Intro

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Real-time object detection and classification. Paper: version 1, version 2.

Read more about YOLO (in darknet) and download weight files here. In case the weight file cannot be found, I uploaded some of mine here, which include yolo-full and yolo-tiny of v1.0, tiny-yolo-v1.1 of v1.1 and yolo, tiny-yolo-voc of v2.

See demo below or see on this imgur

Dependencies

Python3, tensorflow 1.0, numpy, opencv 3.

Citation

Getting started

You can choose one of the following three ways to get started with darkflow.

1. Just build the Cython extensions in place. NOTE: If installing this way you will have to use ./flow in the cloned darkflow directory instead of flow as darkflow is not installed globally.

2. Let pip install darkflow globally in dev mode (still globally accessible, but changes to the code immediately take effect)

3. Install with pip globally

Update

Android demo on Tensorflow'shere

I am looking for help:

• help wanted labels in issue track

Parsing the annotations

Skip this if you are not training or fine-tuning anything (you simply want to forward flow a trained net)

For example, if you want to work with only 3 classes tvmonitor, person, pottedplant; edit labels.txt as follows

And that's it. darkflow will take care of the rest. You can also set darkflow to load from a custom labels file with the --labels flag (i.e. --labels myOtherLabelsFile.txt). This can be helpful when working with multiple models with different sets of output labels. When this flag is not set, darkflow will load from labels.txt by default (unless you are using one of the recognized .cfg files designed for the COCO or VOC dataset - then the labels file will be ignored and the COCO or VOC labels will be loaded).

Design the net

Skip this if you are working with one of the original configurations since they are already there. Otherwise, see the following example:

Flowing the graph using flow

First, let's take a closer look at one of a very useful option --load

All input images from default folder sample_img/ are flowed through the net and predictions are put in sample_img/out/. We can always specify more parameters for such forward passes, such as detection threshold, batch size, images folder, etc.

json output can be generated with descriptions of the pixel location of each bounding box and the pixel location. Each prediction is stored in the sample_img/out folder by default. An example json array is shown below.

JSON output:

• label: self explanatory
• confidence: somewhere between 0 and 1 (how confident yolo is about that detection)
• topleft: pixel coordinate of top left corner of box.
• bottomright: pixel coordinate of bottom right corner of box.

Training new model

Training is simple as you only have to add option --train. Training set and annotation will be parsed if this is the first time a new configuration is trained. To point to training set and annotations, use option --dataset and --annotation. A few examples:

During training, the script will occasionally save intermediate results into Tensorflow checkpoints, stored in ckpt/. To resume to any checkpoint before performing training/testing, use --load [checkpoint_num] option, if checkpoint_num < 0, darkflow will load the most recent save by parsing ckpt/checkpoint.

Example of training on Pascal VOC 2007:

Training on your own dataset

The steps below assume we want to use tiny YOLO and our dataset has 3 classes

1. Create a copy of the configuration file tiny-yolo-voc.cfg and rename it according to your preference tiny-yolo-voc-3c.cfg (It is crucial that you leave the original tiny-yolo-voc.cfg file unchanged, see below for explanation).

2. In tiny-yolo-voc-3c.cfg, change classes in the [region] layer (the last layer) to the number of classes you are going to train for. In our case, classes are set to 3.

3. In tiny-yolo-voc-3c.cfg, change filters in the [convolutional] layer (the second to last layer) to num * (classes + 5). In our case, num is 5 and classes are 3 so 5 * (3 + 5) = 40 therefore filters are set to 40.

4. Change labels.txt to include the label(s) you want to train on (number of labels should be the same as the number of classes you set in tiny-yolo-voc-3c.cfg file). In our case, labels.txt will contain 3 labels.

5. Reference the tiny-yolo-voc-3c.cfg model when you train.

   flow --model cfg/tiny-yolo-voc-3c.cfg --load bin/tiny-yolo-voc.weights --train --annotation train/Annotations --dataset train/Images

• Why should I leave the original tiny-yolo-voc.cfg file unchanged?

  When darkflow sees you are loading tiny-yolo-voc.weights it will look for tiny-yolo-voc.cfg in your cfg/ folder and compare that configuration file to the new one you have set with --model cfg/tiny-yolo-voc-3c.cfg. In this case, every layer will have the same exact number of weights except for the last two, so it will load the weights into all layers up to the last two because they now contain different number of weights.

Camera/video file demo

For a demo that entirely runs on the CPU:

For a demo that runs 100% on the GPU:

To use your webcam/camera, simply replace videofile.avi with keyword camera.

To save a video with predicted bounding box, add --saveVideo option.

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Using darkflow from another python application

Please note that return_predict(img) must take an numpy.ndarray. Your image must be loaded beforehand and passed to return_predict(img). Passing the file path won't work.

Result from return_predict(img) will be a list of dictionaries representing each detected object's values in the same format as the JSON output listed above.

Save the built graph to a protobuf file (.pb)

When saving the .pb file, a .meta file will also be generated alongside it. This .meta file is a JSON dump of everything in the meta dictionary that contains information nessecary for post-processing such as anchors and labels. This way, everything you need to make predictions from the graph and do post processing is contained in those two files - no need to have the .cfg or any labels file tagging along.

The created .pb file can be used to migrate the graph to mobile devices (JAVA / C++ / Objective-C++). The name of input tensor and output tensor are respectively 'input' and 'output'. For further usage of this protobuf file, please refer to the official documentation of Tensorflow on C++ API here. To run it on, say, iOS application, simply add the file to Bundle Resources and update the path to this file inside source code.

Also, darkflow supports loading from a .pb and .meta file for generating predictions (instead of loading from a .cfg and checkpoint or .weights).

If you'd like to load a .pb and .meta file when using return_predict() you can set the 'pbLoad' and 'metaLoad' options in place of the 'model' and 'load' options you would normally set.

That's all.

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