Convert result data from a numpy array [Nx7] where each row contains {imageID,x1,y1,w,h,score,class} :param data (numpy.ndarray) :return: annotations (python nested list)
(self, data)
| 380 | print('downloaded {}/{} images (t={:0.1f}s)'.format(i, N, time.time()- tic)) |
| 381 | |
| 382 | def loadNumpyAnnotations(self, data): |
| 383 | """ |
| 384 | Convert result data from a numpy array [Nx7] where each row contains {imageID,x1,y1,w,h,score,class} |
| 385 | :param data (numpy.ndarray) |
| 386 | :return: annotations (python nested list) |
| 387 | """ |
| 388 | print('Converting ndarray to lists...') |
| 389 | assert(type(data) == np.ndarray) |
| 390 | print(data.shape) |
| 391 | assert(data.shape[1] == 7) |
| 392 | N = data.shape[0] |
| 393 | ann = [] |
| 394 | for i in range(N): |
| 395 | if i % 1000000 == 0: |
| 396 | print('{}/{}'.format(i,N)) |
| 397 | ann += [{ |
| 398 | 'image_id' : int(data[i, 0]), |
| 399 | 'bbox' : [ data[i, 1], data[i, 2], data[i, 3], data[i, 4] ], |
| 400 | 'score' : data[i, 5], |
| 401 | 'category_id': int(data[i, 6]), |
| 402 | }] |
| 403 | return ann |
| 404 | |
| 405 | def annToRLE(self, ann): |
| 406 | """ |