def plot_recErr_per_data(task, data_name, maskFlag, epochNums, resultname,
color, ax):
gtpaths = returnGTPaths(task, data_name)
s0paths = returnS0Paths(task, data_name)
if maskFlag:
maskpaths = returnMaskPaths(task, data_name)
err_perEpoch = []
for epochNum in epochNums:
recpaths = returnRecPaths(task, resultname, epochNum, data_name)
errs = np.empty(0, dtype=np.float32)
for gtpath in gtpaths:
s0path = find_same_id_s0(gtpath, s0paths)
recpath = find_same_id(gtpath, recpaths)
gt = cv2.imread(str(gtpath), -1).astype(np.float32)
s0 = cv2.imread(str(s0path), -1).astype(np.float32)
rec = cv2.imread(str(recpath), -1).astype(np.float32)
# Calc masks
noObj_mask = (np.sum(gt, axis=2) > 0)
s0_mask = (np.sum(s0 / 3, axis=2) > params["s0threshold"])
# Calc error
ang_deg = calcAngError(gt, rec)
# consider mask area
if maskFlag:
maskpath = find_same_id(gtpath, maskpaths)
mask = cv2.imread(str(maskpath), -1).astype(np.float32)
ang_deg = extractMaskArea(ang_deg, mask, maskFlag)
noObj_mask = extractMaskArea(noObj_mask, mask, maskFlag)
s0_mask = extractMaskArea(s0_mask, mask, maskFlag)
ang_deg = ang_deg[noObj_mask * s0_mask]
errs = np.append(errs, ang_deg)
err_perEpoch.append(np.mean(errs))
ax.plot(epochNums,
err_perEpoch,
label=data_name.split("/")[1],
color=color,
marker=shape)
ax.legend(bbox_to_anchor=(1.05, 1), loc="upper left")
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_title("Reconstruction Error per Epoch", fontsize=16)
ax.set_xlabel("epoch num", fontsize=12)
ax.set_ylabel("error [deg]", fontsize=12)
ax.set_xlim(0, epochLim)
for color, resultname, label in zip(colors, resultnames, labels):
result_epochs = [
n.name for n in pathlib.Path("../{}/Result/{}/Model/".format(
task, resultname)).iterdir()
]
epochNums = returnSortedEpochNum(result_epochs)
for shape, data_name in zip(shapes, data_names):
gtpaths = returnGTPaths(task, data_name)
s0paths = returnS0Paths(task, data_name)
if maskFlag:
maskpaths = returnMaskPaths(task, data_name)
err_perEpoch = []
for epochNum in epochNums:
recpaths = returnRecPaths(task, resultname, epochNum, data_name)
errs = np.empty(0, dtype=np.float32)
for gtpath in gtpaths:
s0path = find_same_id_s0(gtpath, s0paths)
recpath = find_same_id(gtpath, recpaths)
gt = cv2.imread(str(gtpath), -1).astype(np.float32)
from mymodules.myutils.imageutils import MAX_16BIT
import mymodules.myutils.mplutils as myplt
# load Params
#------------------------
with open("./parameters_visualize.yml") as f:
params = yaml.load(f)["recError_per_image"]
task = params["task"]
gtfolder = params["gtfolder"]
resultname = params["resultname"]
epochnum = params["epochnum"]
maskFlag = params["maskFlag"]
saveFlag = params["saveFlag"]
gtpaths = returnGTPaths(task, gtfolder)
s0paths = returnS0Paths(task, gtfolder)
recpaths = returnRecPaths(task, resultname, epochnum, gtfolder)
if saveFlag:
savefile = pathlib.Path("../{}/Result/{}/Inference/epoch-{}/{}/{}".format(
task, resultname, epochnum, gtfolder, saveFlag))
savefile.mkdir()
if maskFlag:
raise ValueError("maskFlag shoud be False")
# main process
#------------------------
x, y = [], [] # for plot scatter graph
for gtpath in gtpaths: