def main():
chainer.config.train = False
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--pretrained_model', default=TRAINED_MODEL_DEFAULT)
parser.add_argument('image')
args = parser.parse_args()
if args.pretrained_model == TRAINED_MODEL_DEFAULT and \
not os.path.exists(TRAINED_MODEL_DEFAULT):
download_model.download_model(MODEL_URL, TRAINED_MODEL_DEFAULT)
model = FasterRCNNVGG16(n_fg_class=1,
pretrained_model=args.pretrained_model)
if args.gpu >= 0:
model.to_gpu(args.gpu)
chainer.cuda.get_device(args.gpu).use()
img = utils.read_image(args.image, color=True)
bboxes, labels, scores = model.predict([img])
bbox, label, score = bboxes[0], labels[0], scores[0]
vis_bbox(img, bbox, label, score, label_names=('face', ))
plot.show()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model',
choices=('faster_rcnn_fpn_resnet50',
'faster_rcnn_fpn_resnet101'),
default='faster_rcnn_fpn_resnet50')
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--pretrained-model', default='coco')
parser.add_argument('image')
args = parser.parse_args()
if args.model == 'faster_rcnn_fpn_resnet50':
model = FasterRCNNFPNResNet50(n_fg_class=len(coco_bbox_label_names),
pretrained_model=args.pretrained_model)
elif args.model == 'faster_rcnn_fpn_resnet101':
model = FasterRCNNFPNResNet101(n_fg_class=len(coco_bbox_label_names),
pretrained_model=args.pretrained_model)
if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
model.to_gpu()
img = utils.read_image(args.image)
bboxes, labels, scores = model.predict([img])
bbox = bboxes[0]
label = labels[0]
score = scores[0]
vis_bbox(img, bbox, label, score, label_names=coco_bbox_label_names)
plt.show()
def main():
chainer.config.train = False
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--pretrained_model')
parser.add_argument('image')
parser.add_argument('--label_names',
help='The path to the file with label names')
args = parser.parse_args()
with open(args.label_names, 'r') as f:
label_names = f.read().splitlines()
model = SSD300(n_fg_class=len(label_names),
pretrained_model=args.pretrained_model)
if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
model.to_gpu()
img = utils.read_image(args.image, color=True)
model.nms_thresh = 0.45
model.score_thresh = 0.3
bboxes, labels, scores = model.predict([img])
print((bboxes, labels, scores))
bbox, label, score = bboxes[0], labels[0], scores[0]
vis_bbox(img, bbox, label, score, label_names=label_names)
plot.show()
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--model', choices=('ssd300', 'ssd512'), default='ssd300')
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--pretrained_model', default='voc0712')
parser.add_argument('image')
args = parser.parse_args()
if args.model == 'ssd300':
model = SSD300(
n_fg_class=len(voc_bbox_label_names),
pretrained_model=args.pretrained_model)
elif args.model == 'ssd512':
model = SSD512(
n_fg_class=len(voc_bbox_label_names),
pretrained_model=args.pretrained_model)
if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
model.to_gpu()
img = utils.read_image(args.image, color=True)
bboxes, labels, scores = model.predict([img])
bbox, label, score = bboxes[0], labels[0], scores[0]
vis_bbox(
img, bbox, label, score, label_names=voc_bbox_label_names)
plot.show()
def main():
dataset = VOCBboxDataset(year='2007', split='test')
models = [
('Faster R-CNN', FasterRCNNVGG16(pretrained_model='voc07')),
('SSD300', SSD300(pretrained_model='voc0712')),
('SSD512', SSD512(pretrained_model='voc0712')),
]
indices = [29, 301, 189, 229]
fig = plot.figure(figsize=(30, 30))
for i, idx in enumerate(indices):
for j, (name, model) in enumerate(models):
img, _, _ = dataset[idx]
bboxes, labels, scores = model.predict([img])
bbox, label, score = bboxes[0], labels[0], scores[0]
ax = fig.add_subplot(
len(indices), len(models), i * len(models) + j + 1)
vis_bbox(
img, bbox, label, score,
label_names=voc_bbox_label_names, ax=ax
)
# Set MatplotLib parameters
ax.set_aspect('equal')
if i == 0:
font = FontProperties()
font.set_family('serif')
ax.set_title(name, fontsize=35, y=1.03, fontproperties=font)
plot.axis('off')
plot.tight_layout()
plot.show()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--pretrained-model')
parser.add_argument('--dataset', choices=('voc', ), default='voc')
parser.add_argument('image')
args = parser.parse_args()
if args.dataset == 'voc':
if args.pretrained_model is None:
args.pretrained_model = 'voc07'
label_names = voc_bbox_label_names
model = FasterRCNNVGG16(n_fg_class=len(label_names),
pretrained_model=args.pretrained_model)
if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
model.to_gpu()
img = utils.read_image(args.image, color=True)
bboxes, labels, scores = model.predict([img])
bbox, label, score = bboxes[0], labels[0], scores[0]
vis_bbox(img, bbox, label, score, label_names=label_names)
plt.show()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model',
choices=('ssd300', 'ssd512'),
default='ssd300')
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--pretrained-model', default='voc0712')
parser.add_argument('image')
args = parser.parse_args()
if args.model == 'ssd300':
model = SSD300(n_fg_class=len(voc_bbox_label_names),
pretrained_model=args.pretrained_model)
elif args.model == 'ssd512':
model = SSD512(n_fg_class=len(voc_bbox_label_names),
pretrained_model=args.pretrained_model)
if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
model.to_gpu()
img = utils.read_image(args.image, color=True)
bboxes, labels, scores = model.predict([img])
bbox, label, score = bboxes[0], labels[0], scores[0]
vis_bbox(img, bbox, label, score, label_names=voc_bbox_label_names)
plt.show()
def save_bbox(img,
bbox,
filename=None,
label=None,
score=None,
label_names=None,
instance_colors=None,
alpha=1.0,
linewidth=3.0,
sort_by_score=True,
ax=None):
vis_bbox(img,
bbox,
label=label,
score=score,
label_names=label_names,
instance_colors=instance_colors,
alpha=alpha,
linewidth=linewidth,
sort_by_score=sort_by_score,
ax=ax)
plt.tick_params(labelbottom=False,
labelleft=False,
labelright=False,
labeltop=False)
plt.tick_params(bottom=False, left=False, right=False, top=False)
plt.savefig(filename, bbox_inches='tight', pad_inches=0)
plt.close()
def main():
dataset = VOCBboxDataset(year='2007', split='test')
models = [
('Faster R-CNN', FasterRCNNVGG16(pretrained_model='voc07')),
('SSD300', SSD300(pretrained_model='voc0712')),
('SSD512', SSD512(pretrained_model='voc0712')),
]
indices = [29, 301, 189, 229]
fig = plt.figure(figsize=(30, 30))
for i, idx in enumerate(indices):
for j, (name, model) in enumerate(models):
img, _, _ = dataset[idx]
bboxes, labels, scores = model.predict([img])
bbox, label, score = bboxes[0], labels[0], scores[0]
ax = fig.add_subplot(
len(indices), len(models), i * len(models) + j + 1)
vis_bbox(
img, bbox, label, score,
label_names=voc_bbox_label_names, ax=ax
)
# Set MatplotLib parameters
ax.set_aspect('equal')
if i == 0:
font = FontProperties()
font.set_family('serif')
ax.set_title(name, fontsize=35, y=1.03, fontproperties=font)
plt.axis('off')
plt.tight_layout()
plt.show()
def runYOLO():
'''parser = argparse.ArgumentParser()
parser.add_argument(
'--model', choices=('yolo_v2', 'yolo_v3'),
default='yolo_v2')
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--pretrained-model', default='voc0712')
parser.add_argument('image')
args = parser.parse_args()'''
aimage = 'sample7.jpg'
amodel = 'yolo_v2'
apretrained_model = 'voc0712'
agpu = -1
if amodel == 'yolo_v2':
model = YOLOv2(n_fg_class=len(voc_bbox_label_names),
pretrained_model=apretrained_model)
elif amodel == 'yolo_v3':
model = YOLOv3(n_fg_class=len(voc_bbox_label_names),
pretrained_model=apretrained_model)
if agpu >= 0:
chainer.cuda.get_device_from_id(agpu).use()
model.to_gpu()
img = utils.read_image(aimage, color=True)
bboxes, labels, scores = model.predict([img])
bbox, label, score = bboxes[0], labels[0], scores[0]
vis_bbox(img, bbox, label, score, label_names=voc_bbox_label_names)
def detectChocoballImage(self, img):
"""
ChocoBallの個数をカウントする
Args:
img : jpeg image (binary)
Returns:
dict{'img':image_binary, 'box':[[ymin,xmin,ymax,xmax]], 'objects':[object_id], 'scores':[score]}
"""
#img_pil = Image.open(BytesIO(img))
img_pil = Image.open(img)
print(img_pil.size)
if img_pil.size[0] > self.MAX_WIDTH:
img_pil.thumbnail(size=(self.MAX_WIDTH, self.MAX_WIDTH))
print(img_pil.size)
img_arr = np.asarray(img_pil).transpose(2, 0, 1).astype(np.float32)
bboxes, labels, scores = self.model_frcnn.predict([img_arr])
fig = plt.figure(figsize=(5, 4))
ax = fig.subplots(1, 1)
ax.tick_params(labelbottom="off", bottom="off")
ax.tick_params(labelleft="off", left="off")
vis_bbox(img_arr, bboxes[0], labels[0], ax=ax)
plt.savefig('tmp/tmp.png', bbox_inches="tight", pad_inches=0.0)
with open('tmp/tmp.png', 'rb') as f:
img_b = f.read()
return {'img': img_b, 'box': bboxes[0], 'objects': labels[0], 'scores': scores[0]}
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--base-network', choices=('vgg16', 'resnet101'),
default='vgg16', help='Base network')
parser.add_argument('--pretrained-model', required=True)
parser.add_argument('image')
args = parser.parse_args()
if args.base_network == 'vgg16':
model = chainercv.links.SSD300(
n_fg_class=len(roaddamage_label_names),
pretrained_model=args.pretrained_model)
elif args.base_network == 'resnet101':
model = ssd_resnet101.SSD224(
n_fg_class=len(roaddamage_label_names),
pretrained_model=args.pretrained_model)
else:
raise ValueError('Invalid base network')
if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
model.to_gpu()
img = utils.read_image(args.image, color=True)
bboxes, labels, scores = model.predict([img])
bbox, label, score = bboxes[0], labels[0], scores[0]
vis_bbox(
img, bbox, label, score, label_names=roaddamage_label_names)
plt.axis('off')
plt.show()
def image_cb(self, msg):
img = self.bridge.imgmsg_to_cv2(msg, desired_encoding='bgr8')
H, W = img.shape[:2]
objs = self.engine.DetectWithImage(PIL.Image.fromarray(img),
threshold=self.score_thresh,
keep_aspect_ratio=True,
relative_coord=True,
top_k=self.top_k)
bboxes = []
scores = []
labels = []
rect_msg = RectArray(header=msg.header)
for obj in objs:
x_min, y_min, x_max, y_max = obj.bounding_box.flatten().tolist()
x_min = int(np.round(x_min * W))
y_min = int(np.round(y_min * H))
x_max = int(np.round(x_max * W))
y_max = int(np.round(y_max * H))
bboxes.append([y_min, x_min, y_max, x_max])
scores.append(obj.score)
labels.append(self.label_ids.index(int(obj.label_id)))
rect = Rect(x=x_min,
y=y_min,
width=x_max - x_min,
height=y_max - y_min)
rect_msg.rects.append(rect)
bboxes = np.array(bboxes)
scores = np.array(scores)
labels = np.array(labels)
cls_msg = ClassificationResult(
header=msg.header,
classifier=self.classifier_name,
target_names=self.label_names,
labels=labels,
label_names=[self.label_names[l] for l in labels],
label_proba=scores)
self.pub_rects.publish(rect_msg)
self.pub_class.publish(cls_msg)
if self.visualize:
vis_img = img[:, :, ::-1].transpose(2, 0, 1)
vis_bbox(vis_img,
bboxes,
labels,
scores,
label_names=self.label_names)
fig = plt.gcf()
fig.canvas.draw()
w, h = fig.canvas.get_width_height()
vis_img = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8)
fig.clf()
vis_img.shape = (h, w, 3)
plt.close()
vis_msg = self.bridge.cv2_to_imgmsg(vis_img, 'rgb8')
vis_msg.header = msg.header
self.pub_image.publish(vis_msg)
def detect(image, model="ssd300", gpu=-1, pretrained_model="voc0712"):
detector = Detector(model, gpu, pretrained_model)
img = utils.read_image(image, color=True)
bbox, label, score = detector(image)
vis_bbox(img, bbox, label, score, label_names=voc_bbox_label_names)
plt.show()
def test_vis_bbox_invalid_inputs(self):
if not optional_modules:
return
with self.assertRaises(ValueError):
vis_bbox(
self.img, self.bbox, self.label, self.score,
label_names=self.label_names)
def test_vis_bbox_invalid_inputs(self):
with self.assertRaises(ValueError):
vis_bbox(self.img,
self.bbox,
self.label,
self.score,
label_names=self.label_names,
instance_colors=self.instance_colors,
sort_by_score=self.sort_by_score)
def save_result_images(self, image, save_as, bboxes, labels, scores):
if (self.model == 'fasterrcnnfpnresnet50') or (self.model == 'fasterrcnnfpnresnet101'):
bbox, label, score = bboxes[0], labels[0], scores[0]
vis_bbox(image, bbox, label, score, label_names=coco_bbox_label_names)
else:
vis_bbox(image, bboxes[0], labels[0], scores[0], label_names=voc_bbox_label_names)
plt.savefig(save_as)
def test_vis_bbox_invalid_inputs(self):
if not optional_modules:
return
with self.assertRaises(ValueError):
vis_bbox(self.img,
self.bbox,
self.label,
self.score,
label_names=self.label_names)
def __call__(self, img, bbox, label=None, score=None, save=False, path=None):
vis_bbox(
img, bbox, label, score, label_names=self.names
)
plt.xticks([])
plt.yticks([])
if save:
if path is None:
raise ValueError('Path should be set')
plt.savefig(path)
else:
plt.show()
def save_data(image, bbox, mask):
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from chainercv import visualizations
img = np.concatenate((image, image, mask * 255), axis=0)
visualizations.vis_bbox(img, bbox)
global N
plt.savefig('data/{}.png'.format(N))
N += 1
plt.close()
def runRCNN():
startTime = time.time()
#print(startTime)
# Read an RGB image and return it in CHW format.
img = read_image('sample7.jpg')
model = SSD300(pretrained_model='voc0712')
model1 = SSD512(pretrained_model='voc0712')
bboxes, labels, scores = model1.predict([img])
vis_bbox(img,
bboxes[0],
labels[0],
scores[0],
label_names=voc_bbox_label_names)
print(time.time() - startTime)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--engine',
required=False,
help='TensorRT engine file. If None, chainer-based '
'inference will run')
parser.add_argument('--n-try', '-n', type=int, default=1000)
parser.add_argument('--chainer', action='store_true')
parser.add_argument('image')
args = parser.parse_args()
model = YOLOv2(n_fg_class=len(voc_bbox_label_names),
pretrained_model='voc0712')
img = utils.read_image(args.image, color=True)
chainer.cuda.get_device_from_id(args.gpu).use()
if args.engine is not None:
# Key idea:
# `predict` method applies some pre-processings and preparations,
# runs forward-pass and some post-processings.
# We want to re-use all of them without copy-and-pasting the code,
# while only forward-pass runs with TensorRT.
# solution: replace __call__ and let `predict` call it,
# as if it is the original python based forward pass
def run_infer(self, x):
return tuple(chainer.Variable(t) for t in infer(x))
infer = chainer_trt.Infer(args.engine) # NOQA
type(model).__call__ = run_infer
print("Loaded TensorRT inference engine {}".format(args.engine))
else:
model.to_gpu()
print("Run Chainer based inference")
# Run inference once and get detections
# (1st execution tends to be slow in CUDA)
bboxes, labels, scores = model.predict([img])
before = time.time()
for _ in range(args.n_try):
model.predict([img])
after = time.time()
print('{:.3f}ms/img'.format(1000 * (after - before) / args.n_try))
bbox, label, score = bboxes[0], labels[0], scores[0]
vis_bbox(img, bbox, label, score, label_names=voc_bbox_label_names)
plt.show()
def publish_bbox_image(self, img, bbox, label, score):
vis_bbox(img, bbox, label, score, label_names=self.label_names)
fig = plt.gcf()
fig.canvas.draw()
w, h = fig.canvas.get_width_height()
img = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8)
fig.clf()
img.shape = (h, w, 3)
plt.close()
try:
msg = self.cv_bridge.cv2_to_imgmsg(img, "rgb8")
except Exception as e:
rospy.logerr("Failed to convert bbox image: %s" % str(e))
return
self.pub_image.publish(msg)
def main():
chainer.config.train = False
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--pretrained_model')
parser.add_argument('image')
parser.add_argument(
'--label_names', help='The path to the yaml file with label names')
args = parser.parse_args()
with open(args.label_names, 'r') as f:
label_names = tuple(yaml.load(f))
model = SSD300(
n_fg_class=len(label_names),
pretrained_model=args.pretrained_model)
# Change the threshold for showing labels
#model.score_thresh=0.55
if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
model.to_gpu()
img = utils.read_image(args.image, color=True)
bboxes, labels, scores = model.predict([img])
bbox, label, score = bboxes[0], labels[0], scores[0]
nb_containers = sum([1 for l in label if label_names[int(l)] == 'container'])
a = vis_bbox(
img, bbox, label, score, label_names=label_names)
a.annotate("Number of containers = {}".format(nb_containers), xytext=(0,0), xy=(0,0))
plot.show()
def annotate(args, path_in, path_out):
chainer.config.train = False
with open("model/label_names_coco_container.yml", 'r') as f:
label_names = tuple(yaml.load(f))
model = SSD300(
n_fg_class=len(label_names),
pretrained_model=args.model)
# Change the threshold for showing labels
# model.score_thresh=0.4
# model.nms_thres = args.threshold
count = 1
numfiles = len([f for f in os.listdir(path_in) if ".jpg" in f])
for file in [f for f in os.listdir(path_in) if ".jpg" in f]:
img = utils.read_image(path_in+file, color=True)
bboxes, labels, scores = model.predict([img])
bbox, label, score = bboxes[0], labels[0], scores[0]
nb_containers = sum([1 for l in label if label_names[int(l)] == 'container'])
a = vis_bbox(
img, bbox, label, score, label_names=label_names)
a.annotate("Number of containers = {}".format(nb_containers), xytext=(0, 0), xy=(0, 0))
plot.axis('off')
plot.savefig(path_out + "frame{}.png".format(str(count).zfill(5)), bbox_inches='tight')
plot.close()
if count % 10 == 0:
print("Progress: %0.2f%%" % (count / numfiles * 100,), flush=True)
count += 1
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--pretrained-model', default='sbd')
parser.add_argument('image')
args = parser.parse_args()
model = FCISPSROIAlignResNet101(n_fg_class=20,
pretrained_model=args.pretrained_model)
if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
model.to_gpu()
img = read_image(args.image, color=True)
masks, labels, scores = model.predict([img])
mask, label, score = masks[0], labels[0], scores[0]
bbox = mask_to_bbox(mask)
colors = voc_colormap(list(range(1, len(mask) + 1)))
ax = vis_bbox(img, bbox, instance_colors=colors, alpha=0.5, linewidth=1.5)
vis_instance_segmentation(
None,
mask,
label,
score,
label_names=sbd_instance_segmentation_label_names,
instance_colors=colors,
alpha=0.7,
ax=ax)
plt.show()
def publish_bbox_image(self, img, bbox, label, score):
vis_bbox(img, bbox, label, score,
label_names=self.label_names)
fig = plt.gcf()
fig.canvas.draw()
w, h = fig.canvas.get_width_height()
img = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8)
fig.clf()
img.shape = (h, w, 3)
plt.close()
try:
msg = self.cv_bridge.cv2_to_imgmsg(img, "rgb8")
except Exception as e:
rospy.logerr("Failed to convert bbox image: %s" % str(e))
return
self.pub_image.publish(msg)
def visualize_dataset(config):
from matplotlib import pyplot as plt
dataset = select_dataset(config, return_data=["train_set"])
hand_class = config.get('model_param', 'hand_class').split(",")
hand_class = [k.strip() for k in hand_class]
class_converter, flip_converter = create_converter(hand_class)
logger.info("hand_class = {}".format(hand_class))
logger.info("done get dataset")
idx = random.randint(0, len(dataset) - 1)
logger.info("get example")
rgb, rgb_bbox, rgb_class = dataset.get_example(idx)
logger.info("Done get example")
fig = plt.figure(figsize=(5, 10))
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
label = rgb_class
class_converter = {v: k for k, v in class_converter.items()}
color = [COLOR_MAP[class_converter[c]] for c in label]
print(label)
vis_bbox(
rgb,
rgb_bbox,
instance_colors=color,
label=label,
label_names=hand_class,
ax=ax1,
)
model = create_ssd_model()
transform_dataset = TransformDataset(
dataset, Transform(model.coder, model.insize, model.mean, train=True))
img, mb_loc, mb_label = transform_dataset.get_example(idx)
mb_color = [COLOR_MAP[class_converter[c]] for c in mb_label]
vis_bbox(
img,
mb_loc,
instance_colors=mb_color,
label=mb_label,
label_names=hand_class,
ax=ax2,
)
plt.savefig("vis.png")
plt.show()
def box_alignment(self, img, old_boxes, new_boxes, Spixels,
contours, new_superset, save=True, path=None
):
""" Visualize the change in the bboxes after box alignment
Args:
img : Input image
old_boxes: Old bboxes
new_boxes: Updated bboxes
Spixels : set of superpixels
"""
# fig = plt.figure(figsize=(16, 7))
fig = plt.figure()
ax1 = fig.add_subplot(221)
plt.xticks([])
plt.yticks([])
plt.title('Superpixels')
plt.imshow(contours)
ax1 = fig.add_subplot(222)
plt.xticks([])
plt.yticks([])
plt.title('Original box')
vis_bbox(img, old_boxes, ax=ax1, linewidth=1.0)
ax1 = fig.add_subplot(223)
plt.xticks([])
plt.yticks([])
plt.title('Superpixels added')
plt.imshow(new_superset[0])
ax2 = fig.add_subplot(224)
plt.xticks([])
plt.yticks([])
plt.title('After box-align')
vis_bbox(img, new_boxes, ax=ax2, linewidth=1.0)
for spixel in Spixels:
plt.imshow(spixel, cmap='gray', alpha=0.5, interpolation='none')
if save:
if path is None:
raise ValueError('Path should be set')
plt.savefig(path+'.png')
else:
plt.show()
plt.close(fig)
def test_vis_bbox(self):
if not optional_modules:
return
ax = vis_bbox(
self.img, self.bbox, self.label, self.score,
label_names=self.label_names)
self.assertIsInstance(ax, matplotlib.axes.Axes)
def save_images(image_path, model, out_root, label_names):
image_path_list = [x for x in glob.glob(os.path.join(image_path, '*.jpg'))]
image_path_list += [
x for x in glob.glob(os.path.join(image_path, '*.png'))
]
if not os.path.exists(out_root):
os.mkdir(out_root)
fig, ax = plt.subplots()
for _img in image_path_list:
img = chainercv.utils.read_image(_img, color=True)
with chainer.using_config('train', False):
bboxes, labels, scores = model.predict([img])
bbox, label, score = bboxes[0], labels[0], scores[0]
logger.info("{} {} {}".format(bbox, label, score))
vis_bbox(img, bbox, label, score, label_names=label_names, ax=ax)
f_name = os.path.join(out_root, os.path.basename(_img))
plt.savefig(f_name)
ax.clear()
def visualize_cb(self, event):
if (not self.visualize or self.img is None or self.header is None
or self.bboxes is None or self.labels is None
or self.scores is None):
return
with self.lock:
img = self.img.copy()
header = copy.deepcopy(self.header)
bboxes = self.bboxes.copy()
labels = self.labels.copy()
scores = self.scores.copy()
fig = plt.figure(tight_layout={'pad': 0})
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.axis('off')
fig.add_axes(ax)
vis_bbox(img.transpose((2, 0, 1)),
bboxes,
labels,
scores,
label_names=self.label_names,
ax=ax)
fig.canvas.draw()
w, h = fig.canvas.get_width_height()
vis_img = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8)
vis_img.shape = (h, w, 3)
fig.clf()
plt.close()
if self.pub_image.get_num_connections() > 0:
vis_msg = self.bridge.cv2_to_imgmsg(vis_img, 'rgb8')
# BUG: https://answers.ros.org/question/316362/sensor_msgsimage-generates-float-instead-of-int-with-python3/ # NOQA
vis_msg.step = int(vis_msg.step)
vis_msg.header = header
self.pub_image.publish(vis_msg)
if self.pub_image_compressed.get_num_connections() > 0:
# publish compressed http://wiki.ros.org/rospy_tutorials/Tutorials/WritingImagePublisherSubscriber # NOQA
vis_compressed_msg = CompressedImage()
vis_compressed_msg.header = header
vis_compressed_msg.format = "jpeg"
vis_img_rgb = cv2.cvtColor(vis_img, cv2.COLOR_BGR2RGB)
vis_compressed_msg.data = np.array(
cv2.imencode('.jpg', vis_img_rgb)[1]).tostring()
self.pub_image_compressed.publish(vis_compressed_msg)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model',
choices=('ssd300', 'ssd512'),
default='ssd300')
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--pretrained',
default='./models/model_iter_12000.npz')
args = parser.parse_args()
label_names = pose_bbox_label_names
BboxDataset = PoseBboxDataset
if args.model == 'ssd300':
model = SSD300(n_fg_class=len(label_names),
pretrained_model=args.pretrained)
elif args.model == 'ssd512':
model = SSD512(n_fg_class=len(label_names),
pretrained_model=args.pretrained)
if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
model.to_gpu()
for i in range(0, 10):
dataset = BboxDataset(split='test')
ori_img, ori_bbox, ori_label = dataset[i]
pred_bbox, pred_labels, pred_scores = model.predict([ori_img])
fig = plt.figure(figsize=(20, 10), dpi=80)
fig.suptitle("Original vs Prediction Annotations", fontsize=32)
ax1 = fig.add_subplot(1, 2, 1)
ax1.set_xlabel("Original", fontsize=24)
ax2 = fig.add_subplot(1, 2, 2)
ax2.set_xlabel("Prediction", fontsize=24)
vis_bbox(ori_img, ori_bbox, ori_label, label_names=label_names, ax=ax1)
vis_bbox(ori_img,
pred_bbox[0],
pred_labels[0],
pred_scores[0],
label_names=label_names,
ax=ax2)
plt.tight_layout()
plt.savefig('visualization/compare-{}.png'.format(i))
def test_vis_bbox(self):
ax = vis_bbox(self.img,
self.bbox,
self.label,
self.score,
label_names=self.label_names,
instance_colors=self.instance_colors,
sort_by_score=self.sort_by_score)
self.assertIsInstance(ax, matplotlib.axes.Axes)
def vis_pred_gt_bboxes(image, pred_bbox, gt_bbox, ax=None, show_iou=True):
"""Visualize both prediction and GT bboxes.
The colors of prediction and GT bboxes are red and green, respectively.
Args:
image (~np.ndarray): Greyscale image of shape (H, W).
pred_bbox (~np.ndarray): Prediction bbox of shape (R, 4).
gt_bbox (~np.ndarray): GT bbox of shape (R', 4).
ax (matplotlib.axes.Axis): The visualization is displayed on this
axis. If this is :obj:`None` (default), a new axis is created.
show_iou (bool): If `True`, show maximum IoU for each prediction bbox.
Returns:
~matploblib.axes.Axes:
Returns the Axes object with the plot for further tweaking.
"""
image = np.repeat([image], 3, axis=0) # (3, H, W)
if show_iou and len(pred_bbox) > 0 and len(gt_bbox) > 0:
iou = chainercv.utils.bbox_iou(pred_bbox, gt_bbox)
iou = iou.max(axis=1)
label_names = ['%.3f' % x for x in iou]
label = range(len(label_names))
else:
label_names, label = None, None
ax = vis_bbox(image,
pred_bbox,
label=label,
linewidth=1,
ax=ax,
label_names=label_names)
transp_image = np.zeros((4, *image.shape[1:]), dtype=np.float32)
ax = vis_bbox(transp_image,
gt_bbox,
instance_colors=((0, 255, 0), ),
ax=ax,
linewidth=1)
return ax
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--model', choices=('yolo_v2', 'yolo_v2_tiny', 'yolo_v3'),
default='yolo_v2')
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--pretrained-model', default='voc0712')
parser.add_argument('--export', action='store_true')
parser.add_argument('image')
args = parser.parse_args()
if args.model == 'yolo_v2':
model = YOLOv2(
n_fg_class=len(voc_bbox_label_names),
pretrained_model=args.pretrained_model)
elif args.model == 'yolo_v2_tiny':
model = YOLOv2Tiny(
n_fg_class=len(voc_bbox_label_names),
pretrained_model=args.pretrained_model)
elif args.model == 'yolo_v3':
model = YOLOv3(
n_fg_class=len(voc_bbox_label_names),
pretrained_model=args.pretrained_model)
if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
model.to_gpu()
img = utils.read_image(args.image, color=True)
if args.export:
import onnx_chainer
x = model.xp.stack([img])
onnx_chainer.export_testcase(model, x, args.model)
return
bboxes, labels, scores = model.predict([img])
bbox, label, score = bboxes[0], labels[0], scores[0]
vis_bbox(
img, bbox, label, score, label_names=voc_bbox_label_names)
plt.show()
if __name__ == '__main__':
from chainercv.visualizations import vis_bbox
from chainercv.visualizations import vis_semantic_segmentation
from chainercv.chainer_experimental.datasets.sliceable import TransformDataset
import matplotlib.pyplot as plt
import numpy as np
voc_segm = VOCSemanticSegmentationWithBboxDataset(
split='aug')
dataset = VOCSemanticSegmentationWithBboxDataset(
split='aug').slice[:, ['img', 'bbox', 'label']]
# transformed = TransformDataset(
# dataset, ('img', 'label_map'), grabcut_transform)
transformed = TransformDataset(
dataset, ('img', 'label_map'), SimpleDoesItTransform())
indices = np.random.choice(np.arange(len(voc_segm)), size=(10,))
for index in indices:
img, label_map, bbox, label = voc_segm[index]
vis_bbox(img, bbox, label)
plt.show()
# see doc for better visualization
vis_semantic_segmentation(img, label_map)
plt.show()
img, label_map = transformed[index]
vis_semantic_segmentation(img, label_map, alpha=0.6, ignore_label_color=(255, 0, 0))
plt.show()