我们从Python开源项目中，提取了以下2个代码示例，用于说明如何使用skimage.io.show()。
def get_sport_clip(clip_name, verbose=True): """ Loads a clip to be fed to C3D for classification. TODO: should I remove mean here? Parameters ---------- clip_name: str the name of the clip (subfolder in 'data'). verbose: bool if True, shows the unrolled clip (default is True). Returns ------- Tensor a pytorch batch (n, ch, fr, h, w). """ clip = sorted(glob(join('data', clip_name, '*.png'))) clip = np.array([resize(io.imread(frame), output_shape=(112, 200), preserve_range=True) for frame in clip]) clip = clip[:, :, 44:44+112, :] # crop centrally if verbose: clip_img = np.reshape(clip.transpose(1, 0, 2, 3), (112, 16 * 112, 3)) io.imshow(clip_img.astype(np.uint8)) io.show() clip = clip.transpose(3, 0, 1, 2) # ch, fr, h, w clip = np.expand_dims(clip, axis=0) # batch axis clip = np.float32(clip) return torch.from_numpy(clip)
def selectiveSearch(image): segments = felzenszwalb(image, scale=kFelzenszwalbScale) numRegions = segments.max() rectangles = [] for regionTag in range(numRegions): selectedRegion = segments == regionTag regionPixelIndices = np.transpose(np.nonzero(selectedRegion)) rectangle = aabb(regionPixelIndices) rectangles.append(rectangle) # Implement similarities, neighbourhood merging. # Felzenszwalb's segmentation is ridiculously good already. def debug(): marked = np.zeros(image.shape, dtype=np.uint8) for rectangle in rectangles: rr, cc = rectangle.pixels(marked.shape) randcolor = randint(0, 255), randint(0, 255), randint(0, 255) marked[rr, cc] = randcolor print(image.shape, segments.shape, marked.shape) io.imshow_collection([image, segments, marked]) io.show() # debug() return rectangles
