def run_evaluation(labelmap, groundtruth, detections, exclusions):
"""Runs evaluations given input files.
Args:
labelmap: file object containing map of labels to consider, in pbtxt format
groundtruth: file object
detections: file object
exclusions: file object or None.
"""
categories, class_whitelist = read_labelmap(labelmap)
logging.info("CATEGORIES (%d):\n%s", len(categories),
pprint.pformat(categories, indent=2))
excluded_keys = read_exclusions(exclusions)
pascal_evaluator = object_detection_evaluation.PascalDetectionEvaluator(
categories)
# Reads the ground truth data.
boxes, labels, _ = read_csv(groundtruth, class_whitelist, 0)
start = time.time()
for image_key in boxes:
if image_key in excluded_keys:
logging.info(("Found excluded timestamp in ground truth: %s. "
"It will be ignored."), image_key)
continue
pascal_evaluator.add_single_ground_truth_image_info(
image_key, {
standard_fields.InputDataFields.groundtruth_boxes:
np.array(boxes[image_key], dtype=float),
standard_fields.InputDataFields.groundtruth_classes:
np.array(labels[image_key], dtype=int),
standard_fields.InputDataFields.groundtruth_difficult:
np.zeros(len(boxes[image_key]), dtype=bool)
})
print_time("convert groundtruth", start)
# Reads detections data.
boxes, labels, scores = read_csv(detections, class_whitelist, 50)
start = time.time()
for image_key in boxes:
if image_key in excluded_keys:
logging.info(("Found excluded timestamp in detections: %s. "
"It will be ignored."), image_key)
continue
pascal_evaluator.add_single_detected_image_info(
image_key, {
standard_fields.DetectionResultFields.detection_boxes:
np.array(boxes[image_key], dtype=float),
standard_fields.DetectionResultFields.detection_classes:
np.array(labels[image_key], dtype=int),
standard_fields.DetectionResultFields.detection_scores:
np.array(scores[image_key], dtype=float)
})
print_time("convert detections", start)
start = time.time()
metrics = pascal_evaluator.evaluate()
print_time("run_evaluator", start)
pprint.pprint(metrics, indent=2)
def __init__(self):
this_dir = os.path.dirname(__file__)
lib_path = os.path.join(this_dir, '../external/ActivityNet/Evaluation')
sys.path.insert(0, lib_path)
from ava import object_detection_evaluation
from ava import standard_fields
sys.path.pop(0)
self.sf = standard_fields
categories = [{"id": i, "name": i} for i in range(1, 81)]
self.evaluator = object_detection_evaluation.PascalDetectionEvaluator(
categories)
def __init__(self):
this_dir = os.path.dirname(__file__)
lib_path = os.path.join(this_dir, '../external/ActivityNet/Evaluation')
sys.path.insert(0, lib_path)
from ava import object_detection_evaluation
from ava import standard_fields
sys.path.pop(0)
self.sf = standard_fields
categories = [{"id": i, "name": i} for i in range(1, 81)]
self.evaluator = object_detection_evaluation.PascalDetectionEvaluator(
categories)
top60path = '../external/ActivityNet/Evaluation/ava/ava_action_list_v2.1_for_activitynet_2018.pbtxt.txt'
top60path = os.path.join(this_dir, top60path)
with open(top60path) as f:
self.top60 = [int(x) for x in re.findall('[0-9]+', f.read())]
def run_evaluation(labelmap, groundtruth, exclusions, iou):
root_dir = '../../../data/AVA/files/'
test_dir = "../test_outputs/"
# Make sure not to mess this up
experiments_filters = {}
experiments_detections = {}
experiments_filters['pose'] = ['Pose']
experiments_detections['pose'] = [
open(test_dir + "/rgb_rgb/output_test_rgb.csv", 'rb')
]
experiments_filters['rgb-streams-aug'] = ['RGB', 'Crop', 'Gauss', 'Fovea']
experiments_detections['rgb-streams-aug'] = [
open(test_dir + "/rgb_rgb/output_test_rgb.csv", 'rb'),
open(test_dir + "/rgb_crop/output_test_crop.csv", 'rb'),
open(test_dir + "/rgb_gauss/output_test_gauss.csv", 'rb'),
open(test_dir + "/rgb_fovea/output_test_fovea.csv", 'rb')
]
experiments_filters['flow vs flowcrop'] = ['Flow', 'Flowcrop']
experiments_detections['flow vs flowcrop'] = [
open(test_dir + "/flow/output_test_flowcrop.csv", 'rb'),
]
#all_detections.append(open(test_dir + "/flow/output_test_flow.csv", 'rb'))
experiments_filters['two-streams'] = [
'Two-Stream-RGB', 'Two-Stream-Crop', 'Two-Stream-Gauss',
'Two-Stream-Fovea'
]
experiments_detections['two-streams'] = [
open(test_dir + "/rgb_rgb/output_test_rgb.csv", 'rb'),
open(test_dir + "/rgb_crop/output_test_crop.csv", 'rb'),
open(test_dir + "/rgb_gauss/output_test_gauss.csv", 'rb'),
open(test_dir + "/rgb_fovea/output_test_fovea.csv", 'rb')
]
experiments_filters['two-streams-aug'] = ['RGB', 'Crop', 'Gauss', 'Fovea']
experiments_detections['two-streams-aug'] = [
open(test_dir + "/rgb_rgb/output_test_rgb.csv", 'rb'),
open(test_dir + "/rgb_crop/output_test_crop.csv", 'rb'),
open(test_dir + "/rgb_gauss/output_test_gauss.csv", 'rb'),
open(test_dir + "/rgb_fovea/output_test_fovea.csv", 'rb')
]
experiments_filters['mlp vs lstm'] = ['RGB', 'Crop', 'Gauss', 'Fovea']
experiments_detections['mlp vs lstm'] = [
open(test_dir + "/rgb_rgb/output_test_rgb.csv", 'rb'),
open(test_dir + "/rgb_crop/output_test_crop.csv", 'rb'),
open(test_dir + "/rgb_gauss/output_test_gauss.csv", 'rb'),
open(test_dir + "/rgb_fovea/output_test_fovea.csv", 'rb')
]
experiments_filters['lstmA vs lstmB'] = ['RGB', 'Crop', 'Gauss', 'Fovea']
experiments_detections['lstmA vs lstmB'] = [
open(test_dir + "/rgb_rgb/output_test_rgb.csv", 'rb'),
open(test_dir + "/rgb_crop/output_test_crop.csv", 'rb'),
open(test_dir + "/rgb_gauss/output_test_gauss.csv", 'rb'),
open(test_dir + "/rgb_fovea/output_test_fovea.csv", 'rb')
]
experiments_filters['context-fusion mlp vs lstm'] = [
'RGB', 'Crop', 'Gauss', 'Fovea'
]
experiments_detections['context-fusion mlp vs lstm'] = [
open(test_dir + "/rgb_rgb/output_test_rgb.csv", 'rb'),
open(test_dir + "/rgb_crop/output_test_crop.csv", 'rb'),
open(test_dir + "/rgb_gauss/output_test_gauss.csv", 'rb'),
open(test_dir + "/rgb_fovea/output_test_fovea.csv", 'rb')
]
experiments_filters['balancing sampling'] = [
'RGB', 'Crop', 'Gauss', 'Fovea'
]
experiments_detections['balancing sampling'] = [
open(test_dir + "/rgb_rgb/output_test_rgb.csv", 'rb'),
open(test_dir + "/rgb_crop/output_test_crop.csv", 'rb'),
open(test_dir + "/rgb_gauss/output_test_gauss.csv", 'rb'),
open(test_dir + "/rgb_fovea/output_test_fovea.csv", 'rb')
]
experiments_filters['balancing weights'] = [
'RGB', 'Crop', 'Gauss', 'Fovea'
]
experiments_detections['balancing weights'] = [
open(test_dir + "/rgb_rgb/output_test_rgb.csv", 'rb'),
open(test_dir + "/rgb_crop/output_test_crop.csv", 'rb'),
open(test_dir + "/rgb_gauss/output_test_gauss.csv", 'rb'),
open(test_dir + "/rgb_fovea/output_test_fovea.csv", 'rb')
]
experiments_filters['balancing prior'] = ['RGB', 'Crop', 'Gauss', 'Fovea']
experiments_detections['balancing prior'] = [
open(test_dir + "/rgb_rgb/output_test_rgb.csv", 'rb'),
open(test_dir + "/rgb_crop/output_test_crop.csv", 'rb'),
open(test_dir + "/rgb_gauss/output_test_gauss.csv", 'rb'),
open(test_dir + "/rgb_fovea/output_test_fovea.csv", 'rb')
]
# experiment =
filters = []
# filters.append("pose")
# filters.append("rgb-base")
# filters.append("rgb-prior")
# filters.append("rgb-sampling")
# filters.append("rgb-weights")
# filters.append("rgb-kinetics")
# filters.append("flow-kinetics")
# filters.append("rgb")
# filters.append("crop")
# filters.append("gauss")
# filters.append("fovea")
# filters.append("flowcrop")
# filters.append("flow")
# filters.append("MLP")
#filters.append("best case scenario thresh 0.1")
#filters.append("two pass scenario thresh 0.1")
filters.append("fovea")
filters.append("dense-gt")
#filters.append("sampling no aug")
filters.append("dense-2pass")
#filters.append("weights no aug")
# filters.append("LSTM5-A-512")
# filters.append("random")
# filters.append("LSTM5-B-512")
# filters.append("LSTM10")
# filters.append("2st(rgb)")
# filters.append("2st(crop)")
# filters.append("2st(gauss)")
# filters.append("2st(fovea)")
#filters.append("2st(crop) + flowcrop")
#filters.append("2st(gauss) + flowcrop")
#filters.append("2st(fovea) + flowcrop")
#filters.append("2st(fovea) + mlp")
#filters.append("2st(crop) + mlp")
#filters.append("2st(gauss) + mlp")
# filters.append("2stream")
#filters.append("2stream + lstm (extra pass)")
# filters.append("gauss")
#filters.append("gauss aug")
#filters.append("LSTMA 512 5 2")
#filters.append("LSTMA 512 5 3")
#filters.append("LSTMA 512 5 3")
#filters.append("LSTMA 1024 5 3")
#filters.append("LSTMA 2048 5 3")
#filters.append("LSTMB 512 3 3")
#filters.append("LSTMB 1024 3 3")
#filters.append("LSTMB 2048 3 3")
# filters.append("2st(gauss) + lstm")
all_detections = []
#all_detections.append(open(test_dir + "context/lstmA/output_test_ctx_lstm_512_5_3.csv", 'rb'))
#all_detections.append(open(test_dir + "context/lstmA/output_test_ctx_lstm_1024_5_3.csv", 'rb'))
#all_detections.append(open(test_dir + "context/lstmA/output_test_ctx_lstm_2048_5_3.csv", 'rb'))
#all_detections.append(open(test_dir + "context/lstmB/output_test_ctx_lstm_512_3_3.csv", 'rb'))
#all_detections.append(open(test_dir + "context/lstmB/output_test_ctx_lstm_1024_3_3.csv", 'rb'))
#all_detections.append(open(test_dir + "context/lstmB/output_test_ctx_lstm_2048_3_3.csv", 'rb'))
# Pose
# all_detections.append(open(test_dir + "output_test_flowcrop.csv", 'rb'))
# Balancing
#all_detections.append(open(test_dir + "output_test_flowcrop.csv", 'rb'))
#all_detections.append(open(test_dir + "/rgb_gauss/output_test_gauss.csv", 'rb'))
#all_detections.append(open(test_dir + "/augmentation/predictions_rgb_gauss_1807241628_1000.csv", 'rb'))
#all_detections.append(open(test_dir + "/augmentation/output_test_sampling_gauss_1809221859.csv", 'rb'))
#all_detections.append(open(test_dir + "/augmentation/output_test_weights_gauss_1809221904.csv", 'rb'))
# RGB Streams
#all_detections.append(open(test_dir + "/kinetics_init/output_test_rgb_kineticsinit_gauss_1809220212.csv", 'rb'))
#all_detections.append(open(test_dir + "/kinetics_init/output_test_flow_kineticsinit_1809220244.csv", 'rb'))
# Flow Streams
# Context (LSTMs)
#filters.append("LSTMB 512 3 3")
#filters.append("LSTMB 512 3 2")
#filters.append("LSTMB 512 3 1")
#all_detections.append(open(test_dir + "context/lstmB/output_test_ctx_lstm_512_3_1.csv", 'rb'))
#all_detections.append(open(test_dir + "context/lstmB/output_test_ctx_lstm_512_3_2.csv", 'rb'))
#all_detections.append(open(test_dir + "context/lstmB/output_test_ctx_lstm_512_3_3.csv", 'rb'))
#all_detections.append(open(test_dir + "context/lstmA/output_test_ctx_lstm_512_5_1.csv", 'rb'))
#all_detections.append(open(test_dir + "context/lstmA/output_test_ctx_lstm_512_5_2.csv", 'rb'))
#all_detections.append(open(test_dir + "context/lstmA/output_test_ctx_lstm_512_5_3.csv", 'rb'))
#all_detections.append(open(test_dir + "context/lstmB/output_test_ctx_lstm_32_3_3.csv", 'rb'))
#all_detections.append(open(test_dir + "context/lstmB/output_test_ctx_lstm_32_5_3.csv", 'rb'))
#all_detections.append(open(test_dir + "context/lstmB/output_test_ctx_lstm_32_10_3.csv", 'rb'))
#all_detections.append(open(test_dir + "context/mlp/output_test_ctx.csv", 'rb'))
#all_detections.append(open(test_dir + "context/mlp/output_test_ctx_mlp_1809212356.csv", 'rb'))
#all_detections.append(open(test_dir + "context/lstmA/output_test_ctx_lstm_512_5_3_1809220010.csv", 'rb'))
#all_detections.append(open(test_dir + "random/output_test_random_1809221552.csv", 'rb'))
#all_detections.append(open(test_dir + "context/lstmB/output_test_ctx_lstm_512_5_3_1809211924.csv", 'rb'))
#all_detections.append(open(test_dir + "context/lstmB/output_test_ctx_lstm_128_10_3_1809211930.csv", 'rb'))
# 6 2-streams + baseline
#all_detections.append(open(test_dir + "/two-streams/output_test_2stream_rgb_1809220100.csv", 'rb'))
#all_detections.append(open(test_dir + "/two-streams/output_test_2stream_crop.csv", 'rb'))
#all_detections.append(open(test_dir + "/two-streams/output_test_2stream_gauss.csv", 'rb'))
all_detections.append(
open(test_dir + "/two-streams/output_test_2stream_fovea.csv", 'rb'))
#all_detections.append(open(test_dir + "/two-streams/output_test_2stream_flowcrop_crop_1809220117.csv", 'rb'))
#all_detections.append(open(test_dir + "/two-streams/output_test_2stream_flowcrop_gauss_1809220152.csv", 'rb'))
#all_detections.append(open(test_dir + "/two-streams/output_test_2stream_flowcrop_fovea_1809220136.csv", 'rb'))
# Context Fusions
# all_detections.append(open(test_dir + "/context_fusion/output_test_3stream_fovea.csv", 'rb'))
# all_detections.append(open(test_dir + "/context_fusion/output_test_3stream_crop.csv", 'rb'))
# all_detections.append(open(test_dir + "/context_fusion/output_test_3stream_gauss.csv", 'rb'))
all_detections.append(
open(
test_dir +
"/context_fusion/output_test_LSTM_FCfusion_contextGT_gauss_1810011737.csv",
'rb'))
all_detections.append(
open(
test_dir +
"/context_fusion/output_test_LSTM_FCfusion_context_secondpass_gauss_1810011754.csv",
'rb'))
# LSTMs
#all_detections.append(open(test_dir + "/two-streams/output_test_2stream_rgb_1809220100.csv", 'rb'))
#all_detections.append(open(test_dir + "/two-streams/output_test_2stream_crop.csv", 'rb'))
#all_detections.append(open(test_dir + "/two-streams/output_test_2stream_gauss.csv", 'rb'))
#all_detections.append(open(test_dir + "/two-streams/output_test_2stream_fovea.csv", 'rb'))
#all_detections.append(open(test_dir + "/context_fusion/output_test_ctx_lstm_fusion_thresh_512_5_3_1809242315.csv", 'rb'))
#all_detections.append(open(test_dir + "/context_fusion/output_test_ctx_lstm_512_5_3_1809242252.csv", 'rb'))
#all_detections.append(open(test_dir + "/context_fusion/output_test_ctx_lstmavggoodpedro_512_5_3_1809242338.csv", 'rb'))
#all_detections.append(open(test_dir + "/context_fusion/output_test_ctx_lstmavg_twophase_thresh02_512_5_3_1809281219.csv", 'rb'))
#all_detections.append(open(test_dir + "/context_fusion/output_test_ctx_lstmavg_threephase_512_5_3_1809281317.csv", 'rb'))
#all_detections.append(open(test_dir + "/context_fusion/output_test_ctx_lstm_fusion_thresh01_512_5_3_1809281400.csv", 'rb'))
#all_detections.append(open(test_dir + "/context_fusion/output_test_ctx_lstmavg_twophase_thresh01_512_5_3_1809281423.csv", 'rb'))
#all_detections.append(open(test_dir + "rgb_gauss/output_test_gauss.csv", 'rb'))
#all_detections.append(open(test_dir + "augmentation/output_test_sampling_gauss_1809221859.csv", 'rb'))
#all_detections.append(open(test_dir + "augmentation/output_test_samplingnoaug_gauss_1809281439.csv", 'rb'))
#all_detections.append(open(test_dir + "augmentation/output_test_weightsnew_gauss_1809291104.csv", 'rb'))
#all_detections.append(open(test_dir + "augmentation/output_test_weightsaug_gauss_1809261228.csv", 'rb'))
# output_test_ctx_lstm_512_5_3_1809242252.csv
#all_detections.append(open(test_dir + "/two-streams/output_test_2stream_flowcrop_crop_1809220117.csv", 'rb'))
#all_detections.append(open(test_dir + "/two-streams/output_test_2stream_flowcrop_gauss_1809220152.csv", 'rb'))
#all_detections.append(open(test_dir + "/two-streams/output_test_2stream_flowcrop_fovea_1809220136.csv", 'rb'))
# ---------------------------------
# New run to compare new flow
#all_detections.append(open(test_dir + "/flow/output_test_flowcrop.csv", 'rb'))
#all_detections.append(open(test_dir + "/flow/output_test_flow.csv", 'rb'))
# New 2 and 3 streams
# all_detections.append(open(test_dir + "output_test_gauss.csv", 'rb'))
# all_detections.append(open(test_dir + "output_test_gauss_extra.csv", 'rb'))
# all_detections.append(open(test_dir + "output_test_3stream_gauss.csv", 'rb'))
# all_detections.append(open(test_dir + "output_test_3stream_crop.csv", 'rb'))
# Flow, context, 2-stream, 3-stream run
#all_detections.append(open(test_dir + "output_test_ctx.csv", 'rb'))
#all_detections.append(open(test_dir + "output_test_flow.csv", 'rb'))
#all_detections.append(open(test_dir + "output_test_2stream.csv", 'rb'))
#all_detections.append(open(test_dir + "output_test_3stream.csv", 'rb'))
# RGB run
# all_detections.append(open(test_dir + "/rgb_rgb/output_test_rgb.csv", 'rb'))
# all_detections.append(open(test_dir + "/rgb_crop/output_test_crop.csv", 'rb'))
# all_detections.append(open(test_dir + "/rgb_gauss/output_test_gauss.csv", 'rb'))
# all_detections.append(open(test_dir + "/rgb_fovea/output_test_fovea.csv", 'rb'))
balancing = False
all_gndtruths = []
for i in range(len(all_detections)):
if balancing is False:
all_gndtruths.append(
open(root_dir + "AVA_Test_Custom_Corrected.csv", 'rb'))
else:
all_gndtruths.append(
open(root_dir + "AVA_Test_Custom_Corrected_Balanced.csv",
'rb'))
"""Runs evaluations given input files.
Args:
labelmap: file object containing map of labels to consider, in pbtxt format
groundtruth: file object
detections: file object
exclusions: file object or None.
"""
categories, class_whitelist = read_labelmap(labelmap)
logging.info("CATEGORIES (%d):\n%s", len(categories),
pprint.pformat(categories, indent=2))
excluded_keys = read_exclusions(exclusions)
# Reads detections data.
x_axis = []
xpose_ax = []
xobj_ax = []
xhuman_ax = []
ypose_ax = []
yobj_ax = []
yhuman_ax = []
colors_pose = []
colors_obj = []
colors_human = []
finalmAPs = []
colors = []
maxY = -1.0
for detections, gndtruth, filter_type in zip(all_detections, all_gndtruths,
filters):
pascal_evaluator = None
metrics = None
actions = None
start = 0
pascal_evaluator = object_detection_evaluation.PascalDetectionEvaluator(
categories, matching_iou_threshold=iou)
# Reads the ground truth data.
boxes, labels, _ = read_csv(gndtruth, class_whitelist)
start = time.time()
for image_key in boxes:
if image_key in excluded_keys:
logging.info(("Found excluded timestamp in ground truth: %s. "
"It will be ignored."), image_key)
continue
pascal_evaluator.add_single_ground_truth_image_info(
image_key, {
standard_fields.InputDataFields.groundtruth_boxes:
np.array(boxes[image_key], dtype=float),
standard_fields.InputDataFields.groundtruth_classes:
np.array(labels[image_key], dtype=int),
standard_fields.InputDataFields.groundtruth_difficult:
np.zeros(len(boxes[image_key]), dtype=bool)
})
print_time("convert groundtruth", start)
# Run evaluation
boxes, labels, scores = read_csv(detections, class_whitelist)
start = time.time()
for image_key in boxes:
if image_key in excluded_keys:
logging.info(("Found excluded timestamp in detections: %s. "
"It will be ignored."), image_key)
continue
pascal_evaluator.add_single_detected_image_info(
image_key, {
standard_fields.DetectionResultFields.detection_boxes:
np.array(boxes[image_key], dtype=float),
standard_fields.DetectionResultFields.detection_classes:
np.array(labels[image_key], dtype=int),
standard_fields.DetectionResultFields.detection_scores:
np.array(scores[image_key], dtype=float)
})
print_time("convert detections", start)
start = time.time()
metrics = pascal_evaluator.evaluate()
print_time("run_evaluator", start)
# TODO Show a pretty histogram here besides pprint
actions = list(metrics.keys())
final_value = 0.0
for m in actions:
ms = m.split("/")[-1]
if ms == 'mAP@' + str(iou) + 'IOU':
final_value = metrics[m]
finalmAPs.append(final_value)
else:
# x_axis.append(ms)
# y_axis.append(metrics[m])
for cat in categories:
if cat['name'].split("/")[-1] == ms:
if maxY < metrics[m]:
maxY = metrics[m]
if cat['id'] <= 10:
xpose_ax.append("[" + filter_type + "] " + ms)
ypose_ax.append(metrics[m])
colors_pose.append('pose')
elif cat['id'] <= 22:
xobj_ax.append("[" + filter_type + "] " + ms)
yobj_ax.append(metrics[m])
colors_obj.append('human-object')
else:
xhuman_ax.append("[" + filter_type + "] " + ms)
yhuman_ax.append(metrics[m])
colors_human.append('human-human')
# Make a confusion matrix for this run
pascal_evaluator = None
x_axis = split_interleave(xpose_ax) + split_interleave(
xobj_ax) + split_interleave(xhuman_ax)
y_axis = split_interleave(ypose_ax) + split_interleave(
yobj_ax) + split_interleave(yhuman_ax)
colors = split_interleave(colors_pose) + split_interleave(
colors_obj) + split_interleave(colors_human)
plt.ylabel('frame-mAP')
top = maxY + 0.1 # offset a bit so it looks good
sns.set_style("whitegrid")
g = sns.barplot(y_axis,
x_axis,
hue=colors,
palette=['red', 'blue', 'green'])
ax = g
#ax.legend(loc='lower right')
# annotate axis = seaborn axis
# for p in ax.patches:
# ax.annotate("%.3f" % p.get_height(), (p.get_x() + p.get_width() / 2., p.get_height()),
# ha='center', va='center', fontsize=10, color='gray', rotation=90, xytext=(0, 20),
# textcoords='offset points')
# ax.set_ylim(-1, len(y_axis))
sns.set()
ax.tick_params(labelsize=6)
for p in ax.patches:
p.set_height(p.get_height() * 3)
ax.annotate("%.3f" % p.get_width(),
(p.get_x() + p.get_width(), p.get_y()),
xytext=(5, -5),
fontsize=8,
color='gray',
textcoords='offset points')
_ = g.set_xlim(0, top) # To make space for the annotations
pprint.pprint(metrics, indent=2)
ax.set(ylabel="", xlabel="AP")
plt.xticks(rotation=0)
title = ""
file = open("results.txt", "w")
for filter_type, mAP in zip(filters, finalmAPs):
ft = filter_type + ': mAP@' + str(iou) + 'IOU = ' + str(mAP) + '\n'
title += ft
file.write(ft)
file.close()
# ax.figure.tight_layout()
ax.figure.subplots_adjust(left=0.2) # change 0.3 to suit your needs.
plt.title(title)
plt.gca().xaxis.grid(True)
plt.show()
if len(all_detections) == 1:
sz = 2
grid_sz = [1, 1]
elif len(all_detections) == 2:
sz = 3
grid_sz = [1, 2]
elif len(all_detections) == 3:
sz = 4
grid_sz = [2, 2]
else:
sz = 5
grid_sz = [2, 2]
for i in range(1, sz):
print(i)
plt.subplot(grid_sz[0], grid_sz[1], i)
if i <= len(all_detections):
# Confusion matrix
classes = []
for k in categories:
classes.append(k['name'])
cm = confusion_matrix(all_gndtruths[i - 1], all_detections[i - 1],
x_axis)
g = sns.heatmap(cm,
annot=True,
fmt="d",
xticklabels=classes[:10],
yticklabels=classes[:10],
linewidths=0.5,
linecolor='black',
cbar=True)
#t = 0
# for ytick_label, xtick_label in zip(g.axes.get_yticklabels(), g.axes.get_xticklabels()):
# if t <= 9:
# ytick_label.set_color("r")
# xtick_label.set_color("r")
# elif t <= 22:
# ytick_label.set_color("b")
# xtick_label.set_color("b")
# else:
# ytick_label.set_color("g")
# xtick_label.set_color("g")
# t += 1
plt.xticks(rotation=-90)
plt.title("Pose Confusion Matrix (" + filters[i - 1] + ")")
plt.show()
def run_evaluation_threshold(labelmap, groundtruth, exclusions, iou):
# sns.palplot(sns.diverging_palette(128, 240, n=10))
# seq_col_brew = sns.color_palette("Blues_r", 4) # For sequential, blue gradient in reverse
# Qualitative data palette
# current_palette = sns.color_palette("Paired")
# sns.set_palette(current_palette)
# Make sure not to mess this up
filters = []
filters.append("0.1")
filters.append("0.2")
filters.append("0.3")
filters.append("0.4")
filters.append("0.5")
filters.append("0.6")
filters.append("0.7")
filters.append("0.8")
filters.append("0.9")
root_dir = '../../../data/AVA/files/'
ftype = "fusion"
all_detections = []
ts = "1809281055"
for f in filters:
all_detections.append(
open(
"../thresholds/context_" + ftype +
"/predictions_fusion_avg_fovea_" + ts + "_" + f + ".csv",
'rb'))
all_gndtruths = []
for i in range(len(filters)):
all_gndtruths.append(
open(root_dir + "AVA_Val_Custom_Corrected.csv", 'rb'))
#all_gndtruths.append(open("AVA_Test_Custom_Corrected.csv", 'rb'))
#all_gndtruths.append(open("AVA_Test_Custom_Corrected.csv", 'rb'))
"""Runs evaluations given input files.
Args:
labelmap: file object containing map of labels to consider, in pbtxt format
groundtruth: file object
detections: file object
exclusions: file object or None.
"""
categories, class_whitelist = read_labelmap(labelmap)
logging.info("CATEGORIES (%d):\n%s", len(categories),
pprint.pformat(categories, indent=2))
excluded_keys = read_exclusions(exclusions)
# Reads detections data.
x_axis = []
xpose_ax = []
xobj_ax = []
xhuman_ax = []
ypose_ax = []
yobj_ax = []
yhuman_ax = []
colors_pose = []
colors_obj = []
colors_human = []
finalmAPs = []
colors = []
maxY = -1.0
for detections, gndtruth, filter_type in zip(all_detections, all_gndtruths,
filters):
pascal_evaluator = None
metrics = None
actions = None
start = 0
pascal_evaluator = object_detection_evaluation.PascalDetectionEvaluator(
categories, matching_iou_threshold=iou)
# Reads the ground truth data.
boxes, labels, _ = read_csv(gndtruth, class_whitelist)
start = time.time()
for image_key in boxes:
if image_key in excluded_keys:
logging.info(("Found excluded timestamp in ground truth: %s. "
"It will be ignored."), image_key)
continue
pascal_evaluator.add_single_ground_truth_image_info(
image_key, {
standard_fields.InputDataFields.groundtruth_boxes:
np.array(boxes[image_key], dtype=float),
standard_fields.InputDataFields.groundtruth_classes:
np.array(labels[image_key], dtype=int),
standard_fields.InputDataFields.groundtruth_difficult:
np.zeros(len(boxes[image_key]), dtype=bool)
})
print_time("convert groundtruth", start)
# Run evaluation
boxes, labels, scores = read_csv(detections, class_whitelist)
start = time.time()
for image_key in boxes:
if image_key in excluded_keys:
logging.info(("Found excluded timestamp in detections: %s. "
"It will be ignored."), image_key)
continue
pascal_evaluator.add_single_detected_image_info(
image_key, {
standard_fields.DetectionResultFields.detection_boxes:
np.array(boxes[image_key], dtype=float),
standard_fields.DetectionResultFields.detection_classes:
np.array(labels[image_key], dtype=int),
standard_fields.DetectionResultFields.detection_scores:
np.array(scores[image_key], dtype=float)
})
print_time("convert detections", start)
start = time.time()
metrics = pascal_evaluator.evaluate()
print_time("run_evaluator", start)
# TODO Show a pretty histogram here besides pprint
actions = list(metrics.keys())
final_value = 0.0
for m in actions:
ms = m.split("/")[-1]
if ms == 'mAP@' + str(iou) + 'IOU':
final_value = metrics[m]
finalmAPs.append(final_value)
else:
# x_axis.append(ms)
# y_axis.append(metrics[m])
for cat in categories:
if cat['name'].split("/")[-1] == ms:
if maxY < metrics[m]:
maxY = metrics[m]
if cat['id'] <= 10:
xpose_ax.append("(" + filter_type + ") " + ms)
ypose_ax.append(metrics[m])
colors_pose.append('red')
elif cat['id'] <= 22:
xobj_ax.append("(" + filter_type + ") " + ms)
yobj_ax.append(metrics[m])
colors_obj.append('blue')
else:
xhuman_ax.append("(" + filter_type + ") " + ms)
yhuman_ax.append(metrics[m])
colors_human.append('green')
# Make a confusion matrix for this run
pascal_evaluator = None
x_axis = split_interleave(xpose_ax) + split_interleave(
xobj_ax) + split_interleave(xhuman_ax)
y_axis = split_interleave(ypose_ax) + split_interleave(
yobj_ax) + split_interleave(yhuman_ax)
colors = split_interleave(colors_pose) + split_interleave(
colors_obj) + split_interleave(colors_human)
print(filters)
print(finalmAPs)
plt.ylabel('frame-mAP')
top = 0.1 # offset a bit so it looks good
sns.set_style("whitegrid")
clrs = ['blue' if (x < max(finalmAPs)) else 'red' for x in finalmAPs]
g = sns.barplot(filters, finalmAPs, palette=clrs)
ax = g
# annotate axis = seaborn axis
for p in ax.patches:
ax.annotate("%.4f" % p.get_height(),
(p.get_x() + p.get_width() / 2., p.get_height()),
ha='center',
va='center',
fontsize=10,
color='gray',
rotation=90,
xytext=(0, 20),
textcoords='offset points')
_ = g.set_ylim(0, top) # To make space for the annotations
plt.show()
def run_evaluation(labelmap, groundtruth, exclusions, iou):
root_dir = '../../../data/AVA/files/'
test_dir = "../test_outputs/"
# Make sure not to mess this up
experiments_filters = {}
experiments_detections = {}
experiment = 'balancing'
# Baseline
experiments_filters['baseline'] = ['RGB', 'Flow', 'RGB+Flow']
experiments_detections['baseline'] = [
open(test_dir + "/rgb_rgb/output_test_rgb.csv", 'rb'),
open(test_dir + "/flow/output_test_flow.csv", 'rb'),
open(test_dir + "/two-streams/output_test_2stream_rgb_1809220100.csv",
'rb')
]
# RGBS
experiments_filters['rgb-streams-aug'] = ['Crop', 'Gauss', 'Fovea']
experiments_detections['rgb-streams-aug'] = [
open(test_dir + "/rgb_crop/output_test_crop.csv", 'rb'),
open(test_dir + "/rgb_gauss/output_test_gauss.csv", 'rb'),
open(test_dir + "/rgb_fovea/output_test_fovea.csv", 'rb')
]
# Flows
experiments_filters['flow vs flowcrop'] = ['Flow', 'Flowcrop']
experiments_detections['flow vs flowcrop'] = [
open(test_dir + "/flow/output_test_flow.csv", 'rb'),
open(test_dir + "/flow/output_test_flowcrop.csv", 'rb'),
]
# Two-streams
experiments_filters['two-streams'] = [
'Two-Stream-Crop', 'Two-Stream-GBB', 'Two-Stream-Fovea'
]
experiments_detections['two-streams'] = [
open(test_dir + "/two-streams/output_test_2stream_crop_1807252254.csv",
'rb'),
open(
test_dir + "/two-streams/output_test_2stream_gauss_1807252309.csv",
'rb'),
open(test_dir + "/two-streams/output_test_2stream_fovea.csv", 'rb')
]
#experiments_filters['two-streams'] = ['Two-Stream-Crop', 'Two-Stream-Fovea']
#experiments_detections['two-streams'] = [open(test_dir + "/two-streams/output_test_2stream_crop_1807252254.csv", 'rb'), open(test_dir + "/two-streams/output_test_2stream_fovea.csv", 'rb')]
experiments_filters['two-streams-flowcrop'] = [
'Two-Stream-Crop (Flowcrop)', 'Two-Stream-Gauss (Flowcrop)',
'Two-Stream-Fovea (Flowcrop)'
]
experiments_detections['two-streams-flowcrop'] = [
open(
test_dir +
"/two-streams/output_test_2stream_flowcrop_crop_1809220117.csv",
'rb'),
open(
test_dir +
"/two-streams/output_test_2stream_flowcrop_gauss_1809220152.csv",
'rb'),
open(
test_dir +
"/two-streams/output_test_2stream_flowcrop_fovea_1809220136.csv",
'rb')
]
# MLP VS LSTM
experiments_filters['mlp vs lstm'] = ['MLP', 'LSTMA', 'LSTMB']
experiments_detections['mlp vs lstm'] = [
open(test_dir + "context/mlp/output_test_ctx_mlp_1809212356.csv",
'rb'),
open(test_dir + "context/lstmA/output_test_ctx_lstm_128_3_3.csv",
'rb'),
open(test_dir + "context/lstmB/output_test_ctx_lstm_128_3_3.csv", 'rb')
]
# LSTMS
nhu = 512
neighbs = 3
tws = 3
experiments_filters['lstmA vs lstmB'] = ['LSTM A', 'LSTM B']
experiments_detections['lstmA vs lstmB'] = [
open(
test_dir + "context/lstmA/output_test_ctx_lstm_" + str(nhu) + "_" +
str(tws) + "_" + str(neighbs) + ".csv", 'rb'),
open(
test_dir + "context/lstmB/output_test_ctx_lstm_" + str(nhu) + "_" +
str(tws) + "_" + str(neighbs) + ".csv", 'rb')
]
# Fusions
experiments_filters['class-score-fusion-gt'] = [
'2-stream Fovea', 'Class Score Fusion (GT)'
]
experiments_detections['class-score-fusion-gt'] = [
open(test_dir + "/two-streams/output_test_2stream_fovea.csv", 'rb'),
open(
test_dir +
"/context_fusion/output_test_ctx_lstm_fusion_512_5_3_1809242338.csv",
'rb')
]
experiments_filters['class-score-fusion-two-pass'] = [
'2-stream Fovea', 'Class Score Fusion (Two Pass)'
]
experiments_detections['class-score-fusion-two-pass'] = [
open(test_dir + "/two-streams/output_test_2stream_fovea.csv", 'rb'),
open(
test_dir +
"/context_fusion/output_test_ctx_lstmavg_twophase_512_5_3_1809281149.csv",
'rb')
]
experiments_filters['dense-fusion-gt'] = ['2-stream Fovea', 'Dense Fusion']
experiments_detections['dense-fusion-gt'] = [
open(test_dir + "/two-streams/output_test_2stream_fovea.csv", 'rb'),
open(
test_dir +
"/context_fusion/output_test_LSTM_FCfusion_contextGT_fovea_1810011737.csv",
'rb')
]
experiments_filters['dense-fusion-two-pass'] = [
'2-stream Fovea', 'Dense Fusion (Two Pass)'
]
experiments_detections['dense-fusion-two-pass'] = [
open(test_dir + "/two-streams/output_test_2stream_fovea.csv", 'rb'),
open(
test_dir +
"/context_fusion/output_test_LSTM_FCfusion_context_secondpass_fovea_1810011754.csv",
'rb')
]
# Voting
experiments_filters['class-score-fusion-gt-voting'] = [
'Class Score Fusion (0.2)', 'Class Score Fusion (0.1)'
]
experiments_detections['class-score-fusion-gt-voting'] = [
open(
test_dir +
"/context_fusion/output_test_ctx_lstm_fusion_thresh02_512_5_3_1809242315.csv",
'rb'),
open(
test_dir +
"/context_fusion/output_test_ctx_lstm_fusion_thresh01_512_5_3_1809281400.csv",
'rb')
]
experiments_filters['class-score-fusion-two-pass-voting'] = [
'Class Score Fusion (Two-pass) (0.2)',
'Class Score Fusion (Two-pass) (0.1)'
]
experiments_detections['class-score-fusion-two-pass-voting'] = [
open(
test_dir +
"/context_fusion/output_test_ctx_lstmavg_twophase_thresh02_512_5_3_1809281219.csv",
'rb'),
open(
test_dir +
"/context_fusion/output_test_ctx_lstmavg_twophase_thresh01_512_5_3_1809281423.csv",
'rb')
]
# Balancing
experiments_filters['balancing'] = ['Imbalanced', 'Oversampling']
experiments_detections['balancing'] = [
open(test_dir + "rgb_gauss/output_test_gauss.csv", 'rb'),
open(
test_dir +
"augmentation/output_test_samplingnoaug_gauss_1809281439.csv",
'rb')
]
# Extra experiments
experiments_filters['context-fusion mlp'] = [
'2-stream Fovea', 'Dense Fusion MLP'
]
experiments_detections['context-fusion mlp'] = [
open(test_dir + "/two-streams/output_test_2stream_fovea.csv", 'rb'),
open(test_dir + "/context_fusion/output_test_3stream_fovea.csv", 'rb')
]
experiments_filters['context-fusion extra pass'] = [
'Class Score (Two Pass)', 'Class Score (Extra Pass)'
]
experiments_detections['context-fusion extra pass'] = [
open(
test_dir +
"/context_fusion/output_test_ctx_lstmavg_twophase_512_5_3_1809281149.csv",
'rb'),
open(
test_dir +
"/context_fusion/output_test_ctx_lstmavg_threephase_512_5_3_1809281317.csv",
'rb')
]
experiments_filters['random'] = ['Random guessing']
experiments_detections['random'] = [
open(test_dir + "random/output_test_random_1809221552.csv", 'rb')
]
# Best
experiments_filters['best'] = [
'2-stream Fovea', 'Class Score Fusion (GT)',
'Class Score Fusion (GT, v=0.1)'
]
experiments_detections['best'] = [
open(test_dir + "/two-streams/output_test_2stream_fovea.csv", 'rb'),
open(
test_dir +
"/context_fusion/output_test_ctx_lstm_fusion_512_5_3_1809242338.csv",
'rb'),
open(
test_dir +
"/context_fusion/output_test_ctx_lstm_fusion_thresh01_512_5_3_1809281400.csv",
'rb')
]
filters = experiments_filters[experiment]
all_detections = experiments_detections[experiment]
balancing = False
all_gndtruths = []
for i in range(len(all_detections)):
if balancing is False:
all_gndtruths.append(
open(root_dir + "AVA_Test_Custom_Corrected.csv", 'rb'))
else:
all_gndtruths.append(
open(root_dir + "AVA_Test_Custom_Corrected_Balanced.csv",
'rb'))
"""Runs evaluations given input files.
Args:
labelmap: file object containing map of labels to consider, in pbtxt format
groundtruth: file object
detections: file object
exclusions: file object or None.
"""
categories, class_whitelist = read_labelmap(labelmap)
logging.info("CATEGORIES (%d):\n%s", len(categories),
pprint.pformat(categories, indent=2))
excluded_keys = read_exclusions(exclusions)
# Reads detections data.
x_axis = []
xpose_ax = []
xobj_ax = []
xhuman_ax = []
ypose_ax = []
yobj_ax = []
yhuman_ax = []
colors_pose = []
colors_obj = []
colors_human = []
finalmAPs = []
colors = []
maxY = -1.0
for detections, gndtruth, filter_type in zip(all_detections, all_gndtruths,
filters):
pascal_evaluator = None
metrics = None
actions = None
start = 0
pascal_evaluator = object_detection_evaluation.PascalDetectionEvaluator(
categories, matching_iou_threshold=iou)
# Reads the ground truth data.
boxes, labels, _ = read_csv(gndtruth, class_whitelist)
start = time.time()
for image_key in boxes:
if image_key in excluded_keys:
logging.info(("Found excluded timestamp in ground truth: %s. "
"It will be ignored."), image_key)
continue
pascal_evaluator.add_single_ground_truth_image_info(
image_key, {
standard_fields.InputDataFields.groundtruth_boxes:
np.array(boxes[image_key], dtype=float),
standard_fields.InputDataFields.groundtruth_classes:
np.array(labels[image_key], dtype=int),
standard_fields.InputDataFields.groundtruth_difficult:
np.zeros(len(boxes[image_key]), dtype=bool)
})
print_time("convert groundtruth", start)
# Run evaluation
boxes, labels, scores = read_csv(detections, class_whitelist)
start = time.time()
for image_key in boxes:
if image_key in excluded_keys:
logging.info(("Found excluded timestamp in detections: %s. "
"It will be ignored."), image_key)
continue
pascal_evaluator.add_single_detected_image_info(
image_key, {
standard_fields.DetectionResultFields.detection_boxes:
np.array(boxes[image_key], dtype=float),
standard_fields.DetectionResultFields.detection_classes:
np.array(labels[image_key], dtype=int),
standard_fields.DetectionResultFields.detection_scores:
np.array(scores[image_key], dtype=float)
})
print_time("convert detections", start)
start = time.time()
metrics = pascal_evaluator.evaluate()
print_time("run_evaluator", start)
# TODO Show a pretty histogram here besides pprint
actions = list(metrics.keys())
final_value = 0.0
for m in actions:
ms = m.split("/")[-1]
if ms == 'mAP@' + str(iou) + 'IOU':
final_value = metrics[m]
finalmAPs.append(final_value)
else:
# x_axis.append(ms)
# y_axis.append(metrics[m])
for cat in categories:
if cat['name'].split("/")[-1] == ms:
if maxY < metrics[m]:
maxY = metrics[m]
if cat['id'] <= 10:
xpose_ax.append("[" + filter_type + "] " + ms)
ypose_ax.append(metrics[m])
colors_pose.append('pose')
elif cat['id'] <= 22:
xobj_ax.append("[" + filter_type + "] " + ms)
yobj_ax.append(metrics[m])
colors_obj.append('human-object')
else:
xhuman_ax.append("[" + filter_type + "] " + ms)
yhuman_ax.append(metrics[m])
colors_human.append('human-human')
# Make a confusion matrix for this run
pascal_evaluator = None
parts = len(filters)
x_axis = split_interleave(xpose_ax, parts) + split_interleave(
xobj_ax, parts) + split_interleave(xhuman_ax, parts)
y_axis = split_interleave(ypose_ax, parts) + split_interleave(
yobj_ax, parts) + split_interleave(yhuman_ax, parts)
colors = split_interleave(colors_pose, parts) + split_interleave(
colors_obj, parts) + split_interleave(colors_human, parts)
plt.ylabel('frame-mAP')
top = maxY + 0.1 # offset a bit so it looks good
sns.set_style("whitegrid")
g = sns.barplot(y_axis,
x_axis,
hue=colors,
palette=['red', 'blue', 'green'])
ax = g
# ax.legend(loc='lower right')
# annotate axis = seaborn axis
# for p in ax.patches:
# ax.annotate("%.3f" % p.get_height(), (p.get_x() + p.get_width() / 2., p.get_height()),
# ha='center', va='center', fontsize=10, color='gray', rotation=90, xytext=(0, 20),
# textcoords='offset points')
# ax.set_ylim(-1, len(y_axis))
sns.set()
ax.tick_params(labelsize=6)
for p in ax.patches:
p.set_height(p.get_height() * 3)
ax.annotate("%.3f" % p.get_width(),
(p.get_x() + p.get_width(), p.get_y()),
xytext=(5, -5),
fontsize=8,
color='gray',
textcoords='offset points')
_ = g.set_xlim(0, top) # To make space for the annotations
pprint.pprint(metrics, indent=2)
ax.set(ylabel="", xlabel="AP")
plt.xticks(rotation=0)
title = ""
file = open("results.txt", "w")
for filter_type, mAP in zip(filters, finalmAPs):
ft = filter_type + ': mAP@' + str(iou) + 'IOU = ' + str(mAP) + '\n'
title += ft
file.write(ft)
file.close()
# ax.figure.tight_layout()
ax.figure.subplots_adjust(left=0.2) # change 0.3 to suit your needs.
plt.title(title)
plt.gca().xaxis.grid(True)
plt.show()
if len(all_detections) == 1:
sz = 2
grid_sz = [1, 1]
elif len(all_detections) == 2:
sz = 3
grid_sz = [1, 2]
elif len(all_detections) == 3:
sz = 4
grid_sz = [2, 2]
else:
sz = 5
grid_sz = [2, 2]
for i in range(1, sz):
print(i)
plt.subplot(grid_sz[0], grid_sz[1], i)
if i <= len(all_detections):
# Confusion matrix
classes = []
for k in categories:
classes.append(k['name'])
cm = confusion_matrix(all_gndtruths[i - 1], all_detections[i - 1],
x_axis)
g = sns.heatmap(cm,
annot=True,
fmt="d",
xticklabels=classes[:10],
yticklabels=classes[:10],
linewidths=0.5,
linecolor='black',
cbar=True,
vmin=0,
vmax=2000)
# t = 0
# for ytick_label, xtick_label in zip(g.axes.get_yticklabels(), g.axes.get_xticklabels()):
# if t <= 9:
# ytick_label.set_color("r")
# xtick_label.set_color("r")
# elif t <= 22:
# ytick_label.set_color("b")
# xtick_label.set_color("b")
# else:
# ytick_label.set_color("g")
# xtick_label.set_color("g")
# t += 1
plt.xticks(rotation=-90)
plt.title("Pose Confusion Matrix (" + filters[i - 1] + ")")
plt.show()
