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 = torch.cuda.is_available()
if use_cuda:
m.cuda()
img = Image.open(imgfile).convert('RGB')
sized = letterbox_image(img, m.width, m.height)
start = time.time()
boxes = do_detect(m, sized, 0.5, 0.1, use_cuda)
correct_yolo_boxes(boxes, img.width, img.height, m.width, m.height)
finish = time.time()
print('%s: Predicted in %f seconds.' % (imgfile, (finish - start)))
class_names = load_class_names(namesfile)
plot_boxes(img, boxes, 'predictions.jpg', class_names)
def detect(m, img, img_name, use_cuda):
sized = letterbox_image(img, m.width, m.height)
start = time.time()
boxes = do_detect(m, sized, 0.5, 0.4, use_cuda)
correct_yolo_boxes(boxes, img.width, img.height, m.width, m.height)
finish = time.time()
print('%s: Predicted in %f seconds.' % (img_name, (finish - start)))
return boxes
def darknet_GPU_predict(img, prob=0.5):
imgW, imgH = SIZE
imgResize, fx, fy, dx, dy = letterbox_image(img, SIZE)
im = array_to_image(imgResize)
out = predict_image(tableNet, im)
values = []
for i in range(2 * imgW * imgH):
values.append(out[i])
out = np.array(values).reshape((2, imgH, imgW))
#out = exp(out)
out = out[:, dy:, dx:]
return out, fx, fy
def dnn_table_predict(img, prob=0.5):
imgResize, fx, fy, dx, dy = letterbox_image(img, SIZE)
imgResize = np.array(imgResize)
imgW, imgH = SIZE
image = cv2.dnn.blobFromImage(imgResize,
1,
size=(imgW, imgH),
swapRB=False)
image = np.array(image) / 255
tableNet.setInput(image)
out = tableNet.forward()
out = exp(out[0])
out = out[:, dy:, dx:]
return out, fx, fy
def camera_detect(cfgfile, weightfile):
"""
- camera detect
:cfgfile use tiny config file
:weightfile use tiny weight file
"""
model = DarkNet(cfgfile)
model.print_net()
model.load_weights(weightfile)
print('load weights done!')
num_classes = 80
if num_classes == 20:
namesfile = '../data/voc.names'
elif num_classes == 80:
namesfile = '../data/coco.names'
else:
namesfile = '../data/names'
cap = cv2.VideoCapture(0)
if not cap.isOpened():
print("Unable to open camera")
exit(-1)
while True:
res, img = cap.read()
if res:
img = Image.fromarray(img, mode='RGB') # numpy.array -> PIL.Image
sized = letterbox_image(img, model.width, model.height)
boxes = do_detect(model, sized, 0.5, 0.4, False)
correct_yolo_boxes(boxes, img.width, img.height, model.width,
model.height)
class_names = load_class_names(namesfile)
image_draw = plot_boxes(img, boxes, None, class_names)
np_img = np.asarray(image_draw) # PIL.Image -> numpy.array
cv2.imshow(cfgfile, np_img)
cv2.waitKey(1)
else:
print("Unable to read image")
exit(-1)
from utils import *
from image import letterbox_image, correct_yolo_boxes
from darknet import Darknet
globals()["namesfile"] = "coco.names"
def detect(m, cfgfile, weightfile, imgfile, destfile):
m = Darknet(cfgfile)
m.print_network()
m.load_weights(weightfile)
print('Loading weights from %s... Done!' % (weightfile))
use_cuda = torch.cuda.is_available()
if use_cuda:
m.cuda()
img = Image.open(imgfile).convert('RGB')
sized = letterbox_image(img, m.width, m.height)
start = time.time()
boxes = do_detect(m, sized, 0.5, 0.4, use_cuda)
correct_yolo_boxes(boxes, img.width, img.height, m.width, m.height)
finish = time.time()
print('%s: Predicted in %f seconds.' % (imgfile, (finish-start)))
class_names = load_class_names(namesfile)
plot_boxes(img, boxes, destfile, class_names)
def valid(datacfg, cfgfile, weightfile, outfile):
cudnn.enabled = True
cudnn.benchmark = True
options = read_data_cfg(datacfg)
valid_images = options['valid']
name_list = options['names']
prefix = 'results'
names = load_class_names(name_list)
with open(valid_images) as fp:
tmp_files = fp.readlines()
valid_files = [item.rstrip() for item in tmp_files]
m = Darknet(cfgfile)
m.print_network()
m.load_weights(weightfile)
m.cuda()
m.eval()
print('shape:', m.width, 'x', m.height)
fps = []
if not os.path.exists('results'):
os.mkdir('results')
for i in range(m.num_classes):
buf = '%s/%s%s.txt' % (prefix, outfile, names[i])
fps.append(open(buf, 'w'))
conf_thresh = 0.005
nms_thresh = 0.45
for batch_idx, valid_file in enumerate(valid_files):
image = cv2.imread(valid_file)
assert image is not None
image2 = letterbox_image(image, m.width, m.height)
if batch_idx == 0:
cv2.imwrite('letterbox_image.jpg', image2.astype(np.uint8))
image_tensor = image_to_tensor(image2)
data = image_tensor.cuda()
with torch.no_grad():
output = m(data)
# if batch_idx == 0:
# outputs[-1] = data
# save_outputs('./outputs.npz', outputs)
batch_boxes = get_region_boxes2(output, image.shape[1], image.shape[0],
m.width, m.height, conf_thresh,
m.num_classes, m.anchors,
m.num_anchors, 1)
fileId = os.path.basename(valid_file).split('.')[0]
height, width = image.shape[:2]
print('[{}/{}]: '.format(batch_idx, len(valid_files)), valid_file, ' ',
len(batch_boxes[0]))
boxes = batch_boxes[0]
boxes = nms_class(boxes, nms_thresh, m.num_classes)
for box in boxes:
x1 = (box[0] - box[2] / 2.0) * width
y1 = (box[1] - box[3] / 2.0) * height
x2 = (box[0] + box[2] / 2.0) * width
y2 = (box[1] + box[3] / 2.0) * height
if x1 < 0:
x1 = 0
if y1 < 0:
y1 = 0
if x2 >= width:
x2 = width - 1
if y2 >= height:
y2 = height - 1
for j in range(m.num_classes):
prob = box[5 + j]
if prob >= conf_thresh:
fps[j].write('%s %f %f %f %f %f\n' %
(fileId, prob, x1, y1, x2, y2))
for i in range(m.num_classes):
fps[i].close()