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update coco eval
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@ajaichemmanam
ajaichemmanam committed Sep 17, 2020
1 parent 74718c4 commit 9409886
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Showing 2 changed files with 143 additions and 106 deletions.
1 change: 1 addition & 0 deletions src/lib/customdatasets/dataset/coco.py
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Original file line number Diff line number Diff line change
Expand Up @@ -145,6 +145,7 @@ def run_eval(self, results, save_dir):
# coco_dets = self.coco.loadRes('{}/results.json'.format(save_dir))
detections = self.convert_eval_format(results)
coco_dets = self.coco.loadRes(detections)

coco_eval = COCOeval(self.coco, coco_dets, "bbox")
coco_eval.evaluate()
coco_eval.accumulate()
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248 changes: 142 additions & 106 deletions src/lib/customdatasets/dataset/coco_hp.py
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Expand Up @@ -10,111 +10,147 @@

import torch.utils.data as data


class COCOHP(data.Dataset):
num_classes = 1
num_joints = 17
default_resolution = [512, 512]
mean = np.array([0.40789654, 0.44719302, 0.47026115],
dtype=np.float32).reshape(1, 1, 3)
std = np.array([0.28863828, 0.27408164, 0.27809835],
num_classes = 1
num_joints = 17
default_resolution = [512, 512]
mean = np.array([0.40789654, 0.44719302, 0.47026115],
dtype=np.float32).reshape(1, 1, 3)
std = np.array([0.28863828, 0.27408164, 0.27809835],
dtype=np.float32).reshape(1, 1, 3)
flip_idx = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10],
[11, 12], [13, 14], [15, 16]]
def __init__(self, opt, split):
super(COCOHP, self).__init__()
self.edges = [[0, 1], [0, 2], [1, 3], [2, 4],
[4, 6], [3, 5], [5, 6],
[5, 7], [7, 9], [6, 8], [8, 10],
[6, 12], [5, 11], [11, 12],
[12, 14], [14, 16], [11, 13], [13, 15]]

self.acc_idxs = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]
self.data_dir = os.path.join(opt.data_dir, 'coco')
self.img_dir = os.path.join(self.data_dir, '{}2017'.format(split))
if split == 'test':
self.annot_path = os.path.join(
self.data_dir, 'annotations',
'image_info_test-dev2017.json').format(split)
else:
self.annot_path = os.path.join(
self.data_dir, 'annotations',
'person_keypoints_{}2017.json').format(split)
self.max_objs = 32
self._data_rng = np.random.RandomState(123)
self._eig_val = np.array([0.2141788, 0.01817699, 0.00341571],
dtype=np.float32)
self._eig_vec = np.array([
[-0.58752847, -0.69563484, 0.41340352],
[-0.5832747, 0.00994535, -0.81221408],
[-0.56089297, 0.71832671, 0.41158938]
], dtype=np.float32)
self.split = split
self.opt = opt

print('==> initializing coco 2017 {} data.'.format(split))
self.coco = coco.COCO(self.annot_path)
image_ids = self.coco.getImgIds()

if split == 'train':
self.images = []
for img_id in image_ids:
idxs = self.coco.getAnnIds(imgIds=[img_id])
if len(idxs) > 0:
self.images.append(img_id)
else:
self.images = image_ids
self.num_samples = len(self.images)
print('Loaded {} {} samples'.format(split, self.num_samples))

def _to_float(self, x):
return float("{:.2f}".format(x))

def convert_eval_format(self, all_bboxes):
# import pdb; pdb.set_trace()
detections = []
for image_id in all_bboxes:
for cls_ind in all_bboxes[image_id]:
category_id = 1
for dets in all_bboxes[image_id][cls_ind]:
bbox = dets[:4]
bbox[2] -= bbox[0]
bbox[3] -= bbox[1]
score = dets[4]
bbox_out = list(map(self._to_float, bbox))
keypoints = np.concatenate([
np.array(dets[5:39], dtype=np.float32).reshape(-1, 2),
np.ones((17, 1), dtype=np.float32)], axis=1).reshape(51).tolist()
keypoints = list(map(self._to_float, keypoints))

detection = {
"image_id": int(image_id),
"category_id": int(category_id),
"bbox": bbox_out,
"score": float("{:.2f}".format(score)),
"keypoints": keypoints
}
detections.append(detection)
return detections

def __len__(self):
return self.num_samples

def save_results(self, results, save_dir):
json.dump(self.convert_eval_format(results),
open('{}/results.json'.format(save_dir), 'w'))


def run_eval(self, results, save_dir):
# result_json = os.path.join(opt.save_dir, "results.json")
# detections = convert_eval_format(all_boxes)
# json.dump(detections, open(result_json, "w"))
self.save_results(results, save_dir)
coco_dets = self.coco.loadRes('{}/results.json'.format(save_dir))
coco_eval = COCOeval(self.coco, coco_dets, "keypoints")
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
coco_eval = COCOeval(self.coco, coco_dets, "bbox")
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
flip_idx = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10],
[11, 12], [13, 14], [15, 16]]

def __init__(self, opt, split):
super(COCOHP, self).__init__()
self.edges = [[0, 1], [0, 2], [1, 3], [2, 4],
[4, 6], [3, 5], [5, 6],
[5, 7], [7, 9], [6, 8], [8, 10],
[6, 12], [5, 11], [11, 12],
[12, 14], [14, 16], [11, 13], [13, 15]]

self.acc_idxs = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]
self.data_dir = os.path.join(opt.data_dir, 'coco')
self.img_dir = os.path.join(self.data_dir, '{}2017'.format(split))
if split == 'test':
self.annot_path = os.path.join(
self.data_dir, 'annotations',
'image_info_test-dev2017.json').format(split)
else:
self.annot_path = os.path.join(
self.data_dir, 'annotations',
'person_keypoints_{}2017.json').format(split)
self.max_objs = 32
self._data_rng = np.random.RandomState(123)
self._eig_val = np.array([0.2141788, 0.01817699, 0.00341571],
dtype=np.float32)
self._eig_vec = np.array([
[-0.58752847, -0.69563484, 0.41340352],
[-0.5832747, 0.00994535, -0.81221408],
[-0.56089297, 0.71832671, 0.41158938]
], dtype=np.float32)
self.split = split
self.opt = opt

print('==> initializing coco 2017 {} data.'.format(split))
self.coco = coco.COCO(self.annot_path)
image_ids = self.coco.getImgIds()

if split == 'train':
self.images = []
for img_id in image_ids:
idxs = self.coco.getAnnIds(imgIds=[img_id])
if len(idxs) > 0:
self.images.append(img_id)
else:
self.images = image_ids
self.num_samples = len(self.images)
print('Loaded {} {} samples'.format(split, self.num_samples))

def _to_float(self, x):
return float("{:.2f}".format(x))

def convert_eval_format(self, all_bboxes):
# import pdb; pdb.set_trace()
detections = []
for image_id in all_bboxes:
for cls_ind in all_bboxes[image_id]:
category_id = 1
if type(all_bboxes[image_id][cls_ind]) == dict:
for id in range(len(all_bboxes[image_id][cls_ind]['boxs'])):
dets = all_bboxes[image_id][cls_ind]['boxs'][id]
mask = all_bboxes[image_id][cls_ind]['pred_mask'][id]
bbox = dets[:4]
bbox[2] -= bbox[0]
bbox[3] -= bbox[1]
score = dets[4]
bbox_out = list(map(self._to_float, bbox))
keypoints = np.concatenate([
np.array(
dets[5:39], dtype=np.float32).reshape(-1, 2),
np.ones((17, 1), dtype=np.float32)], axis=1).reshape(51).tolist()
keypoints = list(map(self._to_float, keypoints))

detection = {
"image_id": int(image_id),
"category_id": int(category_id),
"bbox": bbox_out,
"score": float("{:.2f}".format(score)),
"segmentation": mask,
"keypoints": keypoints
}
detections.append(detection)
else:
for dets in all_bboxes[image_id][cls_ind]:
bbox = dets[:4]
bbox[2] -= bbox[0]
bbox[3] -= bbox[1]
score = dets[4]
bbox_out = list(map(self._to_float, bbox))
keypoints = np.concatenate([
np.array(
dets[5:39], dtype=np.float32).reshape(-1, 2),
np.ones((17, 1), dtype=np.float32)], axis=1).reshape(51).tolist()
keypoints = list(map(self._to_float, keypoints))

detection = {
"image_id": int(image_id),
"category_id": int(category_id),
"bbox": bbox_out,
"score": float("{:.2f}".format(score)),
"keypoints": keypoints
}
detections.append(detection)
return detections

def __len__(self):
return self.num_samples

def save_results(self, results, save_dir):
json.dump(self.convert_eval_format(results),
open('{}/results.json'.format(save_dir), 'w'))

def run_eval(self, results, save_dir):
# result_json = os.path.join(opt.save_dir, "results.json")
# detections = convert_eval_format(all_boxes)
# json.dump(detections, open(result_json, "w"))
self.save_results(results, save_dir)
# coco_dets = self.coco.loadRes('{}/results.json'.format(save_dir))
detections = self.convert_eval_format(results)
coco_dets = self.coco.loadRes(detections)

coco_eval = COCOeval(self.coco, coco_dets, "keypoints")
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()

coco_eval = COCOeval(self.coco, coco_dets, "bbox")
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()

coco_eval = COCOeval(self.coco, coco_dets, "segm")
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()

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