def run_eval_debug(self, results, save_dir):
self.save_results(results, save_dir)
save_path = "cache/fail/"
coco_dets = self.coco.loadRes('{}/results.json'.format(save_dir))
coco_eval = COCOeval(self.coco, coco_dets, "bbox")
p = coco_eval.params
p.imgIds = list(np.unique(p.imgIds))
p.maxDets = sorted(p.maxDets)
coco_eval.params = p
coco_eval._prepare()
catIds = [-1]
computeIoU = coco_eval.computeIoU
coco_eval.ious = {(imgId, catId): coco_eval.computeIoU(imgId, catId) \
for imgId in p.imgIds
for catId in catIds}
maxDet = p.maxDets[-1]
for imgId in p.imgIds:
img_path = "/home/user/home/user/Xinyuan/work/CenterNet-1/data/coco/val2017/"
fullImgId = str(imgId).zfill(11)
img_path = os.path.join(img_path, fullImgId + '.jpg')
img = cv2.imread(img_path)
for catId in catIds:
# for i, areaRng in enumerate(p.areaRng):
areaRng = p.areaRng[0]
print("areaRng: " + areaRng)
result = coco_eval.getImageFailCase(imgId, catId, areaRng,
maxDet)
gtIds = result['gtIds']
dtIds = result['dtIds']
gtFailIds = gtIds[result['gtfail'][0]]
dtFailIds = dtIds[result['dtfail'][0]]
gts = [
_ for cId in p.catIds for _ in coco_eval._gts[imgId, cId]
]
dts = [
_ for cId in p.catIds for _ in coco_eval._dts[imgId, cId]
]
gts = [g['bbox'] for g in gts]
dts = [d['bbox'] for d in dts]
pdb.set_trace()
c = [255, 0, 0]
for dt in dts:
bbox = np.array(dt, dtype=np.int32)
cv2.rectangle(img, (bbox[0], bbox[1]), (bbox[2], bbox[3]),
c, 2)
c = [0, 255, 0]
for gt in gts:
bbox = np.array(gt, dtype=np.int32)
cv2.rectangle(img, (bbox[0], bbox[1]), (bbox[2], bbox[3]),
c, 2)
# saveImage
save_path = os.path.join(save_path, fullImgId + "_" + str(i))
cv2.imwrite(save_path, img)
coco_dets = coco.loadRes('{}/results.json'.format(save_dir))
coco_eval = COCOeval(coco, coco_dets, "bbox")
print(dir(coco_eval))
save_path = "src/cache/fail/"
coco_dets = coco.loadRes('{}/results.json'.format(save_dir))
coco_eval = COCOeval(coco, coco_dets, "bbox")
p = coco_eval.params
p.imgIds = list(np.unique(p.imgIds))
p.maxDets = sorted(p.maxDets)
coco_eval.params = p
coco_eval._prepare()
cat_dict = coco.cats
# print(cat_dict)
catIds = coco_eval.params.catIds
computeIoU = coco_eval.computeIoU
coco_eval.ious = {(imgId, catId): coco_eval.computeIoU(imgId, catId) \
for imgId in p.imgIds
for catId in catIds}
maxDet = p.maxDets[-1]
for imgId in p.imgIds:
img_path = "/home/user/home/user/Xinyuan/work/CenterNet-1/data/coco/val2017/"
fullImgId = str(imgId).zfill(12)
img_path = os.path.join(img_path, fullImgId + '.jpg')
print(img_path)
img = cv2.imread(img_path)
for catId in catIds:
cat = int(catId)
txt = cat_dict[cat]['name']
font = cv2.FONT_HERSHEY_SIMPLEX
cat_size = cv2.getTextSize(txt, font, 0.5, 2)[0]
areaRng = p.areaRng[0]
class MapEvaluator:
def __init__(self, gt_json, cat_ids):
# Eval options
self.iou_threshs = [.5]
self.max_dets = [1, 10, 100]
self.cat_ids = cat_ids
self.coco_gt = COCO(gt_json)
def _init_coco_eval(self, dt_json_data):
coco_dt = self.coco_gt.loadRes(dt_json_data)
self.coco_eval = COCOeval(self.coco_gt, coco_dt, 'bbox')
self.coco_eval.params.iouThrs = np.array(self.iou_threshs)
self.coco_eval.params.maxDets = self.max_dets
self.coco_eval.params.catIds = self.cat_ids
def do_eval(self, dt_json_data):
self._init_coco_eval(dt_json_data)
self.coco_eval.evaluate()
self.coco_eval.accumulate()
def do_eval_and_print(self, dt_json_data):
self._init_coco_eval(dt_json_data)
self.coco_eval.evaluate()
self.coco_eval.accumulate()
self.coco_eval.summarize()
def get_avg_precision_recall(self, t=0, a=0, m=-1):
p = self.coco_eval.eval['precision'][t, :, :, a, m]
r = self.coco_eval.eval['recall'][t, :, a, m]
# Removing -1 entries for classes that have no GT objects.
p = p[:, np.where(r > -1)[0]]
r = r[r > -1]
assert (np.all(p >= 0.) and np.all(r >= 0.))
ap = p.mean(axis=1)
ar = r.mean()
return ap, ar
def evaluateImg(self, imgId, catId, aRng=(-np.inf, np.inf), maxDet=100):
p = self.coco_eval.params
# add backward compatibility if useSegm is specified in params
p.iouType = 'bbox'
p.imgIds = list(np.unique(p.imgIds))
if p.useCats:
p.catIds = list(np.unique(p.catIds))
p.maxDets = sorted(p.maxDets)
self.coco_eval.params = p
self.coco_eval._prepare()
self.coco_eval.ious = {
(imgId, catId): self.coco_eval.computeIoU(imgId, catId)
}
img_eval = self.coco_eval.evaluateImg(imgId, catId, aRng, maxDet)
inds = img_eval['dtIds']
matches = img_eval['dtMatches']
return inds, matches