UNLABELED/detect.py at master · QosmoInc/UNLABELED · GitHub

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import sys
import time
from PIL import Image, ImageDraw
from utils import *
from darknet import Darknet

def detect(cfgfile, weightfile, imgfile):
    m = Darknet(cfgfile)

    m.print_network()
    m.load_weights(weightfile)
    print('Loading weights from %s... Done!' % (weightfile))

    if m.num_classes == 20:
        namesfile = 'data/voc.names'
    elif m.num_classes == 80:
        namesfile = 'data/coco.names'
    else:
        namesfile = 'data/names'

    use_cuda = 1
    if use_cuda:
        m.cuda()

    img = Image.open(imgfile).convert('RGB')
    sized = img.resize((m.width, m.height))

    # Simen: niet nodig om dit in loop te doen?
    #for i in range(2):
    start = time.time()
    boxes = do_detect(m, sized, 0.5, 0.4, use_cuda)
    finish = time.time()

    #if i == 1:
    print('%s: Predicted in %f seconds.' % (imgfile, (finish-start)))

    class_names = load_class_names(namesfile)
    # plot_boxes(img, boxes, 'predictions.jpg', class_names)
    plot_boxes(img, boxes, '{}'.format(imgfile), class_names)

def detect_cv2(cfgfile, weightfile, imgfile):
    import cv2
    m = Darknet(cfgfile)

    m.print_network()
    m.load_weights(weightfile)
    print('Loading weights from %s... Done!' % (weightfile))

    if m.num_classes == 20:
        namesfile = 'data/voc.names'
    elif m.num_classes == 80:
        namesfile = 'data/coco.names'
    else:
        namesfile = 'data/names'

    use_cuda = 1
    if use_cuda:
        m.cuda()

    img = cv2.imread(imgfile)
    sized = cv2.resize(img, (m.width, m.height))
    sized = cv2.cvtColor(sized, cv2.COLOR_BGR2RGB)

    for i in range(2):
        start = time.time()
        boxes = do_detect(m, sized, 0.5, 0.4, use_cuda)
        finish = time.time()
        if i == 1:
            print('%s: Predicted in %f seconds.' % (imgfile, (finish-start)))

    class_names = load_class_names(namesfile)
    plot_boxes_cv2(img, boxes, savename='predictions.jpg', class_names=class_names)

def detect_skimage(cfgfile, weightfile, imgfile):
    from skimage import io
    from skimage.transform import resize
    m = Darknet(cfgfile)

    m.print_network()
    m.load_weights(weightfile)
    print('Loading weights from %s... Done!' % (weightfile))

    if m.num_classes == 20:
        namesfile = 'data/voc.names'
    elif m.num_classes == 80:
        namesfile = 'data/coco.names'
    else:
        namesfile = 'data/names'

    use_cuda = 1
    if use_cuda:
        m.cuda()

    img = io.imread(imgfile)
    sized = resize(img, (m.width, m.height)) * 255

    for i in range(2):
        start = time.time()
        boxes = do_detect(m, sized, 0.5, 0.4, use_cuda)
        finish = time.time()
        if i == 1:
            print('%s: Predicted in %f seconds.' % (imgfile, (finish-start)))

    class_names = load_class_names(namesfile)
    plot_boxes_cv2(img, boxes, savename='predictions.jpg', class_names=class_names)


if __name__ == '__main__':
    if len(sys.argv) == 4:
        cfgfile = sys.argv[1]
        weightfile = sys.argv[2]
        imgfile = sys.argv[3]
        detect(cfgfile, weightfile, imgfile)
        #detect_cv2(cfgfile, weightfile, imgfile)
        #detect_skimage(cfgfile, weightfile, imgfile)
    else:
        print('Usage: ')
        print('  python detect.py cfgfile weightfile imgfile')
        #detect('cfg/tiny-yolo-voc.cfg', 'tiny-yolo-voc.weights', 'data/person.jpg', version=1)