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jsonformat_std_to_posetrack18.py
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jsonformat_std_to_posetrack18.py
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'''
Author: Guanghan Ning
E-mail: [email protected]
July 2nd, 2018
'''
import sys, os
sys.path.append(os.path.abspath("utils/"))
from utils_json import *
from utils_io_folder import *
import argparse
dataset_splits = ['whole', 'val', 'test']
dataset_split = "val"
input_keypoints_format = "PoseTrack"
PoseTrack_data = {"annolist":
[{"image": [{"name": "/export/guanghan/Data/posetrack_data/images/bonn_5sec/020910_mpii/00000001.jpg"}],
"annorect": [
{"y2": [820], "annopoints": [{"point": [{"y": [480.276], "x": [1309.639], "score": [1.0], "id": [0]}, {"y": [471.052], "x": [1308.319], "score": [1.0], "id": [1]}, {"y": [472.37], "x": [1309.639], "score": [1.0], "id": [2]}, {"y": [456.557], "x": [1267.417], "score": [1.0], "id": [3]}, {"y": [476.323], "x": [1300.403], "score": [1.0], "id": [4]}, {"y": [480.276], "x": [1225.194], "score": [1.0], "id": [5]}, {"y": [564.609], "x": [1299.083], "score": [1.0], "id": [6]}, {"y": [550.115], "x": [1126.236], "score": [1.0], "id": [7]}, {"y": [575.151], "x": [1151.306], "score": [1.0], "id": [8]}, {"y": [602.823], "x": [1229.153], "score": [1.0], "id": [9]}, {"y": [676.615], "x": [1226.514], "score": [1.0], "id": [10]}, {"y": [706.922], "x": [1188.25], "score": [1.0], "id": [11]}, {"y": [592.281], "x": [1292.486], "score": [1.0], "id": [12]}, {"y": [577.1275], "x": [1203.4235], "score": [1.0], "id": [13]}, {"y": [561.974], "x": [1114.361], "score": [1.0], "id": [14]}]}], "track_id": [0], "y1": [423], "score": [0.9997325539588928], "x2": [1329], "x1": [1094]},
{"y2": [940], "annopoints": [{"point": [{"y": [599.656], "x": [1084.479], "score": [1.0], "id": [0]}, {"y": [589.703], "x": [1085.903], "score": [1.0], "id": [1]}, {"y": [589.703], "x": [1085.903], "score": [1.0], "id": [2]}, {"y": [569.797], "x": [1034.653], "score": [1.0], "id": [3]}, {"y": [593.969], "x": [1078.785], "score": [1.0], "id": [4]}, {"y": [599.656], "x": [999.062], "score": [1.0], "id": [5]}, {"y": [770.281], "x": [1041.771], "score": [1.0], "id": [6]}, {"y": [714.828], "x": [892.292], "score": [1.0], "id": [7]}, {"y": [724.781], "x": [936.424], "score": [1.0], "id": [8]}, {"y": [815.781], "x": [896.562], "score": [1.0], "id": [9]}, {"y": [800.141], "x": [1028.958], "score": [1.0], "id": [10]}, {"y": [844.219], "x": [822.535], "score": [1.0], "id": [11]}, {"y": [719.094], "x": [1036.076], "score": [1.0], "id": [12]}, {"y": [726.914], "x": [1018.281], "score": [1.0], "id": [13]}, {"y": [734.734], "x": [1000.486], "score": [1.0], "id": [14]}]}], "track_id": [1], "y1": [536], "score": [0.9994370341300964], "x2": [1112], "x1": [796]},
{"y2": [742], "annopoints": [{"point": [{"y": [397.156], "x": [848.719], "score": [1.0], "id": [0]}, {"y": [389.474], "x": [850.257], "score": [1.0], "id": [1]}, {"y": [391.01], "x": [848.719], "score": [1.0], "id": [2]}, {"y": [371.036], "x": [807.188], "score": [1.0], "id": [3]}, {"y": [397.156], "x": [836.413], "score": [1.0], "id": [4]}, {"y": [386.401], "x": [757.965], "score": [1.0], "id": [5]}, {"y": [470.906], "x": [842.566], "score": [1.0], "id": [6]}, {"y": [484.734], "x": [677.979], "score": [1.0], "id": [7]}, {"y": [507.781], "x": [691.823], "score": [1.0], "id": [8]}, {"y": [526.219], "x": [762.58], "score": [1.0], "id": [9]}, {"y": [604.578], "x": [751.812], "score": [1.0], "id": [10]}, {"y": [619.943], "x": [739.507], "score": [1.0], "id": [11]}, {"y": [501.635], "x": [824.108], "score": [1.0], "id": [12]}, {"y": [451.70050000000003], "x": [729.509], "score": [1.0], "id": [13]}, {"y": [401.766], "x": [634.91], "score": [1.0], "id": [14]}]}], "track_id": [2], "y1": [337], "score": [0.9968172311782837], "x2": [871], "x1": [613]},
{"y2": [601], "annopoints": [{"point": [{"y": [258.724], "x": [975.375], "score": [1.0], "id": [0]}, {"y": [252.409], "x": [976.639], "score": [1.0], "id": [1]}, {"y": [252.409], "x": [976.639], "score": [1.0], "id": [2]}, {"y": [238.516], "x": [939.986], "score": [1.0], "id": [3]}, {"y": [252.409], "x": [961.472], "score": [1.0], "id": [4]}, {"y": [253.672], "x": [886.903], "score": [1.0], "id": [5]}, {"y": [369.87], "x": [970.319], "score": [1.0], "id": [6]}, {"y": [344.609], "x": [818.653], "score": [1.0], "id": [7]}, {"y": [364.818], "x": [864.153], "score": [1.0], "id": [8]}, {"y": [429.232], "x": [880.583], "score": [1.0], "id": [9]}, {"y": [460.807], "x": [917.236], "score": [1.0], "id": [10]}, {"y": [511.328], "x": [866.681], "score": [1.0], "id": [11]}, {"y": [368.607], "x": [946.306], "score": [1.0], "id": [12]}, {"y": [345.8725], "x": [870.4725000000001], "score": [1.0], "id": [13]}, {"y": [323.138], "x": [794.639], "score": [1.0], "id": [14]}]}], "track_id": [3], "y1": [205], "score": [0.980134904384613], "x2": [994], "x1": [776]} ]
}]
}
def standard_to_PoseTrack_18(standard_keypoints_ret, gt_python_data, mode_track = True, bbox_thresh = 0):
PoseTrack_dict = {"images": [],
"annotations": [],
"categories": [{}]}
PoseTrack_dict["categories"][0]["name"] = "person"
PoseTrack_dict["categories"][0]["keypoints"] = ['right_ankle', 'right_knee', 'right_hip',
'left_hip', 'left_knee', 'left_ankle',
'right_wrist', 'right_elbow', 'right_shoulder',
'left_shoulder', 'left_elbow', 'left_wrist',
'head_bottom', 'nose', 'head_top'] #PoseTrack2017 pose order
PoseTrack_images_info_list = []
PoseTrack_annotations_info_list = []
for standard_data_item in standard_keypoints_ret:
image_name = standard_data_item["image"]["name"]
folder_name = os.path.basename(standard_data_item["image"]["folder"])
_, parent_folder_name = get_parent_folder_from_path(standard_data_item["image"]["folder"])
img_path = os.path.join("images", parent_folder_name, folder_name, image_name)
print(img_path)
gt_images_info = gt_python_data["images"]
frame_id = find_id_from_annotation_by_name(gt_images_info, img_path)
PoseTrack_images_info_item = {"file_name": img_path,
"id": frame_id}
PoseTrack_images_info_list.append(PoseTrack_images_info_item)
candidates = standard_data_item["candidates"]
for candidate in candidates:
det_score = candidate["det_score"]
if det_score < args.bbox_thresh: continue
if "pose_keypoints_2d" not in candidate: continue
if mode_track is True:
track_id = candidate["track_id"]
else:
track_id = -1
keypoints = candidate["pose_keypoints_2d"]
scores = candidate["pose_keypoints_2d"][2::3]
PoseTrack_annotations_info_item = {"image_id": frame_id,
"track_id": track_id,
"keypoints": keypoints,
"scores": scores }
#"score": scores }
PoseTrack_annotations_info_list.append(PoseTrack_annotations_info_item)
PoseTrack_dict["images"] = PoseTrack_images_info_list
PoseTrack_dict["annotations"] = PoseTrack_annotations_info_list
return PoseTrack_dict
# PoseFlow might output the format exactly like the format required by the PoseTrack dataset, therefore we do not need to do any conversion.
# If we only output [detection + pose estimation], then we need the conversion from standard openSVAI into PoseTrack format.
def standard_to_PoseTrack_17(standard_keypoints_ret, gt_python_data, mode_track = True, bbox_thresh = 0, drop_thresh = 0.8):
PoseTrack_data_content_list = []
for standard_data_item in standard_keypoints_ret:
image_name = standard_data_item["image"]["name"]
folder_name = os.path.basename(standard_data_item["image"]["folder"])
_, parent_folder_name = get_parent_folder_from_path(standard_data_item["image"]["folder"])
img_path = os.path.join("images", parent_folder_name, folder_name, image_name)
PoseTrack_data_content = {}
PoseTrack_data_content["image"] = [{"name": img_path}]
''' check if this is within gt '''
gt_images_info = gt_python_data["images"]
frame_id, index = find_id_from_annotation_by_name(gt_images_info, img_path)
annorect = []
standard_data_item_candidates = standard_data_item["candidates"]
for standard_data_item_candidate in standard_data_item_candidates:
det_bbox = standard_data_item_candidate["det_bbox"]
det_score = standard_data_item_candidate["det_score"]
if det_score < bbox_thresh: continue
if "pose_keypoints_2d" not in standard_data_item_candidate: continue
pose_keypoints_2d = standard_data_item_candidate["pose_keypoints_2d"]
if mode_track is True:
track_id = standard_data_item_candidate["track_id"]
track_score = standard_data_item_candidate["track_score"]
PoseTrack_data_content_candidate = {}
PoseTrack_data_content_candidate["x1"] = [det_bbox[0]]
PoseTrack_data_content_candidate["y1"] = [det_bbox[1]]
PoseTrack_data_content_candidate["x2"] = [det_bbox[0] + det_bbox[2]]
PoseTrack_data_content_candidate["y2"] = [det_bbox[1] + det_bbox[3]]
if mode_track is True:
PoseTrack_data_content_candidate["track_id"] = [track_id]
PoseTrack_data_content_candidate["score"] = [track_score]
else:
PoseTrack_data_content_candidate["track_id"] = [-1]
PoseTrack_data_content_candidate["score"] = [0]
annopoints_dict = []
num_keypoints = int(len(pose_keypoints_2d)/3)
if input_keypoints_format == "PoseTrack":
pose_keypoints_2d_PoseTrack = pose_keypoints_2d
num_keypoints_PoseTrack = int(len(pose_keypoints_2d_PoseTrack)/3)
for i in range(num_keypoints_PoseTrack):
annopoint = {}
annopoint["x"] = [pose_keypoints_2d_PoseTrack[3*i]]
annopoint["y"] = [pose_keypoints_2d_PoseTrack[3*i+1]]
annopoint["score"] = [pose_keypoints_2d_PoseTrack[3*i+2]]
# Drop keypoints based on the corresponding confidence
if annopoint["score"][0] <= drop_thresh:
continue
annopoint["id"] = [i]
annopoints_dict.append(annopoint)
annopoints = [{"point": annopoints_dict}]
PoseTrack_data_content_candidate["annopoints"] = annopoints
annorect.append(PoseTrack_data_content_candidate)
PoseTrack_data_content["annorect"] = annorect
PoseTrack_data_content_list.append(PoseTrack_data_content)
PoseTrack_data["annolist"] = PoseTrack_data_content_list
return PoseTrack_data
def batch_standard_to_PoseTrack_17(dataset_split = "light_track", mode = "pose", bbox_thresh = 0, drop_thresh = 0):
if dataset_split == "light_track":
gt_json_folder_base = "data/Data_2018/posetrack_data/annotations/val"
input_json_folder_base = "data/Data_2018/posetrack_results/lighttrack/results_openSVAI"
output_json_folder_base = "data/Data_2018/predictions_lighttrack/"
gt_json_file_paths = get_immediate_childfile_paths(gt_json_folder_base, ext=".json")
for gt_json_file_path in gt_json_file_paths:
json_file_name = os.path.basename(gt_json_file_path)
input_json_file_path = os.path.join(input_json_folder_base, json_file_name)
output_json_file_path = os.path.join(output_json_folder_base, json_file_name)
print("Reading Json: ", input_json_file_path)
rets_video_standard = read_json_from_file(input_json_file_path)
gt_python_data = read_json_from_file(gt_json_file_path)
if mode == "pose":
rets_video_posetrack_17 = standard_to_PoseTrack_17(rets_video_standard, gt_python_data, False, bbox_thresh, drop_thresh)
elif mode == "track":
rets_video_posetrack_17 = standard_to_PoseTrack_17(rets_video_standard, gt_python_data, True, bbox_thresh, drop_thresh)
write_json_to_file(rets_video_posetrack_17, output_json_file_path, flag_verbose = False)
return
def batch_standard_to_PoseTrack_18(dataset_split = "val", mode = "pose", bbox_thresh = 0):
if dataset_split == "light_track":
input_json_folder_base = "data/Data_2018/posetrack_results/lighttrack/results_openSVAI"
gt_json_folder_base = "data/Data_2018/posetrack_data/annotations/val"
output_json_folder_base = "data/Data_2018/predictions_lighttrack/"
gt_json_file_paths = get_immediate_childfile_paths(gt_json_folder_base, ext=".json")
for gt_json_file_path in gt_json_file_paths:
json_file_name = os.path.basename(gt_json_file_path)
input_json_file_path = os.path.join(input_json_folder_base, json_file_name)
output_json_file_path = os.path.join(output_json_folder_base, json_file_name)
print("Reading Json: ", input_json_file_path)
rets_video_standard = read_json_from_file(input_json_file_path)
gt_python_data = read_json_from_file(gt_json_file_path)
if mode == "pose":
rets_video_posetrack_18 = standard_to_PoseTrack_18(rets_video_standard, gt_python_data, False, bbox_thresh)
elif mode == "track":
rets_video_posetrack_18 = standard_to_PoseTrack_18(rets_video_standard, gt_python_data, True, bbox_thresh)
write_json_to_file(rets_video_posetrack_18, output_json_file_path, flag_verbose = False)
return
def find_id_from_annotation_by_name(gt_images_info, img_path):
index_list = find(gt_images_info, key="file_name", value=img_path)
assert(len(index_list) >= 1)
index = index_list[0]
frame_id = gt_images_info[index]["frame_id"]
return frame_id, index
def find(lst, key, value):
# find the index of a dict in list
index_list = []
for i, dic in enumerate(lst):
if dic[key] == value:
index_list.append(i)
return index_list
if __name__ == "__main__":
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--bbox_thresh', '-e', type=float, dest='bbox_thresh', default = 0)
parser.add_argument('--drop_thresh', '-r', type=float, dest='drop_thresh', default = 0)
parser.add_argument('--mode', '-m', type=str, dest='mode', default = "pose")
parser.add_argument('--dataset_split', '-d', type=str, dest='dataset_split', default = "val")
parser.add_argument('--format', '-f', type=str, dest='format', default = "17")
args = parser.parse_args()
return args
global args
args = parse_args()
print("Using detection threshold: ", args.bbox_thresh)
# The following output formats (17 and 18) should have identical evaluation results
# PoseTrack'18 format is designed such that it is easily compatible with COCO
# During evaluation, it seems that PoseTrack'18 format json will be transformed back to PoseTrack'17 format,
# Therefore, it is okay to just output in PoseTrack'17 format.
if args.format == "17":
# Generate PoseTrack17 format jsons for quantitative evaluation
batch_standard_to_PoseTrack_17("light_track",
args.mode,
args.bbox_thresh,
args.drop_thresh)
elif args.format == "18":
# Generate PoseTrack18 format jsons for quantitative evaluation
batch_standard_to_PoseTrack_18(args.dataset_split,
args.mode,
args.bbox_thresh)