Python temporal_nms Examples

Example #1

File: fusion_eval_detection_results.py Project: zdy023/code

                    rel_prop, combined_scores[:, video_cls][:, None], loc_scores, dur_scores), axis=1)
            except:
                print(video_cls, rel_prop.shape, combined_scores.shape, reg_scores.shape, loc_scores.shape, dur_scores.shape)
                raise


print("Preprocessing detections...")
for k, v in detection_scores.items():
    gen_detection_results(k, v)
print('Done.')

# perform NMS
print("Performing nms...")
for cls in range(num_class):
    dataset_detections[cls] = {
        k: temporal_nms(v, nms_threshold) for k,v in dataset_detections[cls].items()
    }
print("NMS Done.")


def perform_regression(detections):
    t0 = detections[:, 0]
    t1 = detections[:, 1]
    center = (t0 + t1) / 2
    duration = (t1 - t0)

    new_center = center + duration * detections[:, 3]
    new_duration = duration * np.exp(detections[:, 4])

    new_detections = np.concatenate((
        np.clip(new_center - new_duration / 2, 0, 1)[:, None], np.clip(new_center + new_duration / 2, 0, 1)[:, None], detections[:, 2:]

Example #2

            except:
                print(video_cls, rel_prop.shape, combined_scores.shape,
                      reg_scores.shape, loc_scores.shape, dur_scores.shape)
                raise


print("Preprocessing detections...")
for k, v in detection_scores.items():
    gen_detection_results(k, v)
print('Done.')

# perform NMS
print("Performing nms...")
for cls in range(num_class):
    dataset_detections[cls] = {
        k: temporal_nms(v, nms_threshold)
        for k, v in dataset_detections[cls].items()
    }
print("NMS Done.")


def perform_regression(detections):
    t0 = detections[:, 0]
    t1 = detections[:, 1]
    center = (t0 + t1) / 2
    duration = (t1 - t0)

    new_center = center + duration * detections[:, 3]
    new_duration = duration * np.exp(detections[:, 4])

    new_detections = np.concatenate(

Example #3

File: eval_detection_results.py Project: codealphago/action-detection

                    rel_prop, combined_scores[:, video_cls][:, None], loc_scores, dur_scores), axis=1)
            except:
                print(video_cls, rel_prop.shape, combined_scores.shape, reg_scores.shape, loc_scores.shape, dur_scores.shape)
                raise


print("Preprocessing detections...")
for k, v in detection_scores.items():
    gen_detection_results(k, v)
print('Done.')

# perform NMS
print("Performing nms...")
for cls in range(num_class):
    dataset_detections[cls] = {
        k: temporal_nms(v, nms_threshold) for k,v in dataset_detections[cls].items()
    }
print("NMS Done.")


def perform_regression(detections):
    t0 = detections[:, 0]
    t1 = detections[:, 1]
    center = (t0 + t1) / 2
    duration = (t1 - t0)

    new_center = center + duration * detections[:, 3]
    new_duration = duration * np.exp(detections[:, 4])

    new_detections = np.concatenate((
        np.clip(new_center - new_duration / 2, 0, 1)[:, None], np.clip(new_center + new_duration / 2, 0, 1)[:, None], detections[:, 2:]