About

Credit to digesting the yoloV3 CCP implementation into a pure python/TF2 implementation goes to https://github.com/zzh8829/yolov3-tf2

The purpose of this project is to build a low-latency image solver for recaptcha puzzles. Why recaptcha? They're the modern day turing test so why not. The idea to build this was inspired by https://github.com/mikeyy/nonoCAPTCHA that solved puzzles using Audio analysis cloud services. After forking and examining the code, I realized the image solver wasn't fully developed.

Currently the image solver does well but hasn't produced significant results to fully beat recaptcha puzzles in the wild.

The browser automation code is not open sourced. Components for that code was based off https://github.com/parejadan/nonoCAPTCHA but that was 6+ months ago. Base weights used to build iterative model are:

wget https://pjreddie.com/media/files/yolov3.weights -O data/yolov3.weights

How To

Creating TFRecords

# for creating tfrecords and visualizing record data
pip install .[training]

building tfrecord record

from bopflow.transform.records import PascalVocDecoder


# construct tfrecord
annotation_tree = self.load_annotation(s3_annotation)
(
    s3_filepath_key,
    width,
    height,
    tf_dict
) = PascalVocDecoder.extract_image_details(annotation_tree)
tf_dict.update(
    PascalVocDecoder.extract_image_objects(
        annotation_root=annotation_tree,
        img_width=width,
        img_height=height,
        label_lookup={self.label_name: class_id},
    )
)
# load image as byte stream so to add to tf record row (tf_dict)
image_bytes = self.lake.load_picture_bytes(s3_filepath_key)
tf_dict.update(
    PascalVocDecoder.get_image_feature(image_bytes)
)

# create train object for single image
tf_object = tf.train.Example(features=tf.train.Features(feature=tf_dict))

# create writer and produce tfrecord file
writter = tf.io.TFRecordWriter(f"{self.label_name}.tfrecord")
writter.write(tf_object.SerializeToString())
writter.close()

bin Commands

python bin/convert.py -input checkpoints/2020.04.10/weights.tf --output-format model

python bin/detect.py -image "test.jpg" --weights-path ./checkpoints/2020.04.10/weights.tf

# requires bopflow[training] installation
python bin/visualize_dataset.py -tfrecord crosswalks-test.tfrecord && open output.png

# for training model
python bin/train.py \
 -tfrecord-train crosswalks-train.tfrecord \
 -tfrecord-test crosswalks-test.tfrecord \
 --weights ./checkpoints/2020.04.10/yolov3.tf \
 --epochs 10 \
 --batch-size 8 \
 --new-model-class-count 81

python bin/detect.py -image "test.jpg" --weights-path ./checkpoints/2020.05.07/yolov3_train_10.tf

Format Code

docker run -v $(pwd):/code mercutiodesign/docker-black black .