def blobs2annList(blobs, categoryDict, batch):
if "maskVoid" not in batch:
maskVoid = None
else:
maskVoid = batch["maskVoid"]
h,w = blobs.shape
annList = []
blobs = ms.t2n(blobs)
for u in np.unique(blobs):
if u == 0:
continue
binmask = (blobs == u)
if maskVoid is not None:
binmask = binmask * (ms.t2n(maskVoid).squeeze())
seg = maskUtils.encode(np.asfortranarray(ms.t2n(binmask)).astype("uint8"))
seg["counts"] = seg["counts"].decode("utf-8")
score = 1
annList += [{"segmentation":seg,
"iscrowd":0,
"area":int(maskUtils.area(seg)),
"image_id":batch["name"][0],
"category_id":int(categoryDict[u]),
"height":h,
"width":w,
"score":score}]
return annList
def blobs2BestDice(blobs, categoryDict, propDict, batch):
h, w = blobs.shape
annList = []
blobs_copy = np.zeros(blobs.shape, int)
if "maskVoid" in batch:
maskVoid = batch["maskVoid"]
else:
maskVoid = None
for u in np.unique(blobs):
if u == 0:
continue
binmask = (blobs == u)
best_dice = 0.
best_mask = None
for ann in propDict['propDict'][u-1]["annList"]:
score = dice(ann["mask"], binmask)
if score > best_dice:
best_dice = score
best_mask = ann["mask"]
prop_score = ann["score"]
if best_mask is None:
best_mask = (blobs==u).astype(int)
if maskVoid is not None:
binmask = best_mask * (ms.t2n(maskVoid).squeeze())
else:
binmask = best_mask
if best_mask is None:
blobs_copy[blobs==u] = u
else:
blobs_copy[best_mask==1] = u
seg = maskUtils.encode(np.asfortranarray(ms.t2n(binmask)).astype("uint8"))
seg["counts"] = seg["counts"].decode("utf-8")
score = best_dice
# if batch["dataset"] == "coco2014":
# image_id = int(batch["name"][0])
# else:
image_id = batch["name"][0]
annList += [{"segmentation":seg,
"iscrowd":0,
"area":int(maskUtils.area(seg)),
"image_id":image_id,
"category_id":int(categoryDict[u]),
"height":h,
"width":w,
"score":score}]
return {"blobs":blobs_copy, "annList":annList}
def pointList2BestObjectness(pointList, batch, proposal_type="sharp"):
if "single_point" in batch:
single_point = True
else:
single_point = False
propDict = pointList2propDict(pointList, batch, thresh=0.5,
single_point=single_point,
proposal_type=proposal_type)
h,w = propDict["background"].squeeze().shape
blobs = np.zeros((h,w), int)
categoryDict = {}
if "maskVoid" in batch and batch["maskVoid"] is not None:
maskVoid = ms.t2n(batch["maskVoid"].squeeze())
else:
maskVoid = None
annList = []
for i, prop in enumerate(propDict['propDict']):
if len(prop["annList"]) == 0:
continue
blobs[prop["annList"][0]["mask"] !=0] = i+1
categoryDict[i+1] = prop["category_id"]
if maskVoid is not None:
binmask = prop["annList"][0]["mask"] * (ms.t2n(maskVoid).squeeze())
else:
binmask = prop["annList"][0]["mask"]
seg = maskUtils.encode(np.asfortranarray(ms.t2n(binmask)).astype("uint8"))
seg["counts"] = seg["counts"].decode("utf-8")
score = prop["annList"][0]["score"]
annList += [{"segmentation":seg,
"iscrowd":0,
"area":int(maskUtils.area(seg)),
"image_id":batch["name"][0],
"category_id":int(prop['category_id']),
"height":h,
"width":w,
"score":score}]
return {"annList":annList, "blobs": blobs, "categoryDict":categoryDict}
def annList2BestDice(annList, batch):
sharp_proposals = base_dataset.SharpProposals(batch)
new_annList = []
if "maskVoid" in batch and batch["maskVoid"] is not None:
maskVoid = batch["maskVoid"]
else:
maskVoid = None
for ann in annList:
binmask = ann2mask(ann)["mask"]
best_dice = 0.
best_mask = None
for sharp_ann in sharp_proposals:
if ann["score"] < 0.5:
continue
score = dice(sharp_ann["mask"], binmask)
if score > best_dice:
best_dice = score
best_mask = sharp_ann["mask"]
# prop_score = ann["score"]
if best_mask is None:
best_mask = binmask
if maskVoid is not None:
binmask = best_mask * (ms.t2n(maskVoid).squeeze())
else:
binmask = best_mask
seg = maskUtils.encode(np.asfortranarray(ms.t2n(binmask)).astype("uint8"))
seg["counts"] = seg["counts"].decode("utf-8")
ann["score"] = best_dice
ann["segmentation"] = seg
new_annList += [ann]
return {"annList":new_annList}
def maskList2annList(maskList, categoryList, image_id):
annList = []
_, h, w = maskList.shape
for i in range(maskList.shape[0]):
binmask = maskList[i]
seg = maskUtils.encode(np.asfortranarray(ms.t2n(binmask)).astype("uint8"))
seg["counts"] = seg["counts"].decode("utf-8")
score = 1
annList += [{"segmentation":seg,
"iscrowd":0,
"area":int(maskUtils.area(seg)),
"image_id":image_id,
"category_id":int(categoryList[i]),
"height":h,
"width":w,
"score":score}]
return annList
def mask2ann(binmask, category_id, image_id,
height, width, maskVoid=None, score=None, point=None):
binmask = binmask.squeeze().astype("uint8")
if maskVoid is not None:
binmask = binmask * maskVoid
segmentation = maskUtils.encode(np.asfortranarray(ms.t2n(binmask)).astype("uint8"))
segmentation["counts"] = segmentation["counts"].decode("utf-8")
# print(segmentation)
ann = {"segmentation":segmentation,
"iscrowd":0,
"area":int(maskUtils.area(segmentation)),
"image_id":image_id,
"category_id":int(category_id),
"height":height,
"width":width,
"score":score,
"point":point}
return ann
def load_gtAnnDict(main_dict, reset=None):
reset = None
_, val_set = load_trainval(main_dict)
annList_path = val_set.annList_path
if os.path.exists(annList_path) and reset != "reset":
return ms.load_json(annList_path)
else:
ann_json = {}
ann_json["categories"] = val_set.categories
ann_json["type"] = "instances"
# Images
imageList = []
annList = []
id = 1
for i in range(len(val_set)):
print("{}/{}".format(i, len(val_set)))
batch = val_set[i]
image_id = batch["name"]
height, width = batch["images"].shape[-2:]
imageList += [{"file_name":batch["name"],
"height":height,
"width":width,
"id":batch["name"]}]
maskObjects = batch["maskObjects"]
maskClasses = batch["maskClasses"]
n_objects = maskObjects[maskObjects!=255].max().item()
for obj_id in range(1, n_objects+1):
if obj_id == 0:
continue
binmask = (maskObjects == obj_id)
segmentation = maskUtils.encode(np.asfortranarray(ms.t2n(binmask)))
segmentation["counts"] = segmentation["counts"].decode("utf-8")
uniques = (binmask.long()*maskClasses).unique()
uniques = uniques[uniques!=0]
assert len(uniques) == 1
category_id = uniques[0].item()
annList += [{"segmentation":segmentation,
"iscrowd":0,
# "bbox":maskUtils.toBbox(segmentation).tolist(),
"area":int(maskUtils.area(segmentation)),
"id":id,
"image_id":image_id,
"category_id":category_id}]
id += 1
ann_json["annotations"] = annList
ann_json["images"] = imageList
ms.save_json(annList_path, ann_json)
# Save dummy results
anns = ms.load_json(annList_path)
fname_dummy = annList_path.replace(".json","_best.json")
annList = anns["annotations"]
for a in annList:
a["score"] = 1
ms.save_json(fname_dummy, annList)