Python data.VOC_CLASSES Examples
The following are 30
code examples of data.VOC_CLASSES().
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Example #1
| Source File: test.py From CSD-SSD with MIT License | 6 votes |
|
def test_voc():
# load net
num_classes = len(VOC_CLASSES) + 1 # +1 background
net = build_ssd('test', 300, num_classes) # initialize SSD
net.load_state_dict(torch.load(args.trained_model))
net.eval()
print('Finished loading model!')
# load data
testset = VOCDetection(args.voc_root, [('2007', 'test')], None, VOCAnnotationTransform())
if args.cuda:
net = net.cuda()
cudnn.benchmark = True
# evaluation
test_net(args.save_folder, net, args.cuda, testset,
BaseTransform(net.size, (104, 117, 123)),
thresh=args.visual_threshold)
Example #2
| Source File: test.py From ssd.pytorch with MIT License | 6 votes |
|
def test_voc():
# load net
num_classes = len(VOC_CLASSES) + 1 # +1 background
net = build_ssd('test', 300, num_classes) # initialize SSD
net.load_state_dict(torch.load(args.trained_model))
net.eval()
print('Finished loading model!')
# load data
testset = VOCDetection(args.voc_root, [('2007', 'test')], None, VOCAnnotationTransform())
if args.cuda:
net = net.cuda()
cudnn.benchmark = True
# evaluation
test_net(args.save_folder, net, args.cuda, testset,
BaseTransform(net.size, (104, 117, 123)),
thresh=args.visual_threshold)
Example #3
| Source File: test.py From repulsion_loss_ssd with MIT License | 6 votes |
|
def test_voc():
# load net
num_classes = len(VOC_CLASSES) + 1 # +1 background
net = build_ssd('test', 300, num_classes) # initialize SSD
net.load_state_dict(torch.load(args.trained_model))
net.eval()
print('Finished loading model!')
# load data
testset = VOCDetection(args.voc_root, [('2007', 'test')], None, VOCAnnotationTransform())
if args.cuda:
net = net.cuda()
cudnn.benchmark = True
# evaluation
test_net(args.save_folder, net, args.cuda, testset,
BaseTransform(net.size, (104, 117, 123)),
thresh=args.visual_threshold)
Example #4
| Source File: test.py From SSD_resnet_pytorch with MIT License | 6 votes |
|
def test_voc():
# load net
num_classes = len(VOC_CLASSES) + 1 # +1 background
net = build_ssd('test',args.model, 300, num_classes) # initialize SSD
net.load_state_dict(torch.load(args.trained_model))
net.eval()
print('Finished loading model!')
# load data
testset = VOCDetection(args.voc_root, [('2007', 'test')], None, VOCAnnotationTransform())
if args.cuda:
net = net.cuda()
cudnn.benchmark = True
# evaluation
test_net(args.save_folder, net, args.cuda, testset,
BaseTransform(net.size, (104, 117, 123)),
thresh=args.visual_threshold)
Example #5
| Source File: eval512.py From CSD-SSD with MIT License | 5 votes |
|
def write_voc_results_file(all_boxes, dataset):
for cls_ind, cls in enumerate(labelmap):
print('Writing {:s} VOC results file'.format(cls))
filename = get_voc_results_file_template(set_type, cls)
with open(filename, 'wt') as f:
for im_ind, index in enumerate(dataset.ids):
dets = all_boxes[cls_ind+1][im_ind]
if dets == []:
continue
# the VOCdevkit expects 1-based indices
for k in range(dets.shape[0]):
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
format(index[1], dets[k, -1],
dets[k, 0] + 1, dets[k, 1] + 1,
dets[k, 2] + 1, dets[k, 3] + 1))
Example #6
| Source File: eval_multitrident.py From multitrident with Apache License 2.0 | 5 votes |
|
def do_python_eval(output_dir='output', use_07=True):
cachedir = os.path.join(devkit_path, 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
use_07_metric = use_07
print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(labelmap):
filename = get_voc_results_file_template(set_type, cls)
rec, prec, ap = voc_eval(
filename, annopath, imgsetpath.format(set_type), cls, cachedir,
ovthresh=0.5, use_07_metric=use_07_metric)
aps += [ap]
print('AP for {} = {:.4f}'.format(cls, ap))
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
print('Mean AP = {:.4f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('Results:')
for ap in aps:
print('{:.3f}'.format(ap))
print('{:.3f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('--------------------------------------------------------------')
Example #7
| Source File: eval_multitrident.py From multitrident with Apache License 2.0 | 5 votes |
|
def write_voc_results_file(all_boxes, dataset):
for cls_ind, cls in enumerate(labelmap):
print('Writing {:s} VOC results file'.format(cls))
filename = get_voc_results_file_template(set_type, cls)
with open(filename, 'wt') as f:
for im_ind, index in enumerate(dataset.ids):
dets = all_boxes[cls_ind+1][im_ind]
if dets == []:
continue
# the VOCdevkit expects 1-based indices
for k in range(dets.shape[0]):
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
format(index[1], dets[k, -1],
dets[k, 0] + 1, dets[k, 1] + 1,
dets[k, 2] + 1, dets[k, 3] + 1))
Example #8
| Source File: eval.py From ssd.pytorch with MIT License | 5 votes |
|
def do_python_eval(output_dir='output', use_07=True):
cachedir = os.path.join(devkit_path, 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
use_07_metric = use_07
print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(labelmap):
filename = get_voc_results_file_template(set_type, cls)
rec, prec, ap = voc_eval(
filename, annopath, imgsetpath.format(set_type), cls, cachedir,
ovthresh=0.5, use_07_metric=use_07_metric)
aps += [ap]
print('AP for {} = {:.4f}'.format(cls, ap))
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
print('Mean AP = {:.4f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('Results:')
for ap in aps:
print('{:.3f}'.format(ap))
print('{:.3f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('--------------------------------------------------------------')
Example #9
| Source File: eval.py From ssd.pytorch with MIT License | 5 votes |
|
def write_voc_results_file(all_boxes, dataset):
for cls_ind, cls in enumerate(labelmap):
print('Writing {:s} VOC results file'.format(cls))
filename = get_voc_results_file_template(set_type, cls)
with open(filename, 'wt') as f:
for im_ind, index in enumerate(dataset.ids):
dets = all_boxes[cls_ind+1][im_ind]
if dets == []:
continue
# the VOCdevkit expects 1-based indices
for k in range(dets.shape[0]):
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
format(index[1], dets[k, -1],
dets[k, 0] + 1, dets[k, 1] + 1,
dets[k, 2] + 1, dets[k, 3] + 1))
Example #10
| Source File: eval.py From repulsion_loss_ssd with MIT License | 5 votes |
|
def do_python_eval(output_dir='output', use_07=True):
cachedir = os.path.join(devkit_path, 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
use_07_metric = use_07
print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(labelmap):
filename = get_voc_results_file_template(set_type, cls)
rec, prec, ap = voc_eval(
filename, annopath, imgsetpath.format(set_type), cls, cachedir,
ovthresh=0.5, use_07_metric=use_07_metric)
aps += [ap]
print('AP for {} = {:.4f}'.format(cls, ap))
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
print('Mean AP = {:.4f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('Results:')
for ap in aps:
print('{:.3f}'.format(ap))
print('{:.3f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('--------------------------------------------------------------')
Example #11
| Source File: eval.py From repulsion_loss_ssd with MIT License | 5 votes |
|
def write_voc_results_file(all_boxes, dataset):
for cls_ind, cls in enumerate(labelmap):
print('Writing {:s} VOC results file'.format(cls))
filename = get_voc_results_file_template(set_type, cls)
with open(filename, 'wt') as f:
for im_ind, index in enumerate(dataset.ids):
dets = all_boxes[cls_ind+1][im_ind]
if dets == []:
continue
# the VOCdevkit expects 1-based indices
for k in range(dets.shape[0]):
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
format(index[1], dets[k, -1],
dets[k, 0] + 1, dets[k, 1] + 1,
dets[k, 2] + 1, dets[k, 3] + 1))
Example #12
| Source File: live.py From pytorch-ssd with MIT License | 5 votes |
|
def cv2_demo(net, transform):
def predict(frame):
height, width = frame.shape[:2]
x = torch.from_numpy(transform(frame)[0]).permute(2, 0, 1)
x = Variable(x.unsqueeze(0))
y = net(x) # forward pass
detections = y.data
# scale each detection back up to the image
scale = torch.Tensor([width, height, width, height])
for i in range(detections.size(1)):
j = 0
while detections[0, i, j, 0] >= 0.6:
pt = (detections[0, i, j, 1:] * scale).cpu().numpy()
cv2.rectangle(frame, (int(pt[0]), int(pt[1])), (int(pt[2]),
int(pt[3])), COLORS[i % 3], 2)
cv2.putText(frame, labelmap[i - 1], (int(pt[0]), int(pt[1])), FONT,
2, (255, 255, 255), 2, cv2.LINE_AA)
j += 1
return frame
# start video stream thread, allow buffer to fill
print("[INFO] starting threaded video stream...")
stream = WebcamVideoStream(src=0).start() # default camera
time.sleep(1.0)
# start fps timer
# loop over frames from the video file stream
while True:
# grab next frame
frame = stream.read()
key = cv2.waitKey(1) & 0xFF
# update FPS counter
fps.update()
frame = predict(frame)
# keybindings for display
if key == ord('p'): # pause
while True:
key2 = cv2.waitKey(1) or 0xff
cv2.imshow('frame', frame)
if key2 == ord('p'): # resume
break
cv2.imshow('frame', frame)
if key == 27: # exit
break
Example #13
| Source File: eval.py From SSD_resnet_pytorch with MIT License | 5 votes |
|
def do_python_eval(output_dir='output', use_07=True):
cachedir = os.path.join(devkit_path, 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
use_07_metric = use_07
print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(labelmap):
filename = get_voc_results_file_template(set_type, cls)
rec, prec, ap = voc_eval(
filename, annopath, imgsetpath.format(set_type), cls, cachedir,
ovthresh=0.5, use_07_metric=use_07_metric)
aps += [ap]
print('AP for {} = {:.4f}'.format(cls, ap))
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
print('Mean AP = {:.4f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('Results:')
for ap in aps:
print('{:.3f}'.format(ap))
print('{:.3f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('--------------------------------------------------------------')
Example #14
| Source File: eval.py From SSD_resnet_pytorch with MIT License | 5 votes |
|
def write_voc_results_file(all_boxes, dataset):
for cls_ind, cls in enumerate(labelmap):
print('Writing {:s} VOC results file'.format(cls))
filename = get_voc_results_file_template(set_type, cls)
with open(filename, 'wt') as f:
for im_ind, index in enumerate(dataset.ids):
dets = all_boxes[cls_ind+1][im_ind]
if dets == []:
continue
# the VOCdevkit expects 1-based indices
for k in range(dets.shape[0]):
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
format(index[1], dets[k, -1],
dets[k, 0] + 1, dets[k, 1] + 1,
dets[k, 2] + 1, dets[k, 3] + 1))
Example #15
| Source File: live.py From SSD_resnet_pytorch with MIT License | 5 votes |
|
def cv2_demo(net, transform):
def predict(frame):
height, width = frame.shape[:2]
x = torch.from_numpy(transform(frame)[0]).permute(2, 0, 1)
x = Variable(x.unsqueeze(0))
y = net(x) # forward pass
detections = y.data
# scale each detection back up to the image
scale = torch.Tensor([width, height, width, height])
for i in range(detections.size(1)):
j = 0
while detections[0, i, j, 0] >= 0.6:
pt = (detections[0, i, j, 1:] * scale).cpu().numpy()
cv2.rectangle(frame,
(int(pt[0]), int(pt[1])),
(int(pt[2]), int(pt[3])),
COLORS[i % 3], 2)
cv2.putText(frame, labelmap[i - 1], (int(pt[0]), int(pt[1])),
FONT, 2, (255, 255, 255), 2, cv2.LINE_AA)
j += 1
return frame
# start video stream thread, allow buffer to fill
print("[INFO] starting threaded video stream...")
stream = WebcamVideoStream(src=0).start() # default camera
time.sleep(1.0)
# start fps timer
# loop over frames from the video file stream
while True:
# grab next frame
frame = stream.read()
key = cv2.waitKey(1) & 0xFF
# update FPS counter
fps.update()
frame = predict(frame)
# keybindings for display
if key == ord('p'): # pause
while True:
key2 = cv2.waitKey(1) or 0xff
cv2.imshow('frame', frame)
if key2 == ord('p'): # resume
break
cv2.imshow('frame', frame)
if key == 27: # exit
break
Example #16
| Source File: eval_refinedet.py From RefineDet.PyTorch with MIT License | 5 votes |
|
def do_python_eval(output_dir='output', use_07=True):
cachedir = os.path.join(devkit_path, 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
use_07_metric = use_07
print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(labelmap):
filename = get_voc_results_file_template(set_type, cls)
rec, prec, ap = voc_eval(
filename, annopath, imgsetpath.format(set_type), cls, cachedir,
ovthresh=0.5, use_07_metric=use_07_metric)
aps += [ap]
print('AP for {} = {:.4f}'.format(cls, ap))
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
print('Mean AP = {:.4f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('Results:')
for ap in aps:
print('{:.3f}'.format(ap))
print('{:.3f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('--------------------------------------------------------------')
Example #17
| Source File: eval_refinedet.py From RefineDet.PyTorch with MIT License | 5 votes |
|
def write_voc_results_file(all_boxes, dataset):
for cls_ind, cls in enumerate(labelmap):
print('Writing {:s} VOC results file'.format(cls))
filename = get_voc_results_file_template(set_type, cls)
with open(filename, 'wt') as f:
for im_ind, index in enumerate(dataset.ids):
dets = all_boxes[cls_ind+1][im_ind]
if dets == []:
continue
# the VOCdevkit expects 1-based indices
for k in range(dets.shape[0]):
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
format(index[1], dets[k, -1],
dets[k, 0] + 1, dets[k, 1] + 1,
dets[k, 2] + 1, dets[k, 3] + 1))
Example #18
| Source File: eval512.py From CSD-SSD with MIT License | 5 votes |
|
def do_python_eval(output_dir='output', use_07=True):
cachedir = os.path.join(devkit_path, 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
use_07_metric = use_07
print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(labelmap):
filename = get_voc_results_file_template(set_type, cls)
rec, prec, ap = voc_eval(
filename, annopath, imgsetpath.format(set_type), cls, cachedir,
ovthresh=0.5, use_07_metric=use_07_metric)
aps += [ap]
print('AP for {} = {:.4f}'.format(cls, ap))
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
print('Mean AP = {:.4f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('Results:')
for ap in aps:
print('{:.3f}'.format(ap))
print('{:.3f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('--------------------------------------------------------------')
Example #19
| Source File: eval_refinedetlite_voc.py From RefinedetLite.pytorch with MIT License | 5 votes |
|
def do_python_eval(output_dir='output', use_07=True):
cachedir = os.path.join(devkit_path, 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
use_07_metric = use_07
print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(labelmap):
filename = get_voc_results_file_template(set_type, cls)
rec, prec, ap = voc_eval(
filename, annopath, imgsetpath.format(set_type), cls, cachedir,
ovthresh=0.5, use_07_metric=use_07_metric)
aps += [ap]
print('AP for {} = {:.4f}'.format(cls, ap))
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
print('Mean AP = {:.4f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('Results:')
for ap in aps:
print('{:.3f}'.format(ap))
print('{:.3f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('--------------------------------------------------------------')
Example #20
| Source File: eval_refinedetlite_voc.py From RefinedetLite.pytorch with MIT License | 5 votes |
|
def write_voc_results_file(all_boxes, dataset):
for cls_ind, cls in enumerate(labelmap):
print('Writing {:s} VOC results file'.format(cls))
filename = get_voc_results_file_template(set_type, cls)
with open(filename, 'wt') as f:
for im_ind, index in enumerate(dataset.ids):
dets = all_boxes[cls_ind+1][im_ind]
if dets == []:
continue
# the VOCdevkit expects 1-based indices
for k in range(dets.shape[0]):
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
format(index[1], dets[k, -1],
dets[k, 0] + 1, dets[k, 1] + 1,
dets[k, 2] + 1, dets[k, 3] + 1))
Example #21
| Source File: eval.py From CSD-SSD with MIT License | 5 votes |
|
def do_python_eval(output_dir='output', use_07=True):
cachedir = os.path.join(devkit_path, 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
use_07_metric = use_07
print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(labelmap):
filename = get_voc_results_file_template(set_type, cls)
rec, prec, ap = voc_eval(
filename, annopath, imgsetpath.format(set_type), cls, cachedir,
ovthresh=0.5, use_07_metric=use_07_metric)
aps += [ap]
print('AP for {} = {:.4f}'.format(cls, ap))
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
print('Mean AP = {:.4f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('Results:')
for ap in aps:
print('{:.3f}'.format(ap))
print('{:.3f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('--------------------------------------------------------------')
Example #22
| Source File: eval_multitrident.py From multitrident with Apache License 2.0 | 4 votes |
|
def test_net(save_folder, net, cuda, dataset, transform, top_k,
im_size=300, thresh=0.05):
num_images = len(dataset)
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [[[] for _ in range(num_images)]
for _ in range(len(labelmap)+1)]
# timers
_t = {'im_detect': Timer(), 'misc': Timer()}
output_dir = get_output_dir('gaijin6_175000', set_type)
det_file = os.path.join(output_dir, 'detections.pkl')
for i in range(num_images):
im, gt, h, w = dataset.pull_item(i)
x = Variable(im.unsqueeze(0))
if args.cuda:
x = x.cuda()
_t['im_detect'].tic()
detections = net(x).data
detect_time = _t['im_detect'].toc(average=False)
# skip j = 0, because it's the background class
for j in range(1, detections.size(1)):
dets = detections[0, j, :]
mask = dets[:, 0].gt(0.).expand(5, dets.size(0)).t()
dets = torch.masked_select(dets, mask).view(-1, 5)
if dets.size(0) == 0:
continue
boxes = dets[:, 1:]
boxes[:, 0] *= w
boxes[:, 2] *= w
boxes[:, 1] *= h
boxes[:, 3] *= h
scores = dets[:, 0].cpu().numpy()
"""do threshold for scores, delet scores < thresh"""
scores = scores[np.where(scores > thresh)]
boxes = boxes[np.where(scores > thresh)]
if boxes.size(0) == 0:
continue
cls_dets = np.hstack((boxes.cpu().numpy(),
scores[:, np.newaxis])).astype(np.float32,
copy=False)
all_boxes[j][i] = cls_dets
print('im_detect: {:d}/{:d} {:.3f}s'.format(i + 1,
num_images, detect_time))
with open(det_file, 'wb') as f:
pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)
print('Evaluating detections')
evaluate_detections(all_boxes, output_dir, dataset)
Example #23
| Source File: live.py From RefineDet.PyTorch with MIT License | 4 votes |
|
def cv2_demo(net, transform):
def predict(frame):
height, width = frame.shape[:2]
x = torch.from_numpy(transform(frame)[0]).permute(2, 0, 1)
x = Variable(x.unsqueeze(0))
y = net(x) # forward pass
detections = y.data
# scale each detection back up to the image
scale = torch.Tensor([width, height, width, height])
for i in range(detections.size(1)):
j = 0
while detections[0, i, j, 0] >= 0.6:
pt = (detections[0, i, j, 1:] * scale).cpu().numpy()
cv2.rectangle(frame,
(int(pt[0]), int(pt[1])),
(int(pt[2]), int(pt[3])),
COLORS[i % 3], 2)
cv2.putText(frame, labelmap[i - 1], (int(pt[0]), int(pt[1])),
FONT, 2, (255, 255, 255), 2, cv2.LINE_AA)
j += 1
return frame
# start video stream thread, allow buffer to fill
print("[INFO] starting threaded video stream...")
stream = WebcamVideoStream(src=0).start() # default camera
time.sleep(1.0)
# start fps timer
# loop over frames from the video file stream
while True:
# grab next frame
frame = stream.read()
key = cv2.waitKey(1) & 0xFF
# update FPS counter
fps.update()
frame = predict(frame)
# keybindings for display
if key == ord('p'): # pause
while True:
key2 = cv2.waitKey(1) or 0xff
cv2.imshow('frame', frame)
if key2 == ord('p'): # resume
break
cv2.imshow('frame', frame)
if key == 27: # exit
break
Example #24
| Source File: live.py From CSD-SSD with MIT License | 4 votes |
|
def cv2_demo(net, transform):
def predict(frame):
height, width = frame.shape[:2]
x = torch.from_numpy(transform(frame)[0]).permute(2, 0, 1)
x = Variable(x.unsqueeze(0))
y = net(x) # forward pass
detections = y.data
# scale each detection back up to the image
scale = torch.Tensor([width, height, width, height])
for i in range(detections.size(1)):
j = 0
while detections[0, i, j, 0] >= 0.6:
pt = (detections[0, i, j, 1:] * scale).cpu().numpy()
cv2.rectangle(frame,
(int(pt[0]), int(pt[1])),
(int(pt[2]), int(pt[3])),
COLORS[i % 3], 2)
cv2.putText(frame, labelmap[i - 1], (int(pt[0]), int(pt[1])),
FONT, 2, (255, 255, 255), 2, cv2.LINE_AA)
j += 1
return frame
# start video stream thread, allow buffer to fill
print("[INFO] starting threaded video stream...")
stream = WebcamVideoStream(src=0).start() # default camera
time.sleep(1.0)
# start fps timer
# loop over frames from the video file stream
while True:
# grab next frame
frame = stream.read()
key = cv2.waitKey(1) & 0xFF
# update FPS counter
fps.update()
frame = predict(frame)
# keybindings for display
if key == ord('p'): # pause
while True:
key2 = cv2.waitKey(1) or 0xff
cv2.imshow('frame', frame)
if key2 == ord('p'): # resume
break
cv2.imshow('frame', frame)
if key == 27: # exit
break
Example #25
| Source File: live.py From multitrident with Apache License 2.0 | 4 votes |
|
def cv2_demo(net, transform):
def predict(frame):
height, width = frame.shape[:2]
x = torch.from_numpy(transform(frame)[0]).permute(2, 0, 1)
x = Variable(x.unsqueeze(0))
y = net(x) # forward pass
detections = y.data
# scale each detection back up to the image
scale = torch.Tensor([width, height, width, height])
for i in range(detections.size(1)):
j = 0
while detections[0, i, j, 0] >= 0.6:
pt = (detections[0, i, j, 1:] * scale).cpu().numpy()
cv2.rectangle(frame,
(int(pt[0]), int(pt[1])),
(int(pt[2]), int(pt[3])),
COLORS[i % 3], 2)
cv2.putText(frame, labelmap[i - 1], (int(pt[0]), int(pt[1])),
FONT, 2, (255, 255, 255), 2, cv2.LINE_AA)
j += 1
return frame
# start video stream thread, allow buffer to fill
print("[INFO] starting threaded video stream...")
stream = WebcamVideoStream(src=0).start() # default camera
time.sleep(1.0)
# start fps timer
# loop over frames from the video file stream
while True:
# grab next frame
frame = stream.read()
key = cv2.waitKey(1) & 0xFF
# update FPS counter
fps.update()
frame = predict(frame)
# keybindings for display
if key == ord('p'): # pause
while True:
key2 = cv2.waitKey(1) or 0xff
cv2.imshow('frame', frame)
if key2 == ord('p'): # resume
break
cv2.imshow('frame', frame)
if key == 27: # exit
break
Example #26
| Source File: eval.py From ssd.pytorch with MIT License | 4 votes |
|
def test_net(save_folder, net, cuda, dataset, transform, top_k,
im_size=300, thresh=0.05):
num_images = len(dataset)
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [[[] for _ in range(num_images)]
for _ in range(len(labelmap)+1)]
# timers
_t = {'im_detect': Timer(), 'misc': Timer()}
output_dir = get_output_dir('ssd300_120000', set_type)
det_file = os.path.join(output_dir, 'detections.pkl')
for i in range(num_images):
im, gt, h, w = dataset.pull_item(i)
x = Variable(im.unsqueeze(0))
if args.cuda:
x = x.cuda()
_t['im_detect'].tic()
detections = net(x).data
detect_time = _t['im_detect'].toc(average=False)
# skip j = 0, because it's the background class
for j in range(1, detections.size(1)):
dets = detections[0, j, :]
mask = dets[:, 0].gt(0.).expand(5, dets.size(0)).t()
dets = torch.masked_select(dets, mask).view(-1, 5)
if dets.size(0) == 0:
continue
boxes = dets[:, 1:]
boxes[:, 0] *= w
boxes[:, 2] *= w
boxes[:, 1] *= h
boxes[:, 3] *= h
scores = dets[:, 0].cpu().numpy()
cls_dets = np.hstack((boxes.cpu().numpy(),
scores[:, np.newaxis])).astype(np.float32,
copy=False)
all_boxes[j][i] = cls_dets
print('im_detect: {:d}/{:d} {:.3f}s'.format(i + 1,
num_images, detect_time))
with open(det_file, 'wb') as f:
pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)
print('Evaluating detections')
evaluate_detections(all_boxes, output_dir, dataset)
Example #27
| Source File: eval512.py From CSD-SSD with MIT License | 4 votes |
|
def test_net(save_folder, net, cuda, dataset, transform, top_k,
im_size=512, thresh=0.05):
num_images = len(dataset)
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [[[] for _ in range(num_images)]
for _ in range(len(labelmap)+1)]
# timers
_t = {'im_detect': Timer(), 'misc': Timer()}
output_dir = get_output_dir('ssd300_120000', set_type)
det_file = os.path.join(output_dir, 'detections.pkl')
for i in range(num_images):
im, gt, h, w = dataset.pull_item(i)
x = Variable(im.unsqueeze(0))
if args.cuda:
x = x.cuda()
_t['im_detect'].tic()
detections = net(x).data
detect_time = _t['im_detect'].toc(average=False)
# skip j = 0, because it's the background class
for j in range(1, detections.size(1)):
dets = detections[0, j, :]
mask = dets[:, 0].gt(0.).expand(5, dets.size(0)).t()
dets = torch.masked_select(dets, mask).view(-1, 5)
if dets.dim() == 0:
continue
boxes = dets[:, 1:]
boxes[:, 0] *= w
boxes[:, 2] *= w
boxes[:, 1] *= h
boxes[:, 3] *= h
scores = dets[:, 0].cpu().numpy()
cls_dets = np.hstack((boxes.cpu().numpy(),
scores[:, np.newaxis])).astype(np.float32,
copy=False)
all_boxes[j][i] = cls_dets
print('im_detect: {:d}/{:d} {:.3f}s'.format(i + 1,
num_images, detect_time))
with open(det_file, 'wb') as f:
pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)
print('Evaluating detections')
evaluate_detections(all_boxes, output_dir, dataset)
Example #28
| Source File: eval.py From CSD-SSD with MIT License | 4 votes |
|
def test_net(save_folder, net, cuda, dataset, transform, top_k,
im_size=300, thresh=0.05):
num_images = len(dataset)
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [[[] for _ in range(num_images)]
for _ in range(len(labelmap)+1)]
# timers
_t = {'im_detect': Timer(), 'misc': Timer()}
output_dir = get_output_dir('ssd300_120000', set_type)
det_file = os.path.join(output_dir, 'detections.pkl')
for i in range(num_images):
im, gt, h, w = dataset.pull_item(i)
x = Variable(im.unsqueeze(0))
if args.cuda:
x = x.cuda()
_t['im_detect'].tic()
detections = net(x).data
detect_time = _t['im_detect'].toc(average=False)
# skip j = 0, because it's the background class
for j in range(1, detections.size(1)):
dets = detections[0, j, :]
mask = dets[:, 0].gt(0.).expand(5, dets.size(0)).t()
dets = torch.masked_select(dets, mask).view(-1, 5)
if dets.dim() == 0:
continue
boxes = dets[:, 1:]
boxes[:, 0] *= w
boxes[:, 2] *= w
boxes[:, 1] *= h
boxes[:, 3] *= h
scores = dets[:, 0].cpu().numpy()
cls_dets = np.hstack((boxes.cpu().numpy(),
scores[:, np.newaxis])).astype(np.float32,
copy=False)
all_boxes[j][i] = cls_dets
print('im_detect: {:d}/{:d} {:.3f}s'.format(i + 1,
num_images, detect_time))
with open(det_file, 'wb') as f:
pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)
print('Evaluating detections')
evaluate_detections(all_boxes, output_dir, dataset)
Example #29
| Source File: live.py From ssd.pytorch with MIT License | 4 votes |
|
def cv2_demo(net, transform):
def predict(frame):
height, width = frame.shape[:2]
x = torch.from_numpy(transform(frame)[0]).permute(2, 0, 1)
x = Variable(x.unsqueeze(0))
y = net(x) # forward pass
detections = y.data
# scale each detection back up to the image
scale = torch.Tensor([width, height, width, height])
for i in range(detections.size(1)):
j = 0
while detections[0, i, j, 0] >= 0.6:
pt = (detections[0, i, j, 1:] * scale).cpu().numpy()
cv2.rectangle(frame,
(int(pt[0]), int(pt[1])),
(int(pt[2]), int(pt[3])),
COLORS[i % 3], 2)
cv2.putText(frame, labelmap[i - 1], (int(pt[0]), int(pt[1])),
FONT, 2, (255, 255, 255), 2, cv2.LINE_AA)
j += 1
return frame
# start video stream thread, allow buffer to fill
print("[INFO] starting threaded video stream...")
stream = WebcamVideoStream(src=0).start() # default camera
time.sleep(1.0)
# start fps timer
# loop over frames from the video file stream
while True:
# grab next frame
frame = stream.read()
key = cv2.waitKey(1) & 0xFF
# update FPS counter
fps.update()
frame = predict(frame)
# keybindings for display
if key == ord('p'): # pause
while True:
key2 = cv2.waitKey(1) or 0xff
cv2.imshow('frame', frame)
if key2 == ord('p'): # resume
break
cv2.imshow('frame', frame)
if key == 27: # exit
break
Example #30
| Source File: eval.py From repulsion_loss_ssd with MIT License | 4 votes |
|
def test_net(save_folder, net, cuda, dataset, transform, top_k,
im_size=300, thresh=0.05):
num_images = len(dataset)
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [[[] for _ in range(num_images)]
for _ in range(len(labelmap)+1)]
# timers
_t = {'im_detect': Timer(), 'misc': Timer()}
output_dir = get_output_dir('ssd300_120000', set_type)
det_file = os.path.join(output_dir, 'detections.pkl')
for i in range(num_images):
im, gt, h, w = dataset.pull_item(i)
x = Variable(im.unsqueeze(0))
if args.cuda:
x = x.cuda()
_t['im_detect'].tic()
detections = net(x).data
detect_time = _t['im_detect'].toc(average=False)
# skip j = 0, because it's the background class
for j in range(1, detections.size(1)):
dets = detections[0, j, :]
mask = dets[:, 0].gt(0.).expand(5, dets.size(0)).t()
dets = torch.masked_select(dets, mask).view(-1, 5)
if dets.dim() == 0:
continue
boxes = dets[:, 1:]
boxes[:, 0] *= w
boxes[:, 2] *= w
boxes[:, 1] *= h
boxes[:, 3] *= h
scores = dets[:, 0].cpu().numpy()
cls_dets = np.hstack((boxes.cpu().numpy(),
scores[:, np.newaxis])).astype(np.float32,
copy=False)
all_boxes[j][i] = cls_dets
print('im_detect: {:d}/{:d} {:.3f}s'.format(i + 1,
num_images, detect_time))
with open(det_file, 'wb') as f:
pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)
print('Evaluating detections')
evaluate_detections(all_boxes, output_dir, dataset)
