def annotationDensityPlot(pyroidb):
"""
From a pyroidb object, create a concentration plot of
the annotations.
"""
clsToSet = loadDatasetIndexDict()
matr = np.zeros((8,500,500)).astype(np.float64)
cls_count = np.zeros((8)).astype(np.int)
for box,cls in pyroidb:
box = box.copy() * 500
xmin = int(box[0])
ymin = int(box[1])
xmax = int(box[2])
ymax = int(box[3])
matr[cls, ymin:ymax, xmin:xmax] += 1
cls_count[cls] += 1
for idx,cls in enumerate(cls_count):
if cls == 0: continue
print("{}: {}".format(clsToSet[idx],cls))
print(np.sum(matr[idx,...]))
print(np.max(matr[idx,...]))
matr[idx,...] /= np.sum(matr[idx,...])
print(np.sum(matr[idx,...]))
print(np.max(matr[idx,...]))
return matr
def annotationDensityPlot(pyroidb):
"""
From a pyroidb object, create a concentration plot of
the annotations.
"""
clsToSet = loadDatasetIndexDict()
matr = np.zeros((9, 500, 500)).astype(np.float64)
cls_count = np.zeros((9)).astype(np.int)
for box, cls in pyroidb:
xmin = int(box[0])
ymin = int(box[1])
xmax = int(box[2])
ymax = int(box[3])
matr[cls - 1, (xmin - 1):xmax, (ymin - 1):ymax] += 1
cls_count[cls - 1] += 1
print(cls_count)
print(sum(cls_count))
for idx, cls in enumerate(cls_count):
if cls == 0: continue
print("{}: {}".format(clsToSet[idx], cls))
matr[idx, ...] /= cls
return matr
print("ave height: {} | std. height: {}".format(
np.mean(heights), np.std(heights, dtype=np.float64)))
prefix_path = cfg.IMDB_REPORT_OUTPUT_PATH
if osp.exists(prefix_path) is False:
os.makedirs(prefix_path)
path = osp.join(prefix_path, "areas.dat")
np.savetxt(path, areas, fmt='%.18e', delimiter=' ')
path = osp.join(prefix_path, "widths.dat")
np.savetxt(path, widths, fmt='%.18e', delimiter=' ')
path = osp.join(prefix_path, "heights.dat")
np.savetxt(path, heights, fmt='%.18e', delimiter=' ')
print("-=-=-=-=-=-")
clsToSet = loadDatasetIndexDict()
print("as pytorch friendly ")
pyroidb = RoidbDataset(roidb, [1, 2, 3, 4, 5, 6, 7, 8],
loader=cv2.imread,
transform=cropImageToAnnoRegion)
if args.save:
print("save 30 cropped annos in output folder.")
saveDir = "./output/mixedDataReport/"
if not osp.exists(saveDir):
print("making directory: {}".format(saveDir))
os.makedirs(saveDir)
for i in range(30):
