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")
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]
mn, std = np.mean(ang_deg), np.std(ang_deg)
errs = np.append(errs, ang_deg)
s0path = find_same_id_s0(gtpath, s0paths)
# load images
gt = cv2.imread(str(gtpath), -1).astype(np.float32)
recon = cv2.imread(str(recpath), -1).astype(np.float32)
s0 = cv2.imread(str(s0path), -1).astype(np.float32)
# mask pixels which do not have any values
noObj_mask = (np.sum(gt, axis=2) > 0)
# mask pixels whose values are smaller than threshold
s0_Darkmask = (np.sum(s0 / 3, axis=2) > params["s0threshold"])
s0_Whitemask = (s0[:, :, 0] < MAX_16BIT) + (s0[:, :, 1] < MAX_16BIT) + (
s0[:, :, 2] < MAX_16BIT)
# calc error
ang_deg = nm.calcAngError(gt, recon)
#calc gt zenith
zenith_deg = nm.calcZenithDegMap(gt[:, :, ::-1])
# calc depth error
depth_err = nm.normalErr2depthErr(zenith_deg, ang_deg)
# prepare figure
fig = plt.figure(figsize=(9, 4.5))
gs = gridspec.GridSpec(3,
3,
height_ratios=[1, 1, 1],
width_ratios=[4, 1, 1])
ax_main = plt.subplot(gs[:, 0])
# prepare cmap
cmap = mpl.cm.jet