def count_quvacount(dataset, localization_method, segmentation_path):
"""
Count entire QUVACount dataset using the online method.
When using the fullsystem this should return approximately these results (20170925):
Num Examples: 100
Mean Abs. Err.: 45.668 +- 58.710
Mean Sq. Err.: 86.390
Accuracy: 0.190
OBOA: 0.440
Within-10%: 0.280
Frac. Zero: 0.000
:param dataset_path:
:return:
"""
# Prepare the CNN in Theano
(
test_set_x,
test_set_y,
shared_test_set_y,
valid_ds,
classify,
batchsize,
) = prepare_network()
# Initialize structure for storing results
cnt_pred = np.zeros(dataset.num_examples, np.float32)
cnt_true = np.zeros(dataset.num_examples, np.int32)
while dataset.has_next():
example = dataset.next_example()
# Actual counting of the video
cnt_pred[example.index] = load_and_count_video(
example.video_path,
classify,
test_set_x,
batchsize,
localization_method,
segmentation_path,
)
cnt_true[example.index] = example.rep_count
print((
"Video {}. True Count = {}, Predict Count = {}".format(
example.name, example.rep_count, cnt_pred[example.index]
)
))
return cnt_true, cnt_pred
def count_quvacount_accelerate(dataset_path, vid_accelate_path):
'''
Same as above but on a subset of videos in which the last half is accelerated
Acceleration 2.0x results (2017-09-25):
Num Examples: 36
Mean Abs. Err.: 29.407 +- 21.347
Mean Sq. Err.: 132.222
Accuracy: 0.028
OBOA: 0.417
Within-10%: 0.111
Frac. Zero: 0.000
Acceleration 0.5x results (2017-09-25):
Num Examples: 36
Mean Abs. Err.: 92.063 +- 91.985
Mean Sq. Err.: 268.083
Accuracy: 0.028
OBOA: 0.250
Within-10%: 0.139
Frac. Zero: 0.000
:param dataset_path:
:param vid_accelate_path:
:return:
'''
# Prepare the CNN in Theano
test_set_x, test_set_y, shared_test_set_y, valid_ds, classify, batchsize = \
prepare_network()
dataset = QUVACountDataset(dataset_path)
# Initialize structure for storing results
cnt_pred = []
cnt_true = []
while dataset.has_next():
example = dataset.next_example()
vid_accelerate = os.path.join(vid_accelate_path, example.name + ".avi")
print(vid_accelerate)
# Check if there is an accelerated version of it
if not os.path.exists(vid_accelerate):
continue
# Actual counting of the video
cnt_pred_curr = load_and_count_video(vid_accelerate, classify,
test_set_x, batchsize)[0]
cnt_pred.append(cnt_pred_curr)
cnt_true.append(example.rep_count)
print(" True Count = {}, Predict Count = {}".format(
example.rep_count, cnt_pred[-1]))
cnt_pred = np.asarray(cnt_pred, np.int32)
cnt_true = np.asarray(cnt_true, np.int32)
return cnt_true, cnt_pred