Question

我开始使用COCO Dataset API- 作者提供的一组脚本，可轻松访问和浏览数据集。

在提供的示例demo notebook中，作者展示了一个加载图像，其标签，注释等的示例。

加载图像的标准方法：

有人可以向我解释下一部分：

# load and display instance annotations
plt.imshow(I); plt.axis('off')
annIds = coco.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)
anns = coco.loadAnns(annIds)
coco.showAnns(anns)

这给出了带有图像注释的不错的输出。它使用PatchCollections内部在图像上创建所需多边形的补丁。

如何保存该图像？

如果我尝试做

fig = plt.gcf()
fig.savefig("Annotated.jpg")

它给了我一块空白的画布。

如果我尝试做：

plt.imwrite("Annotated.jpg", I)

这仅保存原始图像。

供参考code of showAnns：

def showAnns(self, anns):
    """
    Display the specified annotations.
    :param anns (array of object): annotations to display
    :return: None
    """
    if len(anns) == 0:
        return 0
    if 'segmentation' in anns[0] or 'keypoints' in anns[0]:
        datasetType = 'instances'
    elif 'caption' in anns[0]:
        datasetType = 'captions'
    else:
        raise Exception('datasetType not supported')
    if datasetType == 'instances':
        ax = plt.gca()
        ax.set_autoscale_on(False)
        polygons = []
        color = []
        for ann in anns:
            c = (np.random.random((1, 3))*0.6+0.4).tolist()[0]
            if 'segmentation' in ann:
                if type(ann['segmentation']) == list:
                    # polygon
                    for seg in ann['segmentation']:
                        poly = np.array(seg).reshape((int(len(seg)/2), 2))
                        polygons.append(Polygon(poly))
                        color.append(c)
                else:
                    # mask
                    t = self.imgs[ann['image_id']]
                    if type(ann['segmentation']['counts']) == list:
                        rle = maskUtils.frPyObjects([ann['segmentation']], t['height'], t['width'])
                    else:
                        rle = [ann['segmentation']]
                    m = maskUtils.decode(rle)
                    img = np.ones( (m.shape[0], m.shape[1], 3) )
                    if ann['iscrowd'] == 1:
                        color_mask = np.array([2.0,166.0,101.0])/255
                    if ann['iscrowd'] == 0:
                        color_mask = np.random.random((1, 3)).tolist()[0]
                    for i in range(3):
                        img[:,:,i] = color_mask[i]
                    ax.imshow(np.dstack( (img, m*0.5) ))
            if 'keypoints' in ann and type(ann['keypoints']) == list:
                # turn skeleton into zero-based index
                sks = np.array(self.loadCats(ann['category_id'])[0]['skeleton'])-1
                kp = np.array(ann['keypoints'])
                x = kp[0::3]
                y = kp[1::3]
                v = kp[2::3]
                for sk in sks:
                    if np.all(v[sk]>0):
                        plt.plot(x[sk],y[sk], linewidth=3, color=c)
                plt.plot(x[v>0], y[v>0],'o',markersize=8, markerfacecolor=c, markeredgecolor='k',markeredgewidth=2)
                plt.plot(x[v>1], y[v>1],'o',markersize=8, markerfacecolor=c, markeredgecolor=c, markeredgewidth=2)
        p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4)
        ax.add_collection(p)
        p = PatchCollection(polygons, facecolor='none', edgecolors=color, linewidths=2)
        ax.add_collection(p)
    elif datasetType == 'captions':
        for ann in anns:
            print(ann['caption'])

在Matplotlib中的add_collection之后保存图

0 个答案: