def __getitem__(self, idx):
rawdata = self.datainfos[idx]
img = rawdata['data']
image_id = rawdata['id']
height, width = img.shape[0:2]
gt_kpts = np.float32(rawdata['gt_keypoints']).transpose(0, 2, 1) # (N, 17, 3)
gt_segms = rawdata['segms']
gt_masks = np.array([annToMask(segm, height, width) for segm in gt_segms])
return {'img': img, 'kpts': gt_kpts, 'masks': gt_masks}
def test(model, dataset='cocoVal', logger=print):
ImageRoot = './data/coco2017/val2017'
AnnoFile = './data/person_keypoints_val2017_pose2seg.json'
datainfos = CocoDatasetInfo(ImageRoot, AnnoFile, onlyperson=True, loadimg=True)
model.eval()
results_segm = []
imgIds = []
for i in tqdm(range(len(datainfos))):
rawdata = datainfos[i]
img = rawdata['data']
image_id = rawdata['id']
height, width = img.shape[0:2]
gt_kpts = np.float32(rawdata['gt_keypoints']).transpose(0, 2, 1) # (N, 17, 3)
gt_segms = rawdata['segms']
gt_masks = np.array([annToMask(segm, height, width) for segm in gt_segms])
output = model([img], [gt_kpts], [gt_masks])
for mask in output[0]:
maskencode = maskUtils.encode(np.asfortranarray(mask))
maskencode['counts'] = maskencode['counts'].decode('ascii')
results_segm.append({
"image_id": image_id,
"category_id": 1,
"score": 1.0,
"segmentation": maskencode
})
imgIds.append(image_id)
def do_eval_coco(image_ids, coco, results, flag):
from pycocotools.cocoeval import COCOeval
assert flag in ['bbox', 'segm', 'keypoints']
# Evaluate
coco_results = coco.loadRes(results)
cocoEval = COCOeval(coco, coco_results, flag)
cocoEval.params.imgIds = image_ids
cocoEval.params.catIds = [1]
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
return cocoEval
cocoEval = do_eval_coco(imgIds, datainfos.COCO, results_segm, 'segm')
logger('[POSE2SEG] AP|.5|.75| S| M| L| AR|.5|.75| S| M| L|')
_str = '[segm_score] %s '%dataset
for value in cocoEval.stats.tolist():
_str += '%.3f '%value
logger(_str)
def __getitem__(self, idx):
rawdata = self.datainfos[idx]
img = rawdata['data']
image_id = rawdata['id']
height, width = img.shape[0:2]
gt_kpts = np.float32(rawdata['gt_keypoints']).transpose(
0, 2, 1) # (N, 17, 3)
gt_segms = rawdata['segms']
gt_masks = np.array(
[annToMask(segm, height, width) for segm in gt_segms])
gt_boxes = rawdata['boxes']
overlaps = bbox_overlaps(gt_boxes, gt_boxes)
gt_count = (overlaps >= 0.5).astype(np.float).max(1)
return {
'img': img,
'kpts': gt_kpts,
'masks': gt_masks,
'count': gt_count
}
def test(model, dataset='cocoVal', logger=print):
if dataset == 'OCHumanVal':
ImageRoot = r'\\fs01\Algo\ML\Datasets\Pose2Seg\data\OCHuman\images'
AnnoFile = r'\\fs01\Algo\ML\Datasets\Pose2Seg\data\OCHuman\annotations\ochuman_coco_format_val_range_0.00_1.00.json'
elif dataset == 'OCHumanTest':
ImageRoot = r'\\fs01\Algo\ML\Datasets\Pose2Seg\data\OCHuman\images'
AnnoFile = r'\\fs01\Algo\ML\Datasets\Pose2Seg\data\OCHuman\annotations\ochuman_coco_format_test_range_0.00_1.00.json'
elif dataset == 'cocoVal':
ImageRoot = r'\\fs01\Algo\ML\Datasets\Pose2Seg\data\coco2017\val2017'
AnnoFile = r'\\fs01\Algo\ML\Datasets\Pose2Seg\data\coco2017\annotations\person_keypoints_val2017_pose2seg.json'
datainfos = CocoDatasetInfo(ImageRoot,
AnnoFile,
onlyperson=True,
loadimg=True)
model.eval()
results_segm = []
imgIds = []
for i in tqdm(range(3, len(datainfos))):
rawdata = datainfos[i]
img = rawdata['data']
image_id = rawdata['id']
height, width = img.shape[0:2]
gt_kpts = np.float32(rawdata['gt_keypoints']).transpose(
0, 2, 1) # (N, 17, 3)
gt_segms = rawdata['segms']
gt_masks = np.array(
[annToMask(segm, height, width) for segm in gt_segms])
output = model([img], [gt_kpts], [gt_masks])
img = img[..., ::-1]
# plt.switch_backend("TkAgg")
# plt.imshow(gt_masks[0,...])
# plt.show()
# fig.savefig('C:\\Users\\erez\\Projects\\Pose2Seg\\demo.png', bbox_inches='tight')
MASKS = np.zeros(output[0][0].shape)
for id, mask in enumerate(output[0]):
from skimage.color import label2rgb
MASKS += (id + 1) * mask
maskencode = maskUtils.encode(np.asfortranarray(mask))
maskencode['counts'] = maskencode['counts'].decode('ascii')
results_segm.append({
"image_id": image_id,
"category_id": 1,
"score": 1.0,
"segmentation": maskencode
})
imgIds.append(image_id)
image_label_overlay = label2rgb(MASKS,
image=img,
alpha=0.3,
bg_label=0)
plt.imshow(image_label_overlay)
plt.show()
def do_eval_coco(image_ids, coco, results, flag):
from pycocotools.cocoeval import COCOeval
assert flag in ['bbox', 'segm', 'keypoints']
# Evaluate
coco_results = coco.loadRes(results)
cocoEval = COCOeval(coco, coco_results, flag)
cocoEval.params.imgIds = image_ids
cocoEval.params.catIds = [1]
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
return cocoEval
cocoEval = do_eval_coco(imgIds, datainfos.COCO, results_segm, 'segm')
logger('[POSE2SEG] AP|.5|.75| S| M| L| AR|.5|.75| S| M| L|')
_str = '[segm_score] %s ' % dataset
for value in cocoEval.stats.tolist():
_str += '%.3f ' % value
logger(_str)