215,
    "preview": "import math\nimport warnings\n\nimport numpy as np\nimport torch\nimport cv2\nfrom torchvision import transforms\nfrom bounding"
  },
  {
    "path": "src/model.py",
    "chars": 2291,
    "preview": "import torch\nfrom torchvision import models\nimport torch.nn as nn\n\n\nclass GoNet(nn.Module):\n    \"\"\" Neural Network class"
  },
  {
    "path": "src/test.py",
    "chars": 4933,
    "preview": "import os\nimport time\nimport argparse\nimport re\n\nimport torch\nimport numpy as np\nimport cv2\n\nfrom model import GoNet\nfro"
  },
  {
    "path": "src/train.py",
    "chars": 13312,
    "preview": "# necessary imports\nimport os\nimport sys\nimport time\nimport argparse\n\nimport torch\nimport torch.optim as optim\nimport nu"
  }
]

// ... and 1 more files (download for full content)

About this extraction

This page contains the full source code of the amoudgl/pygoturn GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 19 files (2.4 MB), approximately 624.9k tokens, and a symbol index with 73 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.

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