Requirements

Python 3.5, CUDA 9.0, cudnn 7.3.1 and other common packages listed in requirements.txt.

Installation

1. Clone this repository

2. Install dependencies

pip install -r requirements.txt

3. Run setup from the repository root directory

python setup.py build_ext --inplace

4. Download yolo.weights

download data from https://pjreddie.com/media/files/yolov2.weights

5. Make directory

Make /bin directory from root and put the weights file into the bin. Please rename yolov2.weights to yolo.weights.

Sample test

Run Processing_Images.py

import cv2
from darkflow.net.build import TFNet
import matplotlib.pyplot as plt

# define the model options and run

options = {
    'model': 'cfg/yolo.cfg',
    'load': 'bin/yolo.weights',
    'threshold': 0.3,
    'gpu': 1.0
}

tfnet = TFNet(options)

# read the color image and covert to RGB

img = cv2.imread('dog.png', cv2.IMREAD_COLOR)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# use YOLO to predict the image
result = tfnet.return_predict(img)

img.shape

# pull out some info from the results

tl = (result[0]['topleft']['x'], result[0]['topleft']['y'])
br = (result[0]['bottomright']['x'], result[0]['bottomright']['y'])
label = result[0]['label']


# add the box and label and display it
img = cv2.rectangle(img, tl, br, (0, 255, 0), 7)
img = cv2.putText(img, label, tl, cv2.FONT_HERSHEY_COMPLEX, 1, (0, 0, 0), 2)
plt.imshow(img)
plt.show()

Result