Python SSNDataSet.get_all_gt Beispiele

Beispiel #1

# ravel test detections
def ravel_detections(detection_db, cls):
    detection_list = []
    for vid, dets in detection_db[cls].items():
        detection_list.extend([[vid, cls] + x[:3] for x in dets.tolist()])
    df = pd.DataFrame(detection_list,
                      columns=["video-id", "cls", "t-start", "t-end", "score"])
    return df


plain_detections = [
    ravel_detections(dataset_detections, cls) for cls in range(num_class)
]

# get gt
all_gt = pd.DataFrame(dataset.get_all_gt(),
                      columns=["video-id", "cls", "t-start", "t-end"])
gt_by_cls = []
for cls in range(num_class):
    gt_by_cls.append(all_gt[all_gt.cls == cls].reset_index(drop=True).drop(
        'cls', 1))

pickle.dump(gt_by_cls, open('gt_dump.pc', 'wb'), pickle.HIGHEST_PROTOCOL)
pickle.dump(plain_detections, open('pred_dump.pc', 'wb'),
            pickle.HIGHEST_PROTOCOL)
print("Calling mean AP calculator from toolkit with {} workers...".format(
    args.ap_workers))

if args.dataset == 'activitynet1.2':
    iou_range = np.arange(0.5, 1.0, 0.05)
elif args.dataset == 'thumos14':

Beispiel #2

Datei: fusion_eval_detection_results.py Projekt: zdy023/code

    print("Skip regresssion as requested by --no_regression")


# ravel test detections
def ravel_detections(detection_db, cls):
    detection_list = []
    for vid, dets in detection_db[cls].items():
        detection_list.extend([[vid, cls] + x[:3] for x in dets.tolist()])
    df = pd.DataFrame(detection_list, columns=["video-id", "cls","t-start", "t-end", "score"])
    return df

plain_detections = [ravel_detections(dataset_detections, cls) for cls in range(num_class)]


# get gt
all_gt = pd.DataFrame(dataset.get_all_gt(), columns=["video-id", "cls","t-start", "t-end"])
gt_by_cls = []
for cls in range(num_class):
    gt_by_cls.append(all_gt[all_gt.cls == cls].reset_index(drop=True).drop('cls', 1))

pickle.dump(gt_by_cls, open('gt_dump.pc', 'wb'), pickle.HIGHEST_PROTOCOL)
pickle.dump(plain_detections, open('pred_dump.pc', 'wb'), pickle.HIGHEST_PROTOCOL)
print("Calling mean AP calculator from toolkit with {} workers...".format(args.ap_workers))

if args.dataset == 'activitynet1.2':
    iou_range = np.arange(0.5, 1.0, 0.05)
elif args.dataset == 'thumos14':
    iou_range = np.arange(0.1, 1.0, 0.1)
elif args.dataset == 'coin_small':
    iou_range = np.arange(0.1, 1.0, 0.1)
else:

Beispiel #3

Datei: eval_detection_results.py Projekt: hyperfraise/action-detection

    for vid, dets in list(detection_db[cls].items()):
        detection_list.extend([[vid, cls] + x[:3] for x in dets.tolist()])
    df = pd.DataFrame(
        detection_list, columns=["video-id", "cls", "t-start", "t-end", "score"]
    )
    return df


plain_detections = [
    ravel_detections(dataset_detections, cls) for cls in range(num_class)
]


# get gt
all_gt = pd.DataFrame(
    dataset.get_all_gt(), columns=["video-id", "cls", "t-start", "t-end"]
)
gt_by_cls = []
for cls in range(num_class):
    gt_by_cls.append(all_gt[all_gt.cls == cls].reset_index(drop=True).drop("cls", 1))

pickle.dump(gt_by_cls, open("gt_dump.pc", "wb"), pickle.HIGHEST_PROTOCOL)
pickle.dump(plain_detections, open("pred_dump.pc", "wb"), pickle.HIGHEST_PROTOCOL)
print(
    (
        "Calling mean AP calculator from toolkit with {} workers...".format(
            args.ap_workers
        )
    )
)

Beispiel #4

Datei: eval_detection_results.py Projekt: codealphago/action-detection

    print("Skip regresssion as requested by --no_regression")


# ravel test detections
def ravel_detections(detection_db, cls):
    detection_list = []
    for vid, dets in detection_db[cls].items():
        detection_list.extend([[vid, cls] + x[:3] for x in dets.tolist()])
    df = pd.DataFrame(detection_list, columns=["video-id", "cls","t-start", "t-end", "score"])
    return df

plain_detections = [ravel_detections(dataset_detections, cls) for cls in range(num_class)]


# get gt
all_gt = pd.DataFrame(dataset.get_all_gt(), columns=["video-id", "cls","t-start", "t-end"])
gt_by_cls = []
for cls in range(num_class):
    gt_by_cls.append(all_gt[all_gt.cls == cls].reset_index(drop=True).drop('cls', 1))

pickle.dump(gt_by_cls, open('gt_dump.pc', 'wb'), pickle.HIGHEST_PROTOCOL)
pickle.dump(plain_detections, open('pred_dump.pc', 'wb'), pickle.HIGHEST_PROTOCOL)
print("Calling mean AP calculator from toolkit with {} workers...".format(args.ap_workers))

if args.dataset == 'activitynet1.2':
    iou_range = np.arange(0.5, 1.0, 0.05)
elif args.dataset == 'thumos14':
    iou_range = np.arange(0.1, 1.0, 0.1)
else:
    raise ValueError("unknown dataset {}".format(args.dataset))