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")
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) s0 = cv2.imread(str(s0path), -1).astype(np.float32)
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: recpath = find_same_id(gtpath, recpaths)