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()