def postResize(self):
print(" Post resize")
data_name = self._keyword
data_files = dataFiles(data_name)
for data_file in data_files:
data_filename = os.path.basename(data_file)
C_8U = loadRGB(data_file)
if C_8U is None:
os.remove(data_file)
print(" - Delete: %s" % data_filename)
continue
h, w = C_8U.shape[0:2]
opt_scale = 800.0 / float(h)
opt_scale = max(opt_scale, 800.0 / float(w))
opt_scale = min(opt_scale, 1.0)
h_opt = int(opt_scale * h)
w_opt = int(opt_scale * w)
C_8U_small = cv2.resize(C_8U, (w_opt, h_opt))
saveRGB(data_file, C_8U_small)
print(" - Resized: %s" % data_filename)
def loadData(data_name, data_id):
data_file = dataFile(data_name, data_id)
if data_file is None:
return None
return loadRGB(data_file)
def postResize(self):
print " Post resize"
data_name = self._keyword
data_files = dataFiles(data_name)
for data_file in data_files:
data_filename = os.path.basename(data_file)
C_8U = loadRGB(data_file)
if C_8U is None:
os.remove(data_file)
print " - Delete: %s" % data_filename
continue
h, w = C_8U.shape[0:2]
opt_scale = 800.0 / float(h)
opt_scale = max(opt_scale, 800.0 / float(w))
opt_scale = min(opt_scale, 1.0)
h_opt = int(opt_scale * h)
w_opt = int(opt_scale * w)
C_8U_small = cv2.resize(C_8U, (w_opt, h_opt))
saveRGB(data_file, C_8U_small)
print " - Resized: %s" % data_filename
def createMultiImagePixels(data_name, data_ids):
rgb_pixels = []
num_cols = 3
num_rows = (len(data_ids) + 2) / num_cols
fig = plt.figure(figsize=(10, 7))
fig.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95, wspace=0.1, hspace=0.1)
font_size = 15
plot_id = 1
for data_id in data_ids:
image_file = dataFile(data_name, data_id)
image = loadRGB(image_file)
rgb_pixels.extend(ColorPixels(image).rgb())
fig.add_subplot(num_rows, num_cols, plot_id)
plt.imshow(image)
plt.axis("off")
plot_id += 1
rgb_pixels = np.array(rgb_pixels)
multi_image = np.array(rgb_pixels).reshape(1, -1, 3)
multi_tile = figure2numpy(fig)
return multi_image, multi_tile
def histogram3DResult(image_file, num_bins=32, image=None, tile=None):
image_name = os.path.basename(image_file)
if image is None:
image_name = os.path.basename(image_file)
image_name = os.path.splitext(image_name)[0]
image = loadRGB(image_file)
if tile is None:
tile = image
fig_w = 10
fig_h = 6
fig = plt.figure(figsize=(fig_w, fig_h))
fig.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95, wspace=0.02, hspace=0.2)
font_size = 15
fig.suptitle("Hisotogram 3D", fontsize=font_size)
h, w = image.shape[:2]
fig.add_subplot(231)
plt.title("Original Image: %s x %s" % (w, h), fontsize=font_size)
plt.imshow(tile)
plt.axis('off')
color_spaces = ["rgb", "Lab", "hsv"]
plot_id = 234
for color_space in color_spaces:
ax = fig.add_subplot(plot_id, projection='3d')
plotHistogram3D(image, num_bins, color_space, ax)
plot_id += 1
result_name = image_name + "_hist3D"
result_file = resultFile(result_name)
plt.savefig(result_file, transparent=True)
def createMultiImagePixels(data_name, data_ids):
rgb_pixels = []
num_cols = 3
num_rows = (len(data_ids) + 2) / num_cols
fig = plt.figure(figsize=(10, 7))
fig.subplots_adjust(left=0.05,
bottom=0.05,
right=0.95,
top=0.95,
wspace=0.1,
hspace=0.1)
font_size = 15
plot_id = 1
for data_id in data_ids:
image_file = dataFile(data_name, data_id)
image = loadRGB(image_file)
rgb_pixels.extend(ColorPixels(image).rgb())
fig.add_subplot(num_rows, num_cols, plot_id)
plt.imshow(image)
plt.axis('off')
plot_id += 1
rgb_pixels = np.array(rgb_pixels)
multi_image = np.array(rgb_pixels).reshape(1, -1, 3)
multi_tile = figure2numpy(fig)
return multi_image, multi_tile
def histogram3DResult(image_file, num_bins=32, image=None, tile=None):
image_name = os.path.basename(image_file)
if image is None:
image_name = os.path.basename(image_file)
image_name = os.path.splitext(image_name)[0]
image = loadRGB(image_file)
if tile is None:
tile = image
fig_w = 10
fig_h = 6
fig = plt.figure(figsize=(fig_w, fig_h))
fig.subplots_adjust(left=0.05,
bottom=0.05,
right=0.95,
top=0.95,
wspace=0.02,
hspace=0.2)
font_size = 15
fig.suptitle("Hisotogram 3D", fontsize=font_size)
h, w = image.shape[:2]
fig.add_subplot(231)
plt.title("Original Image: %s x %s" % (w, h), fontsize=font_size)
plt.imshow(tile)
plt.axis('off')
color_spaces = ["rgb", "Lab", "hsv"]
plot_id = 234
for color_space in color_spaces:
ax = fig.add_subplot(plot_id, projection='3d')
plotHistogram3D(image, num_bins, color_space, ax)
plot_id += 1
result_name = image_name + "_hist3D"
result_file = resultFile(result_name)
plt.savefig(result_file, transparent=True)
def histogram1DResult(image_file, num_bins=32, image=None, tile=None):
image_name = os.path.basename(image_file)
if image is None:
image_name = os.path.basename(image_file)
image_name = os.path.splitext(image_name)[0]
image = loadRGB(image_file)
if tile is None:
tile = image
fig_w = 10
fig_h = 6
fig = plt.figure(figsize=(fig_w, fig_h))
fig.subplots_adjust(left=0.1, bottom=0.1, right=0.9, top=0.95, wspace=0.3, hspace=0.2)
font_size = 15
fig.suptitle("Hisotogram 1D", fontsize=font_size)
h, w = image.shape[:2]
fig.add_subplot(231)
plt.title("Original Image: %s x %s" % (w, h), fontsize=font_size)
plt.imshow(tile)
plt.axis('off')
color_targets = [["Lab", 0], ["hsv", 0], ["hsv", 2]]
plot_id = 234
for color_target in color_targets:
ax = fig.add_subplot(plot_id)
color_space, channel = color_target
plotHistogram1D(image, num_bins, color_space, channel, ax)
plot_id += 1
result_name = image_name + "_hist1D"
result_file = resultFile(result_name)
plt.savefig(result_file, transparent=True)
# -*- coding: utf-8 -*-
# # @package color_histogram.examples.hist_1d
#
# Minimal example of 1D color histograms.
# @author tody
# @date 2015/08/29
from color_histogram.io_util.image import loadRGB
from color_histogram.core.hist_1d import Hist1D
import matplotlib.pyplot as plt
from color_histogram.datasets.datasets import dataFile
image_file = dataFile("flower", 0)
# Load image.
image = loadRGB(image_file)
# 16 bins, Lab color space, target channel L ('Lab'[0])
hist1D = Hist1D(image, num_bins=16, color_space='Lab', channel=0)
fig = plt.figure()
ax = fig.add_subplot(111)
hist1D.plot(ax)
plt.show()
# -*- coding:utf-8 -*-
import glob, os, shutil
import numpy as np
from color_histogram.io_util.image import loadRGB
from color_histogram.core.hist_1d import Hist1D
from color_histogram.core.hist_3d import Hist3D
import matplotlib.pyplot as plt
srcpath = "E:\\match_sample\\someone\\1494054534-9\\1494056641-0\\"
filelist = glob.glob(r'%s*.jpg' % srcpath)
for afile in filelist:
image = loadRGB(afile)
# 16 bins, Lab color space, target channel L ('Lab'[0])
#hist1D = Hist1D(image, num_bins=16, color_space='rgb')
hist3D = Hist3D(image, num_bins=64, color_space='rgb')
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
hist3D.plot(ax)
plt.show()
from color_histogram.io_util.image import loadRGB
from color_histogram.core.hist_3d import Hist3D
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
from scipy.spatial import ConvexHull
# Load image.
image = loadRGB('7.png')
# 16 bins, rgb color space
hist3D = Hist3D(image, num_bins=255, color_space='Lab')
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
hist3D.plot(ax)
vs = [[62.0070164688, -23.4659787223, -12.9463413357],
[100.409562663, 2.9859782616, -9.0728531732],
[-4.85583594611, -8.57160085891, -6.62002503029],
[52.7760787206, 74.0586148013, 69.0603637824],
[7.93947944138, 31.7299007512, 21.6140843711],
[17.979327872, 12.9425661512, -40.582473457],
[69.7839261866, -16.8646459746, -40.0210475383],
[109.763141803, -18.5892033919, -0.540175787924],
[2.59630991524, -13.0235296149, 15.9177894542],
[80.81699057114272, 10.765956215340807, 74.30941946766103]]
vs = np.asarray(vs)
hull = ConvexHull(vs)
ax.plot(vs.T[0], vs.T[1], vs.T[2], "ko")
for s in hull.simplices:
s = np.append(s, s[0]) # Here we cycle back to the first coordinate
