Python matplotlib.pyplot.Rectangle() Examples

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 

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() 

def vis(im, index, dets, w, h, thresh=0.0):
    import numpy as np
    import matplotlib.pyplot as plt
    npimg = im.cpu().numpy()
    npimg = (npimg + 128)/255
    im = np.transpose(npimg, (1, 2, 0))
    plt.cla()
    plt.imshow(im)
    #print(bbox) 
    for i in range(np.minimum(10, dets.shape[0])):
        bbox = dets[i, :4]
        score = dets[i, -1]
        if score > thresh:
            plt.gca().add_patch(
                plt.Rectangle((bbox[0]*300/w, bbox[1]*300/h),
                              (bbox[2] - bbox[0])*300/w,
                              (bbox[3] - bbox[1])*300/h, fill=False,
                              edgecolor='g', linewidth=3)
                )
    plt.show() 

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 

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() 

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() 

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[:num_boxes,:].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() 

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 

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() 

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() 

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() 

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() 

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() 

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() 

def _vis_minibatch(im_blob, rois_blob, labels_blob, overlaps):
    """Visualize a mini-batch for debugging."""
    import matplotlib.pyplot as plt
    for i in range(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() 

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() 

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() 

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() 

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() 

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() 

def vis_all_detection(im_array, detections, class_names, scale, cfg, threshold=1e-3):
    """
    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, cfg.network.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]
            if score < threshold:
                continue
            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() 

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) 

def vis_all_detection(im_array, detections, class_names, scale, cfg, threshold=1e-3):
    """
    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, cfg.network.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]
            if score < threshold:
                continue
            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() 

def vis_all_detection(im_array, detections, class_names, scale, cfg, threshold=1e-3):
    """
    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, cfg.network.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]
            if score < threshold:
                continue
            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() 

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() 

def plot_bg_roi_rect(self, ax=None, to_pyrlevel=0):
        """Plot rectangular area used for background check."""
        roi = map_roi(self.settings.bg_roi_abs, to_pyrlevel)
        x, y, w, h = roi2rect(roi)
        r = Rectangle(xy=(x, y), width=w, height=h, fc="none", ec="r")
        ax.add_artist(r)
        return ax 

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) 

def vis_tracking_bbox(
        img, bbox, inst_id, label=None, score=None,
        label_names=None, alpha=1.0, ax=None):

    from matplotlib import pyplot as plot
    ax = vis_image(img, ax=ax)

    assert len(bbox) == len(inst_id)
    if len(bbox) == 0:
        return ax

    for i, (bb, inst_i) in enumerate(zip(bbox, inst_id)):
        bb = np.round(bb).astype(np.int32)
        y_min, x_min, y_max, x_max = bb
        color = np.array(_default_cmap(inst_i + 1)) / 255.

        ax.add_patch(plot.Rectangle(
            (x_min, y_min), x_max - x_min, y_max - y_min,
            fill=False, edgecolor=color, linewidth=3))

        caption = []
        caption.append('{}'.format(inst_i))

        if label is not None and label_names is not None:
            lb = label[i]
            if not (0 <= lb < len(label_names)):
                raise ValueError('No corresponding name is given')
            caption.append(label_names[lb])
        if score is not None:
            sc = score[i]
            caption.append('{:.2f}'.format(sc))

        ax.text((x_max + x_min) / 2, y_min,
                ': '.join(caption),
                style='italic',
                bbox={'facecolor': color, 'alpha': alpha},
                fontsize=8, color='white') 

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() 

def _vis_minibatch(im_blob, rois_blob, labels_blob, overlaps, sublabels_blob, view_targets_blob=None, view_inside_blob=None):
    """Visualize a mini-batch for debugging."""
    import matplotlib.pyplot as plt
    import math
    for i in xrange(min(rois_blob.shape[0], 10)):
        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]
        subcls = sublabels_blob[i]
        plt.imshow(im)
        print 'class: ', cls, ' subclass: ', subcls, ' overlap: ', overlaps[i]

        start = 3 * cls
        end = start + 3
        # print 'view: ', view_targets_blob[i, start:end] * 180 / math.pi
        # print 'view weights: ', view_inside_blob[i, start:end]

        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() 

def _vis_proposals(im, dets, thresh=0.5):
    """Draw detected bounding boxes."""
    inds = np.where(dets[:, -1] >= thresh)[0]
    if len(inds) == 0:
        return

    class_name = 'obj'
    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() 

def create_bbox(bbox, color):
  return Rectangle((bbox[0], bbox[1]), bbox[2], bbox[3],
                   fill=False,  # remove background\n",
                   edgecolor=color) 

def display_ori(image, outputs_dict):
  s = outputs_dict["output_scores"]
  l = outputs_dict["output_labels"]
  b = outputs_dict["output_bboxes"]

  h, w = image.shape[:2]
    
  plt.imshow(image)
  currentAxis = plt.gca()

  for score, label, box in zip(s, l, b):
    if score > THRESHOLD:
      box = np.asarray(box) * np.asarray([float(w), float(h), float(w), float(h)])
      xmin = np.clip(box[0], 0, w)
      ymin = np.clip(box[1], 0, h)
      xmax = np.clip(box[2], 0, w)
      ymax = np.clip(box[3], 0, h)
      label_name = MSCOCO_CAT_NAME[label - 1]

      display_txt = '%s: %.2f'%(label_name, score)

      coords = (xmin, ymin), xmax-xmin+1, ymax-ymin+1
      color = COLORS[label]

      currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor=color, linewidth=2))
      currentAxis.text(xmin, ymin, display_txt, bbox={'facecolor':color, 'alpha':0.5})

  plt.axis('off')
  plt.show() 

def display_ori(self, outputs_dict):
    for s, l, b, a, scale, translation, file_name in zip(
      outputs_dict["scores"],
      outputs_dict["labels"],
      outputs_dict["bboxes"],
      outputs_dict["anchors"],
      outputs_dict["scales"],
      outputs_dict["translations"],
      outputs_dict["file_name"]):

      input_image = misc.imread(file_name).astype(np.float32) / 255.0
      h, w = input_image.shape[:2]

      
      plt.imshow(input_image)
      currentAxis = plt.gca()

      for score, label, box in zip(s, l, b):
        box = box * [float(w), float(h), float(w), float(h)]
        xmin = np.clip(box[0], 0, w)
        ymin = np.clip(box[1], 0, h)
        xmax = np.clip(box[2], 0, w)
        ymax = np.clip(box[3], 0, h)
        label_name = MSCOCO_CAT_NAME[label - 1]

        display_txt = '%s: %.2f'%(label_name, score)

        coords = (xmin, ymin), xmax-xmin+1, ymax-ymin+1
        color = COLORS[label]

        currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor=color, linewidth=2))
        currentAxis.text(xmin, ymin, display_txt, bbox={'facecolor':color, 'alpha':0.5})

      plt.axis('off')
      plt.show() 

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() 

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() 

def vis_detections_col(im, class_name, dets, col_ind=0, 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=STANDARD_COLORS[col_ind], 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() 

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() 

def vis_detections_one_img(im, im_name, boxes, scores, NMS_THRESH=0.3, thresh=0.5):
  im = im[:, :, (2, 1, 0)]
  fig, ax = plt.subplots(figsize=(12, 12))
  ax.tick_params(top='off', bottom='off', left='off', right='off', labelleft='off', labelbottom='off')
  ax.imshow(im, aspect='equal')

  for cls_ind, cls in enumerate(CLASSES[1:]):
    cls_ind += 1  # because we skipped background
    cls_boxes = boxes[:, 4 * cls_ind:4 * (cls_ind + 1)]
    cls_scores = scores[:, cls_ind]
    dets = np.hstack((cls_boxes,
                      cls_scores[:, np.newaxis])).astype(np.float32)
    keep = nms(dets, NMS_THRESH)
    dets = dets[keep, :]

    """Draw detected bounding boxes."""
    inds = np.where(dets[:, -1] >= thresh)[0]
    if len(inds) == 0:
      continue

    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=STANDARD_COLORS[cls_ind], linewidth=3.5)
      )
      ax.text(bbox[0], bbox[1] - 2,
              '{:s} {:.3f}'.format(cls, score),
              bbox=dict(facecolor='blue', alpha=0.5),
              fontsize=14, color='white')
  fig.savefig(os.path.join('demo_save', im_name), format='jpg')

  plt.axis('off')
  plt.tight_layout()
  plt.draw() 

def plot_quad_in_map(self):
        x = self.state[0]
        y = self.state[2]
        th = self.state[4]
        r_quad = self.quad_rad
        
        ray_angles = self.get_ray_angles()
        ray_measurements = [self.ray_dist(x,y,th_r) for th_r in ray_angles]

        x_points = x + ray_measurements*np.cos(ray_angles)
        y_points = y + ray_measurements*np.sin(ray_angles)

        for xi, yi in zip(x_points, y_points):
            plt.plot([x,xi], [y, yi], color='r', linestyle=':', alpha=0.5)
        plt.plot(x_points, y_points, marker='+', color='r', linestyle='none')
        ax = plt.gca()
        for xo,yo,ro in zip(self.obst_X, self.obst_Y, self.obst_R):
            c = plt.Circle((xo,yo),ro, color='black', alpha=1.0)
            ax.add_artist(c)
       
        r = plt.Rectangle((self.xg_lower, self.yg_lower), self.xg_upper-self.xg_lower, self.yg_upper - self.yg_lower, color='g', alpha=0.3, hatch='/')
        ax.add_artist(r)

        plt.plot([x - r_quad*np.cos(th), x + r_quad*np.cos(th)], [y - r_quad*np.sin(th), y + r_quad*np.sin(th)], marker='o', linewidth=2, color='b', markersize=5)

        plt.xlim([self.x_lower, self.x_upper])
        plt.ylim([self.y_lower, self.y_upper]) 

def create_bbox(bbox, color):
  return Rectangle((bbox[0], bbox[1]), bbox[2], bbox[3],
                   fill=False,  # remove background\n",
                   edgecolor=color) 

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='green', linewidth=2)
            )
        # 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() 

def set_axis_settings_for_contour_plots(self, ax):
        if self.boxtype == 'circular':
            circle1=plt.Circle((0,0), self.radius, ec='black', fc='none', lw=2)
            ax.add_artist(circle1)
        if self.boxtype == 'linear':
            rectangle1=plt.Rectangle((-self.radius, -self.radius),
                    2*self.radius, 2*self.radius, ec='black', fc='none', lw=2)
            ax.add_artist(rectangle1)
        ax.set_aspect('equal')
        ax.set_xticks([])
        ax.set_yticks([]) 

def _uniform_noise_rectangle(self):
		"""
		Plots a rectangle that indicate the uniform noise
		"""
		central_center = np.array([0, 0])
		xy = (central_center[0] - self.spacing[0] / 2.,
			  central_center[1] - self.spacing[1] / 2.)
		rectangle1=plt.Rectangle(xy, self.spacing[0], self.spacing[1],
								 ec=color_cycle_blue3[0], fc=color_cycle_blue3[0], lw=2)
		plt.gca().add_artist(rectangle1)
		plt.text(central_center[0], central_center[1]+0.11, 'Uniform noise',
				 horizontalalignment='center',
				 verticalalignment='bottom', color=color_cycle_blue3[0],
				 fontsize=8, zorder=300) 

def _number_per_dimension_rectangle(self):
		"""
		Plots an ellipse that indicates the number per dimension
		"""
		# N_x rectangle
		y0 = - self.limit + 2 * self.spacing[0]
		y1 = - self.limit

		# xy = (central_center[0] - spacing[0] / 2.,
		# 	  central_center[1] - spacing[1] / 2.)
		rectangle1=plt.Rectangle((y0, y1), self.spacing[0], 2*self.limit,
								 ec='none',
								 fc=color_cycle_blue3[0], lw=2)
		plt.gca().add_artist(rectangle1) 

def vis_all_detection(im_array, detections, class_names, scale, cfg, threshold=1e-3):
    """
    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, cfg.network.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]
            if score < threshold:
                continue
            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() 

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) 

def box_to_rect(box, color="blue", linewidth=3):
    """

    :param box: (x_min,y_min,x_max,y_max)
    :param color:
    :param linewidth:
    :return:
    """
    """convert an anchor box to a matplotlib rectangle"""
    return plt.Rectangle(
        (box[0], box[1]), box[2]-box[0], box[3]-box[1],
        fill=False, edgecolor=color, linewidth=linewidth) 

def test_hatch():
    fig, ax = plt.subplots()
    ax.add_patch(plt.Rectangle((0, 0), 1, 1, fill=False, hatch="/"))
    ax.set_xlim((0.45, 0.55))
    ax.set_ylim((0.45, 0.55)) 

def test_hatching():
    fig, ax = plt.subplots()

    # Patches
    patch = plt.Rectangle((0, 0), 0.3, 0.3, hatch='xx',
                          label='Patch\ndefault color\nfilled')
    ax.add_patch(patch)
    patch = plt.Rectangle((0.33, 0), 0.3, 0.3, hatch='||', edgecolor='C1',
                          label='Patch\nexplicit color\nfilled')
    ax.add_patch(patch)
    patch = plt.Rectangle((0, 0.4), 0.3, 0.3, hatch='xx', fill=False,
                          label='Patch\ndefault color\nunfilled')
    ax.add_patch(patch)
    patch = plt.Rectangle((0.33, 0.4), 0.3, 0.3, hatch='||', fill=False,
                          edgecolor='C1',
                          label='Patch\nexplicit color\nunfilled')
    ax.add_patch(patch)

    # Paths
    ax.fill_between([0, .15, .3], [.8, .8, .8], [.9, 1.0, .9],
                    hatch='+', label='Path\ndefault color')
    ax.fill_between([.33, .48, .63], [.8, .8, .8], [.9, 1.0, .9],
                    hatch='+', edgecolor='C2', label='Path\nexplicit color')

    ax.set_xlim(-0.01, 1.1)
    ax.set_ylim(-0.01, 1.1)
    ax.legend(handlelength=4, handleheight=4)