Python matplotlib.pyplot.Rectangle() Examples
The following are 30 code examples for showing how to use matplotlib.pyplot.Rectangle(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
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Example 1
| Project: kvae Author: simonkamronn File: plotting.py License: MIT License | 6 votes |
|
def hinton(matrix, max_weight=None, ax=None):
"""Draw Hinton diagram for visualizing a weight matrix."""
ax = ax if ax is not None else plt.gca()
if not max_weight:
max_weight = 2 ** np.ceil(np.log(np.abs(matrix).max()) / np.log(2))
ax.patch.set_facecolor('gray')
ax.set_aspect('equal', 'box')
ax.xaxis.set_major_locator(plt.NullLocator())
ax.yaxis.set_major_locator(plt.NullLocator())
for (x, y), w in np.ndenumerate(matrix):
color = 'white' if w > 0 else 'black'
size = np.sqrt(np.abs(w) / max_weight)
rect = plt.Rectangle([x - size / 2, y - size / 2], size, size,
facecolor=color, edgecolor=color)
ax.add_patch(rect)
ax.autoscale_view()
ax.invert_yaxis()
Example 2
| Project: Deep-Feature-Flow-Segmentation Author: tonysy File: show_boxes.py License: MIT License | 6 votes |
|
def show_boxes(im, dets, classes, scale = 1.0):
plt.cla()
plt.axis("off")
plt.imshow(im)
for cls_idx, cls_name in enumerate(classes):
cls_dets = dets[cls_idx]
for det in cls_dets:
bbox = det[:4] * scale
color = (rand(), rand(), rand())
rect = plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor=color, linewidth=2.5)
plt.gca().add_patch(rect)
if cls_dets.shape[1] == 5:
score = det[-1]
plt.gca().text(bbox[0], bbox[1],
'{:s} {:.3f}'.format(cls_name, score),
bbox=dict(facecolor=color, alpha=0.5), fontsize=9, color='white')
plt.show()
return im
Example 3
| Project: TFFRCNN Author: CharlesShang File: minibatch.py License: MIT License | 6 votes |
|
def _vis_minibatch(im_blob, rois_blob, labels_blob, overlaps):
"""Visualize a mini-batch for debugging."""
import matplotlib.pyplot as plt
for i in xrange(rois_blob.shape[0]):
rois = rois_blob[i, :]
im_ind = rois[0]
roi = rois[1:]
im = im_blob[im_ind, :, :, :].transpose((1, 2, 0)).copy()
im += cfg.PIXEL_MEANS
im = im[:, :, (2, 1, 0)]
im = im.astype(np.uint8)
cls = labels_blob[i]
plt.imshow(im)
print 'class: ', cls, ' overlap: ', overlaps[i]
plt.gca().add_patch(
plt.Rectangle((roi[0], roi[1]), roi[2] - roi[0],
roi[3] - roi[1], fill=False,
edgecolor='r', linewidth=3)
)
plt.show()
Example 4
| Project: TFFRCNN Author: CharlesShang File: test.py License: MIT License | 6 votes |
|
def vis_detections(im, class_name, dets, thresh=0.8):
"""Visual debugging of detections."""
import matplotlib.pyplot as plt
#im = im[:, :, (2, 1, 0)]
for i in xrange(np.minimum(10, dets.shape[0])):
bbox = dets[i, :4]
score = dets[i, -1]
if score > thresh:
#plt.cla()
#plt.imshow(im)
plt.gca().add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor='g', linewidth=3)
)
plt.gca().text(bbox[0], bbox[1] - 2,
'{:s} {:.3f}'.format(class_name, score),
bbox=dict(facecolor='blue', alpha=0.5),
fontsize=14, color='white')
plt.title('{} {:.3f}'.format(class_name, score))
#plt.show()
Example 5
| Project: RetinaNet Author: xmyqsh File: minibatch.py License: MIT License | 6 votes |
|
def _vis_minibatch(im_blob, rois_blob, labels_blob, overlaps):
"""Visualize a mini-batch for debugging."""
import matplotlib.pyplot as plt
for i in xrange(rois_blob.shape[0]):
rois = rois_blob[i, :]
im_ind = rois[0]
roi = rois[1:]
im = im_blob[im_ind, :, :, :].transpose((1, 2, 0)).copy()
im += cfg.PIXEL_MEANS
im = im[:, :, (2, 1, 0)]
im = im.astype(np.uint8)
cls = labels_blob[i]
plt.imshow(im)
print 'class: ', cls, ' overlap: ', overlaps[i]
plt.gca().add_patch(
plt.Rectangle((roi[0], roi[1]), roi[2] - roi[0],
roi[3] - roi[1], fill=False,
edgecolor='r', linewidth=3)
)
plt.show()
Example 6
| Project: RetinaNet Author: xmyqsh File: test.py License: MIT License | 6 votes |
|
def vis_detections(im, class_name, dets, thresh=0.8):
"""Visual debugging of detections."""
import matplotlib.pyplot as plt
#im = im[:, :, (2, 1, 0)]
for i in xrange(np.minimum(10, dets.shape[0])):
bbox = dets[i, :4]
score = dets[i, -1]
if score > thresh:
#plt.cla()
#plt.imshow(im)
plt.gca().add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor='g', linewidth=3)
)
plt.gca().text(bbox[0], bbox[1] - 2,
'{:s} {:.3f}'.format(class_name, score),
bbox=dict(facecolor='blue', alpha=0.5),
fontsize=14, color='white')
plt.title('{} {:.3f}'.format(class_name, score))
#plt.show()
Example 7
| Project: RetinaNet Author: xmyqsh File: minibatch.py License: MIT License | 6 votes |
|
def _vis_minibatch(im_blob, rois_blob, labels_blob, sublabels_blob):
"""Visualize a mini-batch for debugging."""
import matplotlib.pyplot as plt
for i in xrange(rois_blob.shape[0]):
rois = rois_blob[i, :]
im_ind = rois[0]
roi = rois[2:]
im = im_blob[im_ind, :, :, :].transpose((1, 2, 0)).copy()
im += cfg.PIXEL_MEANS
im = im[:, :, (2, 1, 0)]
im = im.astype(np.uint8)
cls = labels_blob[i]
subcls = sublabels_blob[i]
plt.imshow(im)
print 'class: ', cls, ' subclass: ', subcls
plt.gca().add_patch(
plt.Rectangle((roi[0], roi[1]), roi[2] - roi[0],
roi[3] - roi[1], fill=False,
edgecolor='r', linewidth=3)
)
plt.show()
Example 8
| Project: pytorch-detect-to-track Author: Feynman27 File: roibatchLoader.py License: MIT License | 6 votes |
|
def _plot_image(self, data, gt_boxes, num_boxes):
import matplotlib.pyplot as plt
X=data.cpu().numpy().copy()
X += cfg.PIXEL_MEANS
X = X.astype(np.uint8)
X = X.squeeze(0)
boxes = gt_boxes.squeeze(0)[:num_boxes.view(-1)[0],:].cpu().numpy().copy()
fig, ax = plt.subplots(figsize=(8,8))
ax.imshow(X[:,:,::-1], aspect='equal')
for i in range(boxes.shape[0]):
bbox = boxes[i, :4]
ax.add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2]-bbox[0],
bbox[3]-bbox[1], fill=False, linewidth=2.0)
)
#plt.imshow(X[:,:,::-1])
plt.tight_layout()
plt.show()
Example 9
| Project: lightDSFD Author: lijiannuist File: widerface.py License: MIT License | 6 votes |
|
def vis_detections(self , im, dets, image_name ):
cv2.imwrite("./tmp_res/"+str(image_name)+"ori.jpg" , im)
print (im)
size = im.shape[0]
dets = dets*size
"""Draw detected bounding boxes."""
class_name = 'face'
#im = im[:, :, (2, 1, 0)]
fig, ax = plt.subplots(figsize=(12, 12))
ax.imshow(im, aspect='equal')
for i in range(len(dets)):
bbox = dets[i, :4]
ax.add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0] + 1,
bbox[3] - bbox[1] + 1, fill=False,
edgecolor='red', linewidth=2.5)
)
plt.axis('off')
plt.tight_layout()
plt.savefig('./tmp_res/'+str(image_name)+".jpg", dpi=fig.dpi)
Example 10
| Project: dpl Author: ppengtang File: test.py License: MIT License | 6 votes |
|
def vis_detections(im, class_name, dets, thresh=0.3):
"""Visual debugging of detections."""
import matplotlib.pyplot as plt
im = im[:, :, (2, 1, 0)]
for i in xrange(np.minimum(10, dets.shape[0])):
bbox = dets[i, :4]
score = dets[i, -1]
if score > thresh:
plt.cla()
plt.imshow(im)
plt.gca().add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor='g', linewidth=3)
)
plt.title('{} {:.3f}'.format(class_name, score))
plt.show()
Example 11
| Project: face-py-faster-rcnn Author: playerkk File: minibatch.py License: MIT License | 6 votes |
|
def _vis_minibatch(im_blob, rois_blob, labels_blob, overlaps):
"""Visualize a mini-batch for debugging."""
import matplotlib.pyplot as plt
for i in xrange(rois_blob.shape[0]):
rois = rois_blob[i, :]
im_ind = rois[0]
roi = rois[1:]
im = im_blob[im_ind, :, :, :].transpose((1, 2, 0)).copy()
im += cfg.PIXEL_MEANS
im = im[:, :, (2, 1, 0)]
im = im.astype(np.uint8)
cls = labels_blob[i]
plt.imshow(im)
print 'class: ', cls, ' overlap: ', overlaps[i]
plt.gca().add_patch(
plt.Rectangle((roi[0], roi[1]), roi[2] - roi[0],
roi[3] - roi[1], fill=False,
edgecolor='r', linewidth=3)
)
plt.show()
Example 12
| Project: face-py-faster-rcnn Author: playerkk File: test.py License: MIT License | 6 votes |
|
def vis_detections(im, class_name, dets, thresh=0.3):
"""Visual debugging of detections."""
import matplotlib.pyplot as plt
im = im[:, :, (2, 1, 0)]
for i in xrange(np.minimum(10, dets.shape[0])):
bbox = dets[i, :4]
score = dets[i, -1]
if score > thresh:
plt.cla()
plt.imshow(im)
plt.gca().add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor='g', linewidth=3)
)
plt.title('{} {:.3f}'.format(class_name, score))
plt.show()
Example 13
| Project: faster-rcnn-resnet Author: Eniac-Xie File: minibatch.py License: MIT License | 6 votes |
|
def _vis_minibatch(im_blob, rois_blob, labels_blob, overlaps):
"""Visualize a mini-batch for debugging."""
import matplotlib.pyplot as plt
for i in xrange(rois_blob.shape[0]):
rois = rois_blob[i, :]
im_ind = rois[0]
roi = rois[1:]
im = im_blob[im_ind, :, :, :].transpose((1, 2, 0)).copy()
im += cfg.PIXEL_MEANS
im = im[:, :, (2, 1, 0)]
im = im.astype(np.uint8)
cls = labels_blob[i]
plt.imshow(im)
print 'class: ', cls, ' overlap: ', overlaps[i]
plt.gca().add_patch(
plt.Rectangle((roi[0], roi[1]), roi[2] - roi[0],
roi[3] - roi[1], fill=False,
edgecolor='r', linewidth=3)
)
plt.show()
Example 14
| Project: faster-rcnn-resnet Author: Eniac-Xie File: test.py License: MIT License | 6 votes |
|
def vis_detections(im, class_name, dets, thresh=0.3):
"""Visual debugging of detections."""
import matplotlib.pyplot as plt
im = im[:, :, (2, 1, 0)]
for i in xrange(np.minimum(10, dets.shape[0])):
bbox = dets[i, :4]
score = dets[i, -1]
if score > thresh:
plt.cla()
plt.imshow(im)
plt.gca().add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor='g', linewidth=3)
)
plt.title('{} {:.3f}'.format(class_name, score))
plt.show()
Example 15
| Project: python3_ios Author: holzschu File: hinton_demo.py License: BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def hinton(matrix, max_weight=None, ax=None):
"""Draw Hinton diagram for visualizing a weight matrix."""
ax = ax if ax is not None else plt.gca()
if not max_weight:
max_weight = 2 ** np.ceil(np.log(np.abs(matrix).max()) / np.log(2))
ax.patch.set_facecolor('gray')
ax.set_aspect('equal', 'box')
ax.xaxis.set_major_locator(plt.NullLocator())
ax.yaxis.set_major_locator(plt.NullLocator())
for (x, y), w in np.ndenumerate(matrix):
color = 'white' if w > 0 else 'black'
size = np.sqrt(np.abs(w) / max_weight)
rect = plt.Rectangle([x - size / 2, y - size / 2], size, size,
facecolor=color, edgecolor=color)
ax.add_patch(rect)
ax.autoscale_view()
ax.invert_yaxis()
Example 16
| Project: DeepSim Author: shijx12 File: minibatch.py License: MIT License | 6 votes |
|
def _vis_minibatch(im_blob, rois_blob, labels_blob, overlaps):
"""Visualize a mini-batch for debugging."""
import matplotlib.pyplot as plt
for i in xrange(rois_blob.shape[0]):
rois = rois_blob[i, :]
im_ind = rois[0]
roi = rois[1:]
im = im_blob[im_ind, :, :, :].transpose((1, 2, 0)).copy()
im += cfg.PIXEL_MEANS
im = im[:, :, (2, 1, 0)]
im = im.astype(np.uint8)
cls = labels_blob[i]
plt.imshow(im)
print 'class: ', cls, ' overlap: ', overlaps[i]
plt.gca().add_patch(
plt.Rectangle((roi[0], roi[1]), roi[2] - roi[0],
roi[3] - roi[1], fill=False,
edgecolor='r', linewidth=3)
)
plt.show()
Example 17
| Project: DeepSim Author: shijx12 File: test.py License: MIT License | 6 votes |
|
def vis_detections(im, class_name, dets, thresh=0.8):
"""Visual debugging of detections."""
import matplotlib.pyplot as plt
#im = im[:, :, (2, 1, 0)]
for i in xrange(np.minimum(10, dets.shape[0])):
bbox = dets[i, :4]
score = dets[i, -1]
if score > thresh:
#plt.cla()
#plt.imshow(im)
plt.gca().add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor='g', linewidth=3)
)
plt.gca().text(bbox[0], bbox[1] - 2,
'{:s} {:.3f}'.format(class_name, score),
bbox=dict(facecolor='blue', alpha=0.5),
fontsize=14, color='white')
plt.title('{} {:.3f}'.format(class_name, score))
#plt.show()
Example 18
| Project: DeepSim Author: shijx12 File: minibatch.py License: MIT License | 6 votes |
|
def _vis_minibatch(im_blob, rois_blob, labels_blob, sublabels_blob):
"""Visualize a mini-batch for debugging."""
import matplotlib.pyplot as plt
for i in xrange(rois_blob.shape[0]):
rois = rois_blob[i, :]
im_ind = rois[0]
roi = rois[2:]
im = im_blob[im_ind, :, :, :].transpose((1, 2, 0)).copy()
im += cfg.PIXEL_MEANS
im = im[:, :, (2, 1, 0)]
im = im.astype(np.uint8)
cls = labels_blob[i]
subcls = sublabels_blob[i]
plt.imshow(im)
print 'class: ', cls, ' subclass: ', subcls
plt.gca().add_patch(
plt.Rectangle((roi[0], roi[1]), roi[2] - roi[0],
roi[3] - roi[1], fill=False,
edgecolor='r', linewidth=3)
)
plt.show()
Example 19
| Project: rgz_rcnn Author: chenwuperth File: minibatch.py License: MIT License | 6 votes |
|
def _vis_minibatch(im_blob, rois_blob, labels_blob, overlaps):
"""Visualize a mini-batch for debugging."""
import matplotlib.pyplot as plt
for i in xrange(rois_blob.shape[0]):
rois = rois_blob[i, :]
im_ind = rois[0]
roi = rois[1:]
im = im_blob[im_ind, :, :, :].transpose((1, 2, 0)).copy()
im += cfg.PIXEL_MEANS
im = im[:, :, (2, 1, 0)]
im = im.astype(np.uint8)
cls = labels_blob[i]
plt.imshow(im)
print 'class: ', cls, ' overlap: ', overlaps[i]
plt.gca().add_patch(
plt.Rectangle((roi[0], roi[1]), roi[2] - roi[0],
roi[3] - roi[1], fill=False,
edgecolor='r', linewidth=3)
)
plt.show()
Example 20
| Project: rgz_rcnn Author: chenwuperth File: test.py License: MIT License | 6 votes |
|
def vis_detections(im, class_name, dets, thresh=0.8):
"""Visual debugging of detections."""
import matplotlib.pyplot as plt
#im = im[:, :, (2, 1, 0)]
for i in xrange(np.minimum(10, dets.shape[0])):
bbox = dets[i, :4]
score = dets[i, -1]
if score > thresh:
#plt.cla()
#plt.imshow(im)
plt.gca().add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor='g', linewidth=3)
)
plt.gca().text(bbox[0], bbox[1] - 2,
'{:s} {:.3f}'.format(class_name, score),
bbox=dict(facecolor='blue', alpha=0.5),
fontsize=14, color='white')
plt.title('{} {:.3f}'.format(class_name, score))
#plt.show()
Example 21
| Project: color_recognizer Author: michtesar File: hinton.py License: MIT License | 6 votes |
|
def hinton(matrix, max_weight=None, ax=None):
"""Draw Hinton diagram for visualizing a weight matrix."""
ax = ax if ax is not None else plt.gca()
if not max_weight:
max_weight = 2 ** np.ceil(np.log(np.abs(matrix).max()) / np.log(2))
ax.patch.set_facecolor('gray')
ax.set_aspect('equal', 'box')
ax.xaxis.set_major_locator(plt.NullLocator())
ax.yaxis.set_major_locator(plt.NullLocator())
for (x, y), w in np.ndenumerate(matrix):
color = 'white' if w > 0 else 'black'
size = np.sqrt(np.abs(w) / max_weight)
rect = plt.Rectangle([x - size / 2, y - size / 2], size, size,
facecolor=color, edgecolor=color)
ax.add_patch(rect)
ax.autoscale_view()
ax.invert_yaxis()
return ax
Example 22
| Project: models Author: chainer File: vis_bbox_video.py License: MIT License | 6 votes |
|
def bbox_to_patch(bbox, patch=None):
import matplotlib.pyplot as plt
if bbox is None:
return patch
out_patch = []
for i, bb in enumerate(bbox):
xy = (bb[1], bb[0])
height = bb[2] - bb[0]
width = bb[3] - bb[1]
if patch is None:
out_patch.append(
plt.Rectangle(
xy, width, height, fill=False))
else:
patch[i].set_xy(xy)
patch[i].set_width(width)
patch[i].set_height(height)
out_patch.append(patch[i])
return out_patch
Example 23
| Project: kaggle-rsna18 Author: i-pan File: show_boxes.py License: MIT License | 6 votes |
|
def show_boxes(im, dets, classes, scale = 1.0):
plt.cla()
plt.axis("off")
plt.imshow(im)
for cls_idx, cls_name in enumerate(classes):
cls_dets = dets[cls_idx]
for det in cls_dets:
bbox = det[:4] * scale
color = (rand(), rand(), rand())
rect = plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor=color, linewidth=2.5)
plt.gca().add_patch(rect)
if cls_dets.shape[1] == 5:
score = det[-1]
plt.gca().text(bbox[0], bbox[1],
'{:s} {:.3f}'.format(cls_name, score),
bbox=dict(facecolor=color, alpha=0.5), fontsize=9, color='white')
plt.show()
return im
Example 24
| Project: PointNetGPD Author: lianghongzhuo File: contacts.py License: MIT License | 6 votes |
|
def plot_friction_cone(self, color='y', scale=1.0):
success, cone, in_normal = self.friction_cone()
ax = plt.gca(projection='3d')
self.graspable.sdf.scatter() # object
x, y, z = self.graspable.sdf.transform_pt_obj_to_grid(self.point)
nx, ny, nz = self.graspable.sdf.transform_pt_obj_to_grid(in_normal, direction=True)
ax.scatter([x], [y], [z], c=color, s=60) # contact
ax.scatter([x - nx], [y - ny], [z - nz], c=color, s=60) # normal
if success:
ax.scatter(x + scale * cone[0], y + scale * cone[1], z + scale * cone[2], c=color, s=40) # cone
ax.set_xlim3d(0, self.graspable.sdf.dims_[0])
ax.set_ylim3d(0, self.graspable.sdf.dims_[1])
ax.set_zlim3d(0, self.graspable.sdf.dims_[2])
return plt.Rectangle((0, 0), 1, 1, fc=color) # return a proxy for legend
Example 25
| Project: MANet_for_Video_Object_Detection Author: wangshy31 File: show_boxes.py License: Apache License 2.0 | 6 votes |
|
def show_boxes(im, dets, classes, scale = 1.0):
plt.cla()
plt.axis("off")
plt.imshow(im)
for cls_idx, cls_name in enumerate(classes):
cls_dets = dets[cls_idx]
for det in cls_dets:
bbox = det[:4] * scale
color = (random.random(), random.random(), random.random())
rect = plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor=color, linewidth=2.5)
plt.gca().add_patch(rect)
if cls_dets.shape[1] == 5:
score = det[-1]
plt.gca().text(bbox[0], bbox[1],
'{:s} {:.3f}'.format(cls_name, score),
bbox=dict(facecolor=color, alpha=0.5), fontsize=9, color='white')
plt.show()
return im
Example 26
| Project: Collaborative-Learning-for-Weakly-Supervised-Object-Detection Author: Sunarker File: demo.py License: MIT License | 5 votes |
|
def vis_detections(im, class_name, dets, thresh=0.5):
"""Draw detected bounding boxes."""
inds = np.where(dets[:, -1] >= thresh)[0]
if len(inds) == 0:
return
im = im[:, :, (2, 1, 0)]
fig, ax = plt.subplots(figsize=(12, 12))
ax.imshow(im, aspect='equal')
for i in inds:
bbox = dets[i, :4]
score = dets[i, -1]
ax.add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor='red', linewidth=3.5)
)
ax.text(bbox[0], bbox[1] - 2,
'{:s} {:.3f}'.format(class_name, score),
bbox=dict(facecolor='blue', alpha=0.5),
fontsize=14, color='white')
ax.set_title(('{} detections with '
'p({} | box) >= {:.1f}').format(class_name, class_name,
thresh),
fontsize=14)
plt.axis('off')
plt.tight_layout()
plt.draw()
Example 27
| Project: dynamic-training-with-apache-mxnet-on-aws Author: awslabs File: vis.py License: Apache License 2.0 | 5 votes |
|
def vis_detection(im_orig, detections, class_names, thresh=0.7):
"""visualize [cls, conf, x1, y1, x2, y2]"""
import matplotlib.pyplot as plt
import random
plt.imshow(im_orig)
colors = [(random.random(), random.random(), random.random()) for _ in class_names]
for [cls, conf, x1, y1, x2, y2] in detections:
cls = int(cls)
if cls > 0 and conf > thresh:
rect = plt.Rectangle((x1, y1), x2 - x1, y2 - y1,
fill=False, edgecolor=colors[cls], linewidth=3.5)
plt.gca().add_patch(rect)
plt.gca().text(x1, y1 - 2, '{:s} {:.3f}'.format(class_names[cls], conf),
bbox=dict(facecolor=colors[cls], alpha=0.5), fontsize=12, color='white')
plt.show()
Example 28
| Project: insightface Author: deepinsight File: tester.py License: MIT License | 5 votes |
|
def vis_all_detection(im_array, detections, class_names, scale):
"""
visualize all detections in one image
:param im_array: [b=1 c h w] in rgb
:param detections: [ numpy.ndarray([[x1 y1 x2 y2 score]]) for j in classes ]
:param class_names: list of names in imdb
:param scale: visualize the scaled image
:return:
"""
import matplotlib.pyplot as plt
import random
im = image.transform_inverse(im_array, config.PIXEL_MEANS)
plt.imshow(im)
for j, name in enumerate(class_names):
if name == '__background__':
continue
color = (random.random(), random.random(), random.random()) # generate a random color
dets = detections[j]
for det in dets:
bbox = det[:4] * scale
score = det[-1]
rect = plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor=color, linewidth=3.5)
plt.gca().add_patch(rect)
plt.gca().text(bbox[0], bbox[1] - 2,
'{:s} {:.3f}'.format(name, score),
bbox=dict(facecolor=color, alpha=0.5), fontsize=12, color='white')
plt.show()
Example 29
| Project: Deep-Feature-Flow-Segmentation Author: tonysy File: show_offset.py License: MIT License | 5 votes |
|
def show_boxes_simple(bbox, color='r', lw=2):
rect = plt.Rectangle((bbox[0], bbox[1]), bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False, edgecolor=color, linewidth=lw)
plt.gca().add_patch(rect)
Example 30
| Project: TFFRCNN Author: CharlesShang File: demo.py License: MIT License | 5 votes |
|
def vis_detections(im, class_name, dets, ax, thresh=0.5):
"""Draw detected bounding boxes."""
inds = np.where(dets[:, -1] >= thresh)[0]
if len(inds) == 0:
return
for i in inds:
bbox = dets[i, :4]
score = dets[i, -1]
ax.add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor='red', linewidth=3.5)
)
ax.text(bbox[0], bbox[1] - 2,
'{:s} {:.3f}'.format(class_name, score),
bbox=dict(facecolor='blue', alpha=0.5),
fontsize=14, color='white')
ax.set_title(('{} detections with '
'p({} | box) >= {:.1f}').format(class_name, class_name,
thresh),
fontsize=14)
plt.axis('off')
plt.tight_layout()
plt.draw()
