import matplotlib.pyplot as plt from matplotlib import style import numpy as np from utils import raw_to_ndarray fig = plt.figure() style.use('ggplot') size = (960,1200) # images are flipped w.r.t. x-axis groundTruth = raw_to_ndarray("../figures/nose/croppedjaw_sim-pb-pc-pn1_fr180-gt.raw",size) bilfilRoi = raw_to_ndarray("../figures/bilfil/croppedjaw_sim-pb-pc-pn16384_fr180_bilfil-roi.raw",size) bilfilAdpt = raw_to_ndarray("../figures/bilfil/croppedjaw_sim-pb-pc-pn16384_fr180_bilfil-adpt.raw",size) bilfilAdptx5 = raw_to_ndarray("../figures/bilfil/croppedjaw_sim-pb-pc-pn16384_fr180_bilfil-adptx5.raw",size) profile = 450 labels = [ "Ground truth", "Classical bilateral filter", "Adaptive bilateral filter", "Amplified adpt. bil. filter" ] datasets = [ groundTruth, bilfilRoi, bilfilAdpt, bilfilAdptx5 ]
import matplotlib.pyplot as plt from matplotlib import style from utils import raw_to_ndarray, save_tex fig = plt.figure() style.use('ggplot') # actual image size height = 190 width = 340 profile = 28 size = (height, width) gt = raw_to_ndarray("../figures/nose/croppedjaw_sim-pb-pc-pn_fr180_gt_ww13_wl017_crpx0-860y420-350.raw", size) guide = raw_to_ndarray("../figures/nose/croppedjaw_sim-pb-pc-pn16384_fr180_guideimg_ww13_wl017_crpx0-860y420-350.raw",size) raw = raw_to_ndarray("../figures/nose/croppedjaw_sim-pb-pc-pn16384_fr180_rawimg_ww13_wl017_crpx0-860y420-350.raw",size) plt.plot(gt[profile, :], label="ground truth") plt.plot(raw[profile, :], label="raw image") plt.plot(guide[profile, :], label="guide image") plt.xlabel("Pixel position") plt.ylabel("Intensity") plt.grid(True) plt.legend(loc="lower left") #plt.show() save_tex( __file__,
import matplotlib.pyplot as plt from matplotlib import style import numpy as np from utils import raw_to_ndarray fig = plt.figure() style.use('ggplot') size = (1000,1118) datasets = {} # images are flipped w.r.t. x-axis datasets["Ground truth"] = 10.0*raw_to_ndarray("../figures/bilfil/contrast_max255_groundtruth.raw",size) datasets["Noisy image"] = raw_to_ndarray("../figures/bilfil/contrast_max2550_poisson.raw",size) datasets["Conventional bil. filter"] = raw_to_ndarray("../figures/bilfil/contrast_max2550_poisson_roi512-0-90-150.raw",size) datasets["Adaptive bil. filter"] = raw_to_ndarray("../figures/bilfil/contrast_max2550_poisson_adpt1-0.raw",size) profile = 720 for label, dataset in datasets.iteritems(): plt.plot(dataset[profile,355:655], label=label) plt.legend() plt.grid(True) plt.show() #from utils import save_tex #save_tex(__file__)