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Add predict directory option #282

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Add predict directory option #282

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dd0da40
fix read issue
fajarnuha May 11, 2018
b86d072
Add direction in the README
fajarnuha May 11, 2018
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4 changes: 4 additions & 0 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -136,6 +136,10 @@ By the end of this process, the code will write the weights of the best model to

It carries out detection on the image and write the image with detected bounding boxes to the same folder.

or you can also detect the whole images in a directory by specifying the directory path (the path should end with "/")

`python predict.py -c config.json -w /path/to/best_weights.h5 -i /path/to/image/folder/`

## Usage for jupyter notebook

Refer to the notebook (https://github.com/experiencor/basic-yolo-keras/blob/master/Yolo%20Step-by-Step.ipynb) for a complete walk-through implementation of YOLOv2 from scratch (training, testing, and scoring).
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13 changes: 12 additions & 1 deletion predict.py
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Expand Up @@ -9,6 +9,7 @@
from utils import draw_boxes
from frontend import YOLO
import json
import glob

os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]="0"
Expand Down Expand Up @@ -81,7 +82,17 @@ def _main_(args):
video_writer.write(np.uint8(image))

video_reader.release()
video_writer.release()
video_writer.release()

elif image_path[-1] == '/':
for img in glob.glob(image_path+"*.jpg"):
image = cv2.imread(img)
boxes = yolo.predict(image)
image = draw_boxes(image, boxes, config['model']['labels'])

print(len(boxes), 'boxes are found')
cv2.imwrite(img[:-4] + '_detected' + img[-4:], image)

else:
image = cv2.imread(image_path)
boxes = yolo.predict(image)
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