def analyze_outs(predict, labels, label_names, frame_thresh=0.7, dist_threshold=10):
# next apply non max suppression
# labels = labels[0]
num_labels = labels.shape[1]
all_matches = []
for i in range(num_labels):
ground_truth = labels[:, i]
gt_sup, gt_idx = post_processing.nonmax_suppress(
ground_truth, frame_thresh)
predict_sup, predict_idx = post_processing.nonmax_suppress(
predict[:, i], frame_thresh)
match_dict, dist_mat = hungarian_matching.apply_hungarian(
gt_idx, predict_idx, dist_threshold=dist_threshold
)
match_dict["tps"] = len(match_dict["tps"])
match_dict["fps"] = len(match_dict["fps"])
# # write processed file
# output_name = os.path.join(
# out_dir, exp_names[0], 'processed_%s.csv' % label_names[i]
# )
# create_proc_file(output_name, gt_sup, predict_sup, match_dict)
all_matches.append(match_dict)
return all_matches
def analyze_outs(predict, labels, label_names, frame_thresh=0.7):
# next apply non max suppression
# labels = labels[0]
num_labels = labels.shape[1]
all_dists = {
'lift': [],
'hand': [],
'grab': [],
'supinate': [],
'mouth': [],
'chew': []
}
dist_keys = label_names
all_matches = []
for i in range(num_labels):
ground_truth = labels[:, i]
gt_sup, gt_idx = post_processing.nonmax_suppress(
ground_truth, frame_thresh)
predict_sup, predict_idx = post_processing.nonmax_suppress(
predict[:, i], frame_thresh)
match_dict, dist_mat = hungarian_matching.apply_hungarian(
gt_idx, predict_idx
)
# predict_idx = match_dict["tps"]
predict_idx = match_dict["fps"]
# setup "greedy matching" to make a list of distances for each prediction.
dist_mat = numpy.zeros((len(gt_idx), len(predict_idx)))
abs_dist_mat = numpy.zeros((len(gt_idx), len(predict_idx)))
for j in range(len(gt_idx)):
for k in range(len(predict_idx)):
# dist_mat[j, k] = predict_idx[k][1] - gt_idx[j]
# abs_dist_mat[j, k] = abs(predict_idx[k][1] - gt_idx[j])
dist_mat[j, k] = predict_idx[k] - gt_idx[j]
abs_dist_mat[j, k] = abs(predict_idx[k] - gt_idx[j])
# get the min idx for each column (each prediction).
min_dists = []
if len(gt_idx) > 0 and len(predict_idx) > 0:
min_idx = numpy.argmin(abs_dist_mat, axis=0)
for j in range(len(predict_idx)):
min_dists.append(dist_mat[min_idx[j], j])
all_dists[dist_keys[i]] += min_dists
# if len(gt_idx) > 1 and len(predict_idx) > 1:
# import pdb; pdb.set_trace()
# match_dict, dist_mat = hungarian_matching.apply_hungarian(
# gt_idx, predict_idx
# )
# if len(match_dict["tps"]) > 0:
# import pdb; pdb.set_trace()
# match_dict["tps"] = len(match_dict["tps"])
# match_dict["fps"] = len(match_dict["fps"])
# # write processed file
# output_name = os.path.join(
# out_dir, exp_names[0], 'processed_%s.csv' % label_names[i]
# )
# create_proc_file(output_name, gt_sup, predict_sup, match_dict)
# all_matches.append(dist_mat)
return all_dists
def eval_scores(scores, score_thresh=0.7, dist_thresh=30.0):
gt_sup, gt_idx = post_processing.nonmax_suppress(scores[:, 1],
score_thresh)
predict_sup, predict_idx = post_processing.nonmax_suppress(
scores[:, 0], score_thresh)
# go through each prediction and see if it is a false positive or true
# positive.
predicts = predict_sup[predict_idx]
is_tp = numpy.zeros(predicts.shape)
is_gt = numpy.zeros(predicts.shape)
match_dict, dist_mat = hungarian_matching.apply_hungarian(
gt_idx,
predict_idx,
val_threshold=score_thresh,
dist_threshold=dist_thresh)
for j in range(len(predict_idx)):
for i in range(len(gt_idx)):
# check if in the right range?
# dist mat is buffered by dummy nodes (of the number of gt's)
if numpy.argmin(dist_mat[:, j + len(gt_idx)]) < dist_thresh:
# is this prediction the lowest score for any gt?
dists = dist_mat[i, :]
if numpy.argmin(dists) == j + len(gt_idx):
is_tp[j] = dists[j + len(gt_idx)]
is_gt[j] = True
# no reason to check for other matches
break
return predicts, is_tp, is_gt, len(gt_idx)
def analyze_outs(csv_dir, predict, labels, label_names, frame_thresh=0.1):
# next apply non max suppression
# labels = labels[0]
num_labels = labels.shape[1]
# create a mask for the network predictions.
# apply an argmax to figure out which locations the network was most sure
# that a behavior has occured
predict_mask = numpy.zeros(predict.shape)
predict_max_idx = numpy.argmax(predict, axis=1)
for i in range(len(predict_max_idx)):
# "i" should be the row.
j = predict_max_idx[i]
predict_mask[i, j] = 1
# next convert the mask into predictions using the rules described by
# startnet/odas paper.
# c_t = argmax is an action.
# c_t != c_{t-1}
# as_t^{c_t} exceeds a threshold. This isn't available for odas.
# set the previous behavior to background
c_tminus1 = 6
predict_starts = numpy.zeros(predict.shape)
for i in range(predict_mask.shape[0]):
c_t = predict_max_idx[i]
if c_t != 6 and c_t != c_tminus1:
predict_starts[i, c_t] = 1
c_tminus1 = c_t
# write predictions back to disk
# copy the templates
for label_name in label_names:
csv_name = "odas_" + label_name + ".csv"
base_out = csv_dir
sequences_helper.create_html_file(base_out, csv_name, "movie_comb.avi",
30)
# write the predictions.
exp_name = os.path.basename(base_out)
write_csvs(base_out, exp_name, label_names, labels, predict_starts)
all_matches = []
for i in range(num_labels):
ground_truth = labels[:, i]
gt_sup, gt_idx = post_processing.nonmax_suppress(
ground_truth, frame_thresh)
predict_sup, predict_idx = post_processing.nonmax_suppress(
predict[:, i], frame_thresh)
match_dict, dist_mat = hungarian_matching.apply_hungarian(
gt_idx, predict_idx)
match_dict["tps"] = len(match_dict["tps"])
match_dict["fps"] = len(match_dict["fps"])
# # write processed file
# output_name = os.path.join(
# out_dir, exp_names[0], 'processed_%s.csv' % label_names[i]
# )
# create_proc_file(output_name, gt_sup, predict_sup, match_dict)
all_matches.append(match_dict)
return all_matches
def eval_scores(scores, score_thresh=0.7, dist_thresh=30.0):
gt_sup, gt_idx = post_processing.nonmax_suppress(scores[:, 1],
score_thresh)
predict_sup, predict_idx = post_processing.nonmax_suppress(
scores[:, 0], score_thresh)
# go through each prediction and see if it is a false positive or true
# positive.
predicts = predict_sup[predict_idx]
is_tp = numpy.zeros(predicts.shape)
is_gt = numpy.zeros(predicts.shape)
match_dict, dist_mat = hungarian_matching.apply_hungarian(
gt_idx,
predict_idx,
val_threshold=score_thresh,
dist_threshold=dist_thresh)
# # loop over the predictions, and create a is_tp_fp indicator
# for i in range(len(match_dict["tps"])):
# # second element is the prediction index
# is_tp[match_dict["tps"][i][1]] = 1
# is_tp = is_tp[predict_idx]
# for i in range(len(match_dict["fps"])):
# # second element is the prediction index
# is_tp[match_dict["tps"][i][1]] = 1
# # try:
# # is_tp[match_dict["tps"][i][1] == numpy.asarray(predict_idx)] = 1
# # except:
# # import pdb; pdb.set_trace()
for j in range(len(predict_idx)):
for i in range(len(gt_idx)):
# check if in the right range?
# dist mat is buffered by dummy nodes (of the number of gt's)
if numpy.argmin(dist_mat[:, j + len(gt_idx)]) < dist_thresh:
# is this prediction the lowest score for any gt?
dists = dist_mat[i, :]
if numpy.argmin(dists) == j + len(gt_idx):
is_tp[j] = dists[j + len(gt_idx)]
is_gt[j] = True
# no reason to check for other matches
break
# else:
# is_tp[j] = -1
# import pdb; pdb.set_trace()
# if len(gt_idx) > 2:
# import pdb; pdb.set_trace()
return predicts, is_tp, is_gt, len(gt_idx)
def analyze_outs(out_dir, exp_names, predict, labels, frame_thresh=0.7):
# next apply non max suppression
labels = labels[0]
num_labels = labels.shape[2]
all_matches = []
for i in range(num_labels):
ground_truth = labels[:, 0, i]
gt_sup, gt_idx = post_processing.nonmax_suppress(
ground_truth, frame_thresh)
predict_sup, predict_idx = post_processing.nonmax_suppress(
predict[:, 0, i], frame_thresh)
match_dict, dist_mat = hungarian_matching.apply_hungarian(
gt_idx, predict_idx)
# write processed file
output_name = os.path.join(out_dir, exp_names[0].decode("utf-8"),
'processed_%s.csv' % g_label_names[i])
create_proc_file(output_name, gt_sup, predict_sup, match_dict)
all_matches.append(match_dict)
# import pdb; pdb.set_trace()
return all_matches
def proc_prediction_file(predict_file):
"""proc_prediction
Process a single prediction file. Given a csv output of the form
frame num, prediction, ground truth.
"""
# Post processing flow:
# nonmax suppress
# Apply hungarian matching
# Compute stats.
csv_data = post_processing.load_predict_csv(predict_file)
# always assume there is a ground truth key name, and skip anything called
# frames
key_names = csv_data.keys()
# get the values out of the dictionary
ground_truth = []
predict = []
for key in key_names:
if "ground truth" in key:
ground_truth = csv_data[key]
elif "frame" not in key:
predict = csv_data[key]
# next apply non max suppression
gt_sup, gt_idx = post_processing.nonmax_suppress(ground_truth, 0.7)
predict_sup, predict_idx = post_processing.nonmax_suppress(predict, 0.7)
# hungarian matching
match_dict, dist_mat = hungarian_matching.apply_hungarian(
gt_idx, predict_idx
)
# create the post processed file
output_name = predict_file.replace("predict_", "processed_")
create_proc_file(output_name, gt_sup, predict_sup, match_dict)
return match_dict
def process_prediction_csv(filename,
out_filename,
frame_thresh=5,
val_threshold=0.75):
"""Given a predict_*.csv file, post process it."""
# run non max on the labels and data
all_csv = load_predict_csv(filename)
# loop over the rows (2nd row and on) and convert to numeric
# remove header
data, all_csv = convert_csv_numpy2(all_csv)
all_csv = data
# all_csv = numpy.asarray(all_csv[1:])
processed = data
# predicted
suppressed, max_vals = nonmax_suppress(data[:, 1], val_threshold)
processed[:, 1] = suppressed
# import pdb; pdb.set_trace()
# ground truth
suppressed, _ = nonmax_suppress(data[:, 2], val_threshold)
processed[:, 2] = suppressed
labelled = numpy.argwhere(processed[:, 2] == 1)
labelled = labelled.flatten().tolist()
# add a dummy variable for each of the nodes.
# dist_mat = create_frame_dists(processed, max_vals, labelled)
# num_found = len(max_vals)
num_labelled = len(labelled)
# rows, cols, dist_mat = apply_hungarian(dist_mat, frame_thresh)
match_dict, dist_mat = hungarian_matching.apply_hungarian(
max_vals, labelled)
# missed classifications
# false_neg = len(labelled) - len(
# [i for i in range(len(max_vals)) if cols[i] < len(labelled)])
false_neg = match_dict["num_fn"]
# extra predictions
# for each idx in labelled, find its location in the cols array. The
# location in the cols array represents the row id, which is related
# to the max_vals ids.
# false_pos = len(max_vals) - len(
# [i for i in range(len(labelled))
# if numpy.where(i == cols)[0][0] < len(max_vals)])
false_pos = len(match_dict["fps"])
# create a graph with this data
with open(out_filename, "w") as file:
# write the header first
file.write("frame,predicted,ground truth,image,nearest\n")
for i in range(all_csv.shape[0]):
file.write("%f,%f,%f,%s" %
(processed[i, 0], processed[i, 1], processed[i, 2],
"frames/%05d.jpg" % i))
if i in max_vals:
# this is dumb...
idx = numpy.where(numpy.asarray(max_vals) == i)
# col_idx = cols[idx][0]
# row_idx = rows[idx][0]
# import pdb; pdb.set_trace()
for j in range(len(match_dict["tps"])):
if i == match_dict["tps"][j][0]:
match = match_dict["tps"][j][1]
file.write(",%d" % match)
else:
file.write(",no match")
# if col_idx < len(labelled) and\
# dist_mat[row_idx, col_idx] < frame_thresh:
# match = processed[labelled[col_idx], 0]
# file.write(",%d" % match)
# else:
# # this peak does not have a matched peak
# file.write(",no match")
# if i in max_vals:
# # if "M134_20141203_v030" in out_filename\
# # and "hand" in out_filename:
# # import pdb; pdb.set_trace()
# # is this a peak?
# # if it is a peak, does it have a match
# idx = numpy.where(numpy.asarray(max_vals) == i)
# if cols[idx][0] >= len(labelled):
# # this peak does not have a matched peak
# file.write(",no match")
# else:
# match = processed[labelled[cols[idx][0]], 0]
# file.write(",%d" % match)
else:
file.write(",N/A")
file.write("\n")
# mean_abs_offset += numpy.abs(dist)
# num_count += 1
# dists = dist_mat[rows[list(range(len(max_vals)))], cols[list(range(len(max_vals)))]]
# dists = dists[dists < frame_thresh]
dists = [numpy.abs(match[1] - match[0]) for match in match_dict["tps"]]
# import pdb; pdb.set_trace()
# import pdb; pdb.set_trace()
# return processed, data, all_csv, num_labelled, dists, missed, extra
return num_labelled, dists, false_neg, false_pos
