def inverse_pressure_images():
"""
Invert the values of the pressure images to benerate binary masks
"""
root = "../data/framed"
names = imt.get_imlist(root,ext ='.png')
for name in names:
if "pressure" in name:
# print name.split('_')[-5]
im = cv2.cvtColor(cv2.imread(name), cv2.COLOR_RGB2GRAY)
mask = np.zeros(im.shape, dtype=im.dtype)
cv2.imshow('input', im)
crop,_ = imt.cropMask(im, None, th=128)
mask = np.zeros(crop.shape, dtype=crop.dtype)
mask[crop>0]=1
mask[mask!=0]=0
mask = np.invert(mask)
#diff[diff!=1]=0
#diff =diff*255
crop = cv2.medianBlur(crop,3)
output = mask*crop
output = frameImage(output)
# var = mask
# print "mask: ",type(var), var.dtype, len(np.unique(var)), var.min(), var.max()
# cv2.imshow('mask', mask*255)
cv2.imwrite(name,output)
# var = output
# print "output: ",type(var), var.dtype, len(np.unique(var)), var.min(), var.max()
cv2.imshow('output', output)
cv2.waitKey(5)
def resize_rotate_and_frame():
print "Rotate and Frame Images"
srcpp = "../data/dataset"
dstpp = "../data/framed"
imnames = imt.get_imlist(srcpp, ext = ".png")
s=320
i=0
for imname in imnames:
filename = imname.split('\\')[-1]
## === OPERATORS ===
if 'dept' in imname:
im = cv2.imread(imname, -1)
else:
im = cv2.imread(imname)
b = imt.shrinkIm(im, s)
#rotate the image:
if 'right' in imname:
b = np.fliplr(cv2.transpose(b))
elif 'head' in imname:
b = cv2.flip(b, -1)
#frame the image
a = imt.frameImage(b)
dst = ("/").join([dstpp,filename])
print i, dst, b.shape
i+=1
cv2.imshow('rotated', a)
cv2.waitKey(5)
def replicate_and_rename_pressure_images():
mode = "pressure"
pp = "../data/preprocess"
actors = ["carlos", "chris", "victor", "lakshman", "norma"]
poses = ["bgn", "soldierU", "fallerU", "soldierD", "fallerD",
"logR", "yearnerR", "fetalR", "logL", "yearnerL", "fetalL"]
views = ["bottom"]#, "right", "head"]
B = {'ideal': ["medium","dark", "blanket"],
'pillow': ["blanketpillow"]
}
for actor in actors[:1]:
for srcscene in B.keys():
for dstscene in B[srcscene]:
for view in views:
for pose in poses [1:2]:
srcpath = ("/").join([pp,actor,mode,srcscene,view, pose])
print "path: ", srcpath
imlist = imt.get_imlist(srcpath, ext='.png')
if len(imlist)>0:
for src in imlist:
dst = src.replace(srcscene, dstscene)
print dst
shutil.copy(src, dst)
def adjust_dataset_names():
"""
Use this script to adjust the names of the imaegs in teh dataset.
old format: <actor>_<pose>_<scene>_<view>_<modality>_<idx>.png
** change** : <scene> :: <light>_<condition>
new format: <actor>_<pose>_<light>_<condition>_<view>_<modality>_<idx>.png
"""
root = "../data/framed"
imnames = imt.get_imlist(root,ext ='.png')
for src in imnames:
dst= src
if "ideal" in src:
dst = src.replace("ideal", "bright_clear")
elif "medium" in src:
dst = src.replace("medium", "medium_clear")
elif "dark" in src:
dst = src.replace("dark", "dark_clear")
elif "blanket" in src:
dst = src.replace("blanket", "medium_blanket")
elif "pillow" in src:
dst = src.replace("pillow", "medium_pillow")
elif "blanketpillow" in src:
dst = src.replace("blanketpillow", "medium_blanketpillow")
# if "carlos" in src:
# print "Old name: {} \t || New adjusted name: {}".format(src, dst)
print "Old name: {} \t || New adjusted name: {}".format(src, dst)
os.rename(src, dst)
def __init__(self, path, feature_type, vocab_size=800):
self.path = path
self.im_list = imtools.get_imlist(path)
self.vocab_size = vocab_size
self.feature_type = feature_type
self.logger = logging.getLogger(__name__)
self.load_model()
def __init__(self, path):
self.path = path
self.histograms = None
self.im_list = imtools.get_imlist(path)
self.all_scores=[]
self.logger = logging.getLogger(__name__)
self.load_histograms()
def __init__(self, path, feature_type, img_type='test', vocab_size=800):
self.im_list = imtools.get_imlist(imgs_path)
self.img_type = img_type
self.path = path
self.vocab_size = vocab_size
self.feature_type = feature_type
self.vocab_model = None
self.logger = logging.getLogger(__name__)
def add_tag_to_filename(tag = "_framed.png"):
newpp = "../data/framed"
imnames = imt.get_imlist(pp, ext = ".png")
s=320
i=0
for src in imnames:
dst = src.split('.')[0]
dst = dst+tag
os.rename(src, dst)
def __init__(self):
self.config = yaml.safe_load(open("../config.yml"))['preprocessing']
self.logger = logging.getLogger(__name__)
self.path = self.config['path']
self.experiment_name = self.config['experiment_name']
self.im_list = imtools.get_imlist(self.config['path'])
self.all_images_data = []
self.size = self.config['img_size']
self.datagen = ImageDataGenerator(featurewise_center=True,
featurewise_std_normalization=False)
def test4():
imlist = imtools.get_imlist(data_path)
averageim = imtools.compute_average(imlist)
figure()
imshow(averageim)
show()
def binarize_images_under_folder():
root = "../data/framed"
names = imt.get_imlist(root,ext ='.png')
for name in names:
if "binary" in name:
im = cv2.imread(name)
if len(np.unique(im)) > 2:
print "image: {} had {} values".format(name, len(np.unique(im) ))
im[im>0]=255
cv2.imwrite(name,im)
def __init__(self, path, distance_metric='chisquared'):
self.path = path
self.histograms = None
self.im_list = imtools.get_imlist(path)
self.all_scores = []
self.all_dist = []
self.logger = logging.getLogger(__name__)
self.load_histograms()
self.load_tfidf()
self.dist_function = dist.distance(distance_metric)
def __init__(self):
self.model = VGG16(weights='imagenet', include_top=True)
self.config = yaml.safe_load(
open("../config.yml"))['deep_feature_extraction']
self.logger = logging.getLogger(__name__)
self.path = self.config['path']
self.experiment_name = self.config['experiment_name']
self.im_list = imtools.get_imlist(self.config['path'])
self.all_images_data = []
self.size = self.config['img_size']
self.save_features = self.config['save_features']
self.datagen = ImageDataGenerator(featurewise_center=True,
featurewise_std_normalization=False)
def load_data(path):
""" 加载数据 """
imlist = imtools.get_imlist(path)
labels = [int(imfile.split('/')[-1][-5]) for imfile in imlist]
# create features from the images
features = []
for imname in imlist:
im = np.array(Image.open(imname).convert('L'))
features.append(compute_feature(im))
return np.array(features), labels
def Getdata():
global fe_arr
Set_dir = os.curdir + os.sep + "Set"
for chr in os.listdir(Set_dir):
img_list = imtools.get_imlist(Set_dir + os.sep + chr)
ExtractFt(img_list)
#fe_arr.save('f_data.npy')
#label_arr.save('l_data.npy')
fe_arr = fe_arr[1:]
#print fe_arr.size
#print label_arr.size
np.save('f_data.npy', fe_arr)
np.save('l_data.npy', label_arr)
return fe_arr,label_arr
def __init__(self):
self.config = yaml.safe_load(open("../config.yml"))['html']
self.path = self.config['path']
self.histograms = None
self.im_list = imtools.get_imlist(self.config['path'])
self.all_scores=[]
self.logger = logging.getLogger(__name__)
self.html= None
self.all_dist = []
self.all_data = []
#jinja 2
template_dir = os.path.dirname(os.path.abspath(__file__))
env = Environment(loader=FileSystemLoader(template_dir + "/templates"),trim_blocks=True, autoescape=select_autoescape(enabled_extensions=('html', 'xml'), default_for_string=True))
self.template = env.get_template('annotate.html')
self.load_comparison()
def load_data(path):
""" 加载数据 """
imlist = imtools.get_imlist(path)
# imlist = [
# os.path.join(path, f) for f in os.listdir(path) if f.endswith('.bmp')
# ]
labels = [int(imfile.split('/')[-1][-5]) for imfile in imlist]
# create features from the images
features = []
for imname in imlist:
im = np.array(Image.open(imname).convert('L'))
features.append(compute_feature(im))
return np.array(features), labels
def __init__(self):
self.imlist = imtools.get_imlist(
'/Users/thakis/Downloads/ukbench/first1000')[:100]
self.ndx = range(len(self.imlist))
with open('vocabulary.pkl', 'rb') as f:
self.voc = pickle.load(f)
self.maxresults = 15
self.header = """\
<!doctype html>
<html>
<head><title>Image search example</title></head>
<body>"""
self.footer = """\
def generate_synthetic_dark_bright_depth_pressure_scenes():
"""
Copy with new label (Illumination invariant) scenes
"""
root = "../data/framed"
modality = "depth" #"pressure"
imnames = imt.get_imlist(root,ext ='.png')
for src in imnames:
if("medium" in src) and ("binary" in src) and not ("clear" in src):
dst_bri = src.replace("medium", "bright")
dst_drk = src.replace("medium", "dark")
# med = cv2.cvtColor(med, cv2.COLOR_RGB2GRAY)
print "NAMES medium:{} \t brigh:{} \t dark:{}".format(src, dst_bri, dst_drk)
shutil.copy(src, dst_bri)
shutil.copy(src, dst_drk)
def correct_rotation_of_right_view(dstpp="../data/framed"):
print "Correct Rotation of Right View"
imnames = imt.get_imlist(dstpp, ext = ".png")
s=320
i=0
for imname in imnames:
filename = imname.split('\\')[-1]
if 'right' in filename:
## === OPERATORS ===
if 'dept' in imname:
im = cv2.imread(imname, -1)
else:
im = cv2.imread(imname)
#rotate the image:
b = np.fliplr(im)
print i, imname, b.shape
i+=1
cv2.imwrite(imname, b)
cv2.imshow('rotated', b)
cv2.waitKey(5)
def resize_and_copy_images():
"""Resize all images and copy to new location """
print "Resize and Copy Images"
pp = "../data/mm_sleeping_poses_dataset"
newpp = "../data/framed"
imnames = imt.get_imlist(pp, ext = ".png")
for imname in imnames:
## === OPERATORS ===
## copy all files to single location
filename = imname.split('\\')[-1] #.split('.')[0]
#filename = filename+'_resized.png'
im = cv2.imread(imname, -1)
dst = ("/").join([newpp,filename])
cv2.imwrite(dst, r_im)
# shutil.copy(imname, dst)
print "\n src:{} \n dst:{} \n name: {}".format(imname, dst,filename)
import matplotlib.pyplot as plt
import imtools
import pca
import pickle
from scipy.ndimage import filters, measurements, morphology
import scipy.io
from scipy.misc import imsave
from numpy import random
import rof
# reload(imtools)
### 1.3.6 画像の主成分分析 ###
# pca.pyを作成する
imlist = imtools.get_imlist("./a_thumbs") # 相対アドレスを使っている。右から開くということで
im = np.array(Image.open(imlist[0]))
# サイズを得るため画像を１つ開く
m, n = im.shape[0:2] # 画像のサイズを得る
imnbr = len(imlist) # 画像数を得る
# すべての平板化画像を格納する行列を作る
immatrix = np.array([np.array(Image.open(img)).flatten() for img in imlist], "f")
# 主成分分析を実行する
V, S, immean = pca.pca(immatrix)
### 1.3.7 pickleモジュール ###
# 平均と主成分を保存する
from PIL import Image
from numpy import *
from pylab import *
from imtools import pca, get_imlist
imlist = get_imlist('../data/a_thumbs')
print(imlist)
im = array(Image.open(imlist[0]))
m, n = im.shape[0:2]
imnbr = len(imlist)
immatrix = array([array(Image.open(im)).flatten() for im in imlist], 'f')
V, S, immean = pca(immatrix)
figure()
gray()
subplot(2, 4, 1)
imshow(immean.reshape(m, n))
for i in range(7):
subplot(2, 4, i+2)
imshow(V[i].reshape(m, n))
show()
from PIL import Image
from numpy import *
import imtools
import pca
import os
imlist = imtools.get_imlist("data/a_thumbs")
im = array(Image.open(imlist[0]))
m,n = im.shape[0:2]
imnbr = len(imlist)
immatrix = array([array(Image.open(im)).flatten() for im in imlist], 'f')
V,S,immean = pca.pca(immatrix)
pil_im = Image.fromarray(uint8(immean.reshape(m,n)))
try:
pil_im.save("./pca.jpg")
except IOError:
print "cannot save", "pca.jpg"
for i in range(7):
tmp = (V[i] - min(V[i])) / (max(V[i]) - min(V[i])) * 255
pil_im = Image.fromarray(uint8(tmp.reshape(m,n)))
print min(V[i]), max(V[i])
print tmp.reshape(m,n)
try:
pil_im.save("./pca-" + str(i) + ".jpg")
except IOError:
print "cannnot save", "pca-" + str(i) + ".jpg"
# -*- coding: utf-8 -*-
"""
Created on Sat Apr 19 22:57:50 2014
@author: ray
"""
import imtools
from PIL import Image
import pca
from numpy import *
close('all')
# generate data
imlist = imtools.get_imlist('../data/a_selected_thumbs/')
imnbr = len(imlist)
immatrix = array([array(Image.open(im)).flatten() for im in imlist],'f')
V,S,immean = pca.pca(immatrix)
immean = immean.flatten()
projected = array([dot(V[0:40],immatrix[i]-immean) for i in range(imnbr)])
from scipy.cluster.vq import *
n = len(projected)
# compute distance matrix
S = array([[ sqrt(sum((projected[i]-projected[j])**2)) for i in range(n) ] for j in range(n)], 'f')
# create Laplacian matrix
rowsum = sum(S,axis=0)
D = diag(1 / sqrt(rowsum))
I = identity(n)
L = I - dot(D,dot(S,D))
# Using the Pickle module
# Saving results or data for later use.
# Pickle can take almost any Python object and convert it to a string representation, and back (unpickling)
from PIL import Image
from numpy import *
from pylab import *
import imtools
import pca
import pickle
imlist = imtools.get_imlist("data/fontimages/", "jpg")
im = array(Image.open(imlist[0])) # open one image to get size
m, n = im.shape[0:2] # get the size of the images
imnbr = len(imlist) # get the number of images
# create matrix to store all flattened images
immatrix = array([array(Image.open(im)).flatten() for im in imlist], 'f')
# perform PCA
V, S, immean = pca.pca(immatrix)
# show some images (mean and 7 first modes)
figure()
gray()
subplot(2, 4, 1)
imshow(immean.reshape(m, n))
for i in range(7):
subplot(2, 4, i + 2)
import imtools
import os
train_feat_path = '../trainfeature/'
test_feat_path = '../testfeature/'
trainlist = imtools.get_imlist(train_feat_path)
testlist = imtools.get_imlist(test_feat_path)
zcount = 0
for fp in trainlist:
if os.path.getsize(fp) == 0:
zcount = zcount + 1
for fp in testlist:
if os.path.getsize(fp) == 0:
zcount = zcount + 1
print zcount
#!/usr/bin/python
# -*- coding: utf-8 -*-
import sys
sys.path.append('C:/workPyth/Mypython/cv')
from PIL import Image
from numpy import *
from pylab import *
import sift
import imtools
import os
from os.path import basename, splitext
im_path = os.getcwd()
imlist = imtools.get_imlist('nail_book')
featlist = []
for imname in imlist:
name, ext = splitext(basename(imname))
sname = name + '.sift'
sift.process_image(imname, sname)
featlist.append(sname)
nbr_images = len(imlist)
matchscores = zeros((nbr_images, nbr_images))
for i in xrange(nbr_images):
for j in xrange(i, nbr_images): # 上三角成分だけを計算する
print 'comparing ', imlist[i], imlist[j]
import Image
import sys
sys.path.append('utils')
import imtools
import os
import pickle
from scipy.cluster.vq import *
f = open(sys.argv[1]+'_histograms.pickle', 'r')
histograms = pickle.load(f)
f.close()
path = sys.argv[1]
nbr_images = imtools.get_imlist(path)
#word, the index of 200 in the histogram
#im, the histogram of the specific image
#histograms, the list of histograms
#tf(word, histogram) computes "term frequency" which is the number of times a word appears in a image
def tf(word, im):
return float(word) / sum(im)
#n_containing(word, histograms) returns the number of documents containing word.
def n_containing(wordidx, word, histograms):
noOfContainingIms=[]
count=0
for idx,im in enumerate(histograms):
from PIL import Image
import numpy as np
import pandas as pd
import posixpath
import matplotlib.pyplot as plt
import imtools
parsed_coord = open(posixpath.abspath('parsed_data/params_res.txt'))
parsed_coord = parsed_coord.readlines()
res = []
for line in parsed_coord:
res.append(line.split())
df = pd.DataFrame(res)
image_names = imtools.get_imlist(posixpath.abspath('parsed_data/imgs'))
image = Image.open(image_names[2])
image_arr = np.array(image)
plt.figure(1)
plt.imshow(image_arr)
plt.plot(df[df['Filename'] == image_names[2]].CenX,
df[df['Filename'] == image_names[2]].CenY, 'r*')
plt.show()
# -*- coding: utf-8 -*-
import imtools
import pickle
from scipy.cluster.vq import *
if __name__ == '__main__':
# 画像のリスト取得
imlist = imtools.get_imlist('/usr/local/git_local/python_study/oreilly_computer_vision/data/PIXLEE-computer-vision-clustering/data/a_selected_thumbs')
imnbr = len(imlist)
print imnbr
# モデルのファイルを読み込む
with open('')
import cPickle as pickle
import homography
import imtools
import sift
import imagesearch
"""After ch07_buildindex.py has built an index in test.db, this program
queries it, and fits a homography to improve query results.
"""
imlist = imtools.get_imlist('/Users/thakis/Downloads/ukbench/first1000')[:100]
imcount = len(imlist)
featlist = [imlist[i][:-3] + 'sift' for i in range(imcount)]
with open('vocabulary.pkl', 'rb') as f:
voc = pickle.load(f)
searcher = imagesearch.Searcher('test.db', voc)
query_imid = 50
res_count = 20
res = [w[1] for w in searcher.query(imlist[query_imid])[:res_count]]
print 'regular results for query %d:' % query_imid, res
# Rerank by trying to fit a homography.
q_locs, q_descr = sift.read_features_from_file(featlist[query_imid])
fp = homography.make_homog(q_locs[:, :2].T)
model = homography.RansacModel()
subplot(223)
imshow(im3,cmap=cm.Greys_r)
subplot(224)
imshow(im4,cmap=cm.Greys_r)
figure(4)
im5,cdf = imtools.histeq(pil_img)
subplot(221)
imshow(pil_img,cmap=cm.Greys_r)
subplot(222)
imshow(im5,cmap=cm.Greys_r)
subplot(223)
plot(cdf)
###########################################################
close("all")
# pca
imlist = imtools.get_imlist('gwb_cropped')
im = array(Image.open(imlist[0]))
m,n = im.shape[0:2]
imnbr = len(imlist)
immatrix = array([array(Image.open(im)).flatten() for im in imlist],'f')
# perform PCA
V,S,immean= imtools.pca(immatrix)
figure()
subplot(2,4,1)
imshow(immean.reshape(m,n))
for i in range(7):
subplot(2,4,i+2)
value = myarray[x][k]
if value == 0:
row.append(0)
else:
row.append(value/value_max)
new_array.append(row)
row =[]
new_array = asarray(new_array)
return new_array
# get list of images
imlist = imtools.get_imlist('../img/img_test/')
nbr_images = len(imlist)
con = sqlite.connect('test.db')
matchscores = []
row = []
a = 0
for i in range(nbr_images):
filename1 = imlist[i]
imid1 = con.execute("select rowid from imlist where filename='%s'" % filename1 ).fetchone()
cand_h1 = con.execute("select histogram from imhistograms where rowid='%d'" % imid1).fetchone()
cand_h1 = pickle.loads(str(cand_h1[0]))
for j in range(nbr_images):
#!/usr/bin/python
# -*- coding: utf-8 -*-
import imtools
imlist = sorted(imtools.get_imlist('first1000'))
#imlist = imlist[:100] # for small memory
import pickle
import vocabulary
nbr_images = len(imlist)
featlist = [ imlist[i][:-3]+'sift' for i in range(nbr_images) ]
voc = vocabulary.Vocabulary('ukbenchtest')
voc.train(featlist,1000,10)
# ボキャブラリを保存する
with open('vocabulary.pkl', 'wb') as f:
pickle.dump(voc,f)
print 'vocabulary is:', voc.name, voc.nbr_words
centroids, variance = kmeans(features,2)
code, distance = vq(features,centroids)
plt.figure()
ndx = np.where(code==0)[0]
plt.plot(features[ndx,0],features[ndx,1],'*')
ndx = np.where(code==1)[0]
plt.plot(features[ndx,0],features[ndx,1],'r.')
plt.plot(centroids[:,0],centroids[:,1],'go')
plt.axis('off')
#6.1.2
# 画像のリストを得る
imlist = imtools.get_imlist('selected_fontimages/')
imnbr = len(imlist)
# モデルのファイルを読み込む
with open('font_pca_modes.pkl','rb') as f:
immean = pickle.load(f)
V = pickle.load(f)
# 平板化した画像を格納する行列を作る
immatrix = np.array([np.array(Image.open(im)).flatten()
for im in imlist],'f')
# 第40主成分までを射影する
immean = immean.flatten()
projected = np.array([np.dot(V[:40],immatrix[i]-immean)
for i in range(imnbr)])
from PIL import Image
from numpy import *
from pylab import *
import pca, imtools
imlist = imtools.get_imlist('corpus')
im = array(Image.open(imlist[0]))
m,n = im.shape[0:2]
imnbr = len(imlist)
for i in range(imnbr):
tmpArr = [array(Image.open(imlist[i])).flatten()]
immatrix = array(tmpArr, 'f')
V,S,immean = pca.pca(immatrix)
pil_im_contr = Image.open('empire.jpg').convert('L')
pil_im.show()
pil_im_contr.show()
#Save usage with jpg
pil_im_contr.save("empire_grayscale.jpg", "jpeg")
#Copy image
pil_im_cp = pil_im.copy()
#Thumbnail usage
pil_im_contr.thumbnail((128, 128))
pil_im_contr.show()
#List all jpg images in directory
l_images = get_imlist(os.getcwd())
#Crop the image
rect = (100, 100, 400, 400)
rect_region = pil_im.crop(rect)
rect_region.show()
#rotate image, paste it
rect_region = rect_region.transpose(Image.ROTATE_180)
rect_region.show()
pil_im.paste(rect_region, rect)
pil_im.show()
#Resize and rotate
pil_im_resized = pil_im_cp.resize((256, 256))
pil_im_resized.show("Resized")
# coding: utf-8
import imtools
raw_name_list = imtools.get_imlist("../animals/") #使用する元画像の名前のリストを作る
#processed_name_list = ["../processed_pictures/" + raw_name for raw_name in range(193)] #加工後画像の名前のリストを作る
processed_name_list = []
for i in range(len(raw_name_list)):
processed_name_list.append("../animals/" + str(i) + ".jpg")
print processed_name_list
for raw_picture_name, processed_picture_name in zip(raw_name_list, processed_name_list):
print 'a'
imtools.resize(raw_picture_name, processed_picture_name, 500, 500) #画像のサイズ変更
vis_roi = vis[y0:y1, x0:x1]
cv2.bitwise_not(vis_roi, vis_roi) # 色の反転
vis[mask == 0] = 0
prob = cv2.calcBackProject([hsv], [0], self.hist, [0, 180], 1)
prob &= mask
term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )
#track_box, self.track_window = cv2.CamShift(prob, self.track_window, term_crit)
if self.show_backproj:
vis[:] = prob[...,np.newaxis]
"""
try: cv2.ellipse(vis, track_box, (0, 0, 255), 2)
except: print track_box
"""
cv2.imshow('camshift', vis)
ch = cv2.waitKey(5)
#cv2.destroyAllWindows()
if ch == 27:
break
if ch == ord('b'):
self.show_backproj = not self.show_backproj
if __name__ == '__main__':
import sys
import imtools
imlist = imtools.get_imlist('nail_book03')
App(imlist).run()
import imtools
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
filelist = imtools.get_imlist('../stp_log/image/', 'jpg')
for fname in filelist:
img=mpimg.imread(fname) #image to array
plt.imshow(img) #array to 2Dfigure
plt.show()
# -*- coding: utf-8 -*-
import imagesearch
import imtools
imlist = sorted(imtools.get_imlist('nail_book03'))
from svm import *
from svmutil import *
import os
import cv2
import cv2.cv as cv
import sqlite3 as sqlite
import pickle
import numpy as np
import csv
import random
import math
from scipy.stats import norm
from copy import deepcopy
execfile('loaddata.py')
path = 'nail_book03'
train_csvfile = 'train_labels04.csv'
test_csvfile = 'test_labels04.csv'
csvfile = 'train_labels.csv'
HSV_fname = 'fHSV_hist.txt'
hsv_flag_fname = 'hsv_flag.csv'
src = imagesearch.Searcher('nail_image500.db', voc)
def main_boost():
import hcluster
import imtools
from PIL import Image
from pylab import *
imlist = imtools.get_imlist('/Users/thakis/Downloads/data/flickr-sunsets-small')
# extract histogram as feature vector (8 bins per color channel)
features = zeros([len(imlist), 512])
for i, f in enumerate(imlist):
im = array(Image.open(f))
h, edges = histogramdd(im.reshape(-1, 3), 8, normed=True,
range=[(0,255), (0, 255), (0, 255)])
features[i] = h.flatten()
tree = hcluster.hcluster(features)
hcluster.draw_dendrogram(tree, imlist, filename='out_sunset.png')
# visualize clusters
clusters = tree.extract_clusters(dist=0.23 * tree.distance)
for c in clusters:
elements = c.get_cluster_elements()
if len(elements) > 3:
figure()
for p in range(minimum(len(elements), 20)):
subplot(4, 5, p + 1)
im = array(Image.open(imlist[elements[p]]))
imshow(im)
axis('off')
show()
from PIL import Image
# from PCV.localdescriptors import sift
# from PCV.tools import imtools
import sift
import imtools
import pydot
import os
""" This is the example graph illustration of matching images from Figure 2-10.
To download the images, see ch2_download_panoramio.py."""
parent_path = os.path.abspath(
os.path.join(os.path.dirname("__file__"), os.path.pardir))
download_path = parent_path + '/data/panoimages/' # set this to the path where you downloaded the panoramio images
path = parent_path + '/data/panoimages/thumbnails/' # path to save thumbnails (pydot needs the full system path)
# list of downloaded filenames
imlist = imtools.get_imlist(download_path)
nbr_images = len(imlist)
# extract features
featlist = [imname[:-3] + 'sift' for imname in imlist]
matchscores = zeros((nbr_images, nbr_images))
for i in range(nbr_images):
for j in range(i, nbr_images): # only compute upper triangle
# print('comparing ', imlist[i],imlist[j])
# process and save features to file
sift.process_image(imlist[i], featlist[i])
l1, d1 = sift.read_features_from_file(featlist[i])
sift.process_image(imlist[j], featlist[j])
l2, d2 = sift.read_features_from_file(featlist[j])
matches = sift.match_twosided(d1, d2)
import cPickle as pickle
import homography
import imtools
import sift
import imagesearch
"""After ch07_buildindex.py has built an index in test.db, this program
queries it, and fits a homography to improve query results.
"""
imlist = imtools.get_imlist("/Users/thakis/Downloads/ukbench/first1000")[:100]
imcount = len(imlist)
featlist = [imlist[i][:-3] + "sift" for i in range(imcount)]
with open("vocabulary.pkl", "rb") as f:
voc = pickle.load(f)
searcher = imagesearch.Searcher("test.db", voc)
query_imid = 50
res_count = 20
res = [w[1] for w in searcher.query(imlist[query_imid])[:res_count]]
print "regular results for query %d:" % query_imid, res
# Rerank by trying to fit a homography.
q_locs, q_descr = sift.read_features_from_file(featlist[query_imid])
fp = homography.make_homog(q_locs[:, :2].T)
model = homography.RansacModel()
import imtools
import pca
from PIL import Image
from pylab import *
from scipy.cluster.vq import *
# PCA on all images.
imlist = imtools.get_imlist('/Users/thakis/Downloads/data/a_thumbs')
imcount = len(imlist)
immatrix = array([array(Image.open(im)).flatten() for im in imlist], 'f')
V, S, immean = pca.pca(immatrix)
# Visualize only selected images.
imlist = imtools.get_imlist('/Users/thakis/Downloads/data/a_selected_thumbs')
imcount = len(imlist)
immatrix = array([array(Image.open(im)).flatten() for im in imlist], 'f')
# Project on 40 first PCs.
projected = array([dot(V[:40], immatrix[i] - immean) for i in range(imcount)])
# spectral clustering
n = len(projected)
# compute distance matrix
S = array([[sqrt(sum((projected[i] - projected[j])**2)) for i in range(n)]
for j in range(n)], 'f')
# create Laplacian matrix
# (See "A Comparison of Spectral Clustering Algorithms", NJW algorithm
rowsum = sum(S, axis=0)
D = diag(1 / sqrt(rowsum))
# -*- coding: utf-8 -*-
"""
Created on Fri Sep 02 19:26:16 2016
@author: user
"""
import sys
sys.path.append('../ch1/')
import imtools
from numpy import *
from PIL import Image
import pickle
from scipy.cluster.vq import *
imlist = imtools.get_imlist(
'../pcv_data/data/selectedfontimages/a_selected_thumbs/')
imnbr = len(imlist)
# load model file
with open('a_pca_modes.pkl', 'rb') as f:
immean = pickle.load(f)
V = pickle.load(f)
# create matrix to store all flattened images
immatrix = array([array(Image.open(im)).flatten() for im in imlist], 'f')
# project on the 40 first PCs
immean = immean.flatten()
projected = array([dot(V[[0, 1]], immatrix[i] - immean) for i in range(imnbr)])
n = len(projected)
# compute distance matrix
S = array([[sqrt(sum((projected[i] - projected[j])**2)) for i in range(n)]
import imtools
import pickle
from PIL import Image
from scipy.cluster.vq import *
from pylab import *
# 获取 selected-fontimages 文件下图像文件名，并保存在列表中
imlist = imtools.get_imlist('../data/fontimages/a_thumbs/')
imnbr = len(imlist)
# 载入模型文件
# with open('a_pca_modes.pkl','rb') as f:
with open('../data/fontimages/font_pca_modes.pkl', 'rb') as f:
immean = pickle.load(f)
V = pickle.load(f)
# 创建矩阵，存储所有拉成一组形式后的图像
immatrix = array([array(Image.open(im)).flatten() for im in imlist], 'f')
# 投影到前 40 个主成分上
immean = immean.flatten()
projected = array([dot(V[:40], immatrix[i] - immean) for i in range(imnbr)])
# 进行 k-means 聚类
projected = whiten(projected)
centroids, distortion = kmeans(projected, 4)
code, distance = vq(projected, centroids)
# 绘制聚类簇
for k in range(4):
from scipy.cluster.vq import *
import numpy as np
from pylab import *
from PIL import Image
import imtools,pca
import pickle
# 获取 selected-fontimages 文件下图像文件名，并保存在列表中
imlist = imtools.get_imlist('C:\\Users\\msi\\Desktop\\selectedfontimages\\a_selected_thumbs\\')
imnbr = len(imlist)
# 载入模型文件
# 创建矩阵，存储所有拉成一组形式后的图像
immatrix = np.array([np.array(Image.open(im)).flatten() for im in imlist],'f')
V, S, immean = pca.pca(immatrix)
# 投影到前 40 个主成分上
projected = np.array([np.dot(V[[0,1]],immatrix[i]-immean) for i in range(imnbr)])
# projected = whiten(projected)
# centroids,distortion = kmeans(projected,2)
# code,distance = vq(projected,centroids)
# for k in range(2):
# ind = where(code==k)[0]
# figure()
# gray()
# for i in range(minimum(len(ind),40)):
# subplot(4,10,i+1)
# imshow(immatrix[ind[i]].reshape((25,25)))
from sklearn.cluster import KMeans
from sklearn.cluster import MiniBatchKMeans
class vocabulary:
def __init__(self, img_type, vocab_size=800, feature):
self.img_type = img_type
self.vocab_size = vocab_size
self.feature = feature
if __name__ == '__main__':
path = sys.argv[1]
vocab_size = 800
init_size = vocab_size * 3
im_list = imtools.get_imlist(path)
all_features = []
for idx, image_name in enumerate(im_list):
gray_img = rgb2gray(np.array(Image.open(image_name)))
features = daisy(gray_img, step=8)
all_features.append(features.reshape(-1, 200))
X = np.vstack(all_features)
km = MiniBatchKMeans(n_clusters=vocab_size, max_iter=200, verbose=1)
km.fit(X)
f = open(sys.argv[1] + '_vocab.pickle', 'w')
pickle.dump(km, f)
f.close()
from PIL import Image
import os
import imtools
import imtools
im_paths = imtools.get_imlist(os.getcwd())
im1 = Image.open(im_paths[0])
im2 = Image.open(im_paths[1])
#grayscale
im1_gs = im1.convert('L')
im2_gs = im2.convert('L')
for infile in im_paths:
outfile = os.path.splitext(infile)[0] + ".jpg"
if infile != outfile:
try:
Image.open(infile).save(outfile)
except IOerror:
print("can't convert", infile)
# coding: utf-8
import imtools
raw_name_list = imtools.get_imlist("../raw_pictures/") #使用する元画像の名前のリストを作る
processed_name_list = ["../processed_pictures/" + raw_name.split('/')[2] for raw_name in raw_name_list] #加工後画像の名前のリストを作る
for raw_picture_name, processed_picture_name in zip(raw_name_list, processed_name_list):
imtools.resize(raw_picture_name, processed_picture_name, 50, 50) #画像のサイズ変更
return num_flag
def draw_image(filename, wrfname):
image_res = 200 * 200
imagebit = read_target(filename, "camcap:camcap_1|data_in_R[7..0]")
imagebit = hex_dec(imagebit)
trigger = read_target(filename, "camcap:camcap_1|trig_0_R")
start_flag = detect_trigger(trigger)
if len(start_flag) == 0:
print("can't find flag\nexit.........")
exit(1)
frame = imagebit[start_flag[0] + 1:start_flag[0] + image_res + 1]
frame = np.array(frame)
frame = frame.reshape((200, 200))
print(frame)
pilout = Image.fromarray(np.uint8(frame))
pilout.save(wrfname)
# def cap_imageSequences(filename, wrfname):
if __name__ == '__main__':
filelist = imtools.get_imlist('../stp_log/', 'csv')
dir_path, fname = os.path.split(filelist[-1])
name, ext = os.path.splitext(fname)
wrfname = dir_path + "/image/" + name + ".jpg"
print(wrfname)
draw_image(filelist[-1], wrfname)
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 31 16:19:28 2016
@author: user
"""
from PIL import Image
from pylab import *
from numpy import *
sys.path.append('../ch1/')
import imtools, pca
# Get list of images and their size
imlist = imtools.get_imlist(
'../pcv_data/data/selectedfontimages/a_selected_thumbs/'
) # fontimages.zip is part of the book data set
im = array(Image.open(imlist[0])) # open one image to get the size
m, n = im.shape[:2]
# Create matrix to store all flattened images
immatrix = array([array(Image.open(imname)).flatten() for imname in imlist],
'f')
# Perform PCA
V, S, immean = pca.pca(immatrix)
# Show the images (mean and 7 first modes)
# This gives figure 1-8 (p15) in the book.
figure()
gray()
def main():
imlist = it.get_imlist(PATH)
for img_path in imlist:
original = preparing_img(img_path)
detecting(original)
#!/usr/bin/python
# -*- coding: utf-8 -*-
from pylab import *
import imtools
for dir in ('jkfaces2008_small', 'jkfaces2008_small/aligned'):
imlist = imtools.get_imlist(dir)
avgimg = imtools.compute_average(sorted(imlist)[:150])
figure()
imshow(avgimg)
gray()
axis('off')
title(dir)
show()
import pickle
import vocabulary
import imtools
import sift
# get list of images
imlist = imtools.get_imlist('../img/sunsets/treino/')
nbr_images = len(imlist)
featlist = [ imlist[i][:-3]+'sift' for i in range(nbr_images) ]
#create vocabulary
voc = vocabulary.Vocabulary('test')
voc.train(featlist,1000,10)
# saving vocabulary
with open('vocabulary.pkl', 'wb') as f:
pickle.dump(voc,f)
print 'vocabulary is:', voc.name, voc.nbr_words
import warp
#reload(sift)
reload(homography)
reload(imtools)
"""前回のプログラム"""
#3.2.3
xmlFileName = 'jkfaces2008_small/jkfaces.xml'
points = imregistration.read_points_from_xml(xmlFileName)
#位置合わせする
imregistration.rigid_alignment(points,'jkfaces2008_small/')
""" """
# 平均画像の比較
imlist = imtools.get_imlist('jkfaces2008_small/')
avgim = imtools.compute_average(imlist)
plt.subplot(1,2,1)
plt.imshow(avgim)
plt.axis('off')
imlist = imtools.get_imlist('jkfaces2008_small/aligned/')
avgim_aligned = imtools.compute_average(imlist)
plt.subplot(1,2,2)
plt.imshow(avgim_aligned)
plt.axis('off')
#マスクの作成
imlist = imtools.get_imlist('jkfaces2008_small/')
im = np.array(Image.open(imlist[0]))
m,n = im.shape[0:2]
#im.shape[0:1]->(400L,)となり、カンマまでになってしまうので[0:2]までにした
