def image_calc(img):
try:
#img = cv2.imdecode(str2
#img = cv2.imread(str2)
correct_fea = Gist_feat_last.singleImage2(img)
feat = HOG_feat2.hog_call(img)
#print 'hog',feat.shape
#print 'correct shape',correct_fea.shape
Label_classify2(feat,'batman')
Label_classify(correct_fea,'batman')
print 'list',list1
print '2nd list',list2
for file12 in files_name:
list3[file12] = []
names = list1[file12]
names2 = list2[file12]
l1 = list(set(names).intersection(names2))
l1 = remove_num(l1)
l1=list(set(l1))
list3[file12] = l1
print 'last',list3
return list3
except Exception,e:
return 'Image not found'
def single_affine(img,name):
#img = cv2.imread(path)
#modify_database(name)
img = imresize(img, (47*2, 55*2), interp = 'bicubic')
cv2.imwrite("/root/ideaswirw/imageprocessing/DataBase/1.png",img)
#print 'image is',img
#print img.shape
histogram.add_hist(img, name)
gist_feat = Gist_feat_last.singleImage2(img)
hog_feat = HOG_feat2.hog_call(img)
gist_feat = np.concatenate((gist_feat,[1]))
hog_feat = np.concatenate((hog_feat,[1]))
with open(feat_gist+name+'.csv', 'a') as myfile:
wr = csv.writer(myfile, quoting=csv.QUOTE_ALL)
wr.writerow(gist_feat)
with open(feat_hog+name+'.csv', 'a') as myfile:
wr = csv.writer(myfile, quoting=csv.QUOTE_ALL)
wr.writerow(hog_feat)
'''
add_feature_to_database(name,gist_feat,"Gabor")
add_feature_to_database(name,hog_feat,"HOG")
'''
ret = generate_affine(img,name)
return ret
def image_calc(img):
img = imresize(img, (47 * 2, 55 * 2), interp='bicubic')
correct_fea = Gist_feat_last.singleImage2(img)
feat = HOG_feat2.hog_call(img)
orig_hog = Label_classify2(feat, 'batman')
orig_gist = Label_classify(correct_fea, 'batman')
orig_res = gen_res(orig_hog, orig_gist)
af_img = afine_search.affine_transform(img)
af_gist = Gist_feat_last.singleImage2(af_img)
af_hog = HOG_feat2.hog_call(af_img)
aff_hog = Label_classify2(feat, 'batman')
aff_gist = Label_classify(correct_fea, 'batman')
af_res = gen_res(aff_hog, aff_gist)
final_res = list(set(orig_res).intersection(af_res))
#final_response = histogram.search_hist(final_res, img)
print final_res
print orig_res
print af_res
print orig_hog
print orig_gist
print aff_hog
print aff_gist
new_hog, hog_weig = make_final_hog(aff_hog, orig_hog)
new_gist, gist_weig = make_final_hog(aff_gist, orig_gist)
if hog_weig > gist_weig:
return new_hog
elif hog_weig < gist_weig:
if gist_weig > 1:
return new_gist
else:
return []
elif hog_weig == gist_weig:
temp = []
for x in new_hog:
temp.append(x)
for x in new_gist:
temp.append(x)
return temp
def image_calc(img):
img = imresize(img, (47*2, 55*2), interp = 'bicubic')
correct_fea = Gist_feat_last.singleImage2(img)
feat = HOG_feat2.hog_call(img)
orig_hog = Label_classify2(feat,'batman')
orig_gist = Label_classify(correct_fea,'batman')
orig_res = gen_res(orig_hog, orig_gist)
af_img = afine_search.affine_transform(img)
af_gist = Gist_feat_last.singleImage2(af_img)
af_hog = HOG_feat2.hog_call(af_img)
aff_hog = Label_classify2(feat,'batman')
aff_gist = Label_classify(correct_fea,'batman')
af_res = gen_res(aff_hog, aff_gist)
final_res = list(set(orig_res).intersection(af_res))
final_response = histogram.search_hist(final_res, img)
return final_response
def single_affine(img, name):
#img = cv2.imread(path)
#modify_database(name)
gist_feat = Gist_feat_last.singleImage2(img)
hog_feat = HOG_feat2.hog_call(img)
gist_feat = np.concatenate((gist_feat, [1]))
hog_feat = np.concatenate((hog_feat, [1]))
with open(feat_gist + name + '.csv', 'a') as myfile:
wr = csv.writer(myfile, quoting=csv.QUOTE_ALL)
wr.writerow(gist_feat)
with open(feat_hog + name + '.csv', 'a') as myfile:
wr = csv.writer(myfile, quoting=csv.QUOTE_ALL)
wr.writerow(hog_feat)
'''
add_feature_to_database(name,gist_feat,"Gabor")
add_feature_to_database(name,hog_feat,"HOG")
'''
ret = generate_affine(img, name)
return ret
def generate_affine(img, name):
#for i in range(10):
for i in range(30):
#padd the image
img1 = padd(img)
#cv2.imshow('after concatat',img1)
#cv2.waitKey()
gamma_1 = random_1()
gamma_main = random.randrange(1, 3, 1)
gamma_last = gamma_main + gamma_1
noise_addition = adjust_gamma(
img1, gamma=gamma_last) #change the gammmaa of the image randomly
noise_ad2 = sp.noise_addition(
noise_addition) #add the salt and peppr noise to the image
pts1 = np.float32([[50, 50], [200, 50], [50, 200]])
(h, w) = img1.shape[:2]
rows2, cols2, ch2 = img1.shape #ch=3 rows = hieght and col is width
#creating the rotation matrix
x = random.randrange(-3, 3, 1)
y = random.random()
z = x + y
x = math.radians(z)
sin = math.sin(x)
cos = math.cos(x)
rotation = [[cos, sin], [-sin, cos]] #2x2 rotation matrix
#shear matrices
shear_fact_x = -0.05 + random_2() #take random values for both x and y
shear_fact_y = -0.05 + random_2()
shear_x = [[1, shear_fact_x], [0, 1]]
shear_y = [[1, 0], [shear_fact_y, 1]]
shear_new = np.array(shear_x, dtype=np.float32)
rotation_new = np.array(rotation, dtype=np.float32)
#multiplying 2D matrices
afine_t = np.dot(shear_new, rotation_new)
shear_new = np.array(shear_y, dtype=np.float32)
afine_t2 = np.dot(afine_t, shear_new)
#afine_t = shear_new
pts2 = np.dot(pts1, afine_t2) #new points use for warping the image
#print pts2
#cv2.imshow('Affine_2',img)
#cv2.waitKey()
s11, s12, s13 = noise_ad2.shape
for r in range(s11):
for c in range(s12):
if np.all(noise_ad2[r, c, :] == 0):
noise_ad2[r, c, 0] = noise_ad2[r, c, 0] + 1
noise_ad2[r, c, 1] = noise_ad2[r, c, 1] + 1
noise_ad2[r, c, 2] = noise_ad2[r, c, 2] + 1
M = cv2.getAffineTransform(pts1, pts2)
dst = cv2.warpAffine(noise_ad2, M, (cols2, rows2)) #get afine of image
#cv2.imshow('imshow',dst)
#cv2.imshow('Affine_3',dst)
#cv2.waitKey()
s1, s2, s3 = dst.shape
#removing the extra black areas from the images so that only logos is visible.
count = 0
count2 = 0
for r in range(s1):
num = count_rows(dst[r, :, :])
#print '*******NUM*******', num
if num >= 30:
count = count + 1
#break
else:
#count = count+1
break
for c in range(s2):
num2 = count_rows(dst[:, c, :])
#print '*******NUM*******1\t', num2
if (num2 >= 30):
count2 = count2 + 1
#break
else:
#count2 = count2+1
break
r1 = s1 - 1
while (r1 >= 0):
num3 = count_rows(dst[r1, :, :])
#print '*******NUM*******2\t', num3
if (num3 >= 30):
#break
r1 = r1 - 1
else:
#r1=r1-1
break
c1 = s2 - 1
while (c1 >= 0):
num4 = count_rows(dst[:, c1, :])
#print '*******NUM*******3\t', num4
if (num4 >= 30):
#break
c1 = c1 - 1
else:
#c1 = c1-1
break
# print 'first and last also col1 and col2',r1,count,count2,c1
dst2 = dst[count:r1, count2:c1]
#major change
or_image_x, or_image_y, or_image_z = img.shape
#dst2 = dst
s1, s2, s3 = dst2.shape
t2 = (-or_image_y + s2) / 2
t1 = (-or_image_x + s1) / 2
#print '***t2,t2***', t1, t2
count = r1 = c1 = count2 = 0
#print '*******s1*******', s1, s2, s3
if s1 > 100 and s2 > 100:
dst2 = dst2[t1:s1 - t1, t2:s2 - t2]
#cv2.imshow('Affine',dst2)
#cv2.waitKey()
else:
#print '******AFFINE FAILED********'
dst2 = img #comping original image to dst
#add the gist and hog feature of the image
if s2 != 0 and s1 != 0:
# cv2.imshow('noise',dst2)
#dst2 remove black.
gist_feat = Gist_feat_last.singleImage2(dst2)
hog_feat = HOG_feat2.hog_call(dst2)
gist_feat = np.concatenate((gist_feat, [1]))
hog_feat = np.concatenate((hog_feat, [1]))
'''add_feature_to_database(name,gist_feat,"Gabor")
add_feature_to_database(name,hog_feat,"HOG")'''
with open(feat_gist + name + '.csv', 'a') as myfile:
wr = csv.writer(myfile, quoting=csv.QUOTE_ALL)
wr.writerow(gist_feat)
with open(feat_hog + name + '.csv', 'a') as myfile:
wr = csv.writer(myfile, quoting=csv.QUOTE_ALL)
wr.writerow(hog_feat)
return i
def image_calc(img):
try:
#img = cv2.imdecode(str2
#img = cv2.imread(str2)
global list1
global list2
global files_name
print 'feature here'
correct_fea = Gist_feat_last.singleImage2(img)
feat = HOG_feat2.hog_call(img)
#print 'hog',feat.shape
#print 'correct shape',correct_fea.shape
print 'shapes'
Label_classify2(feat, 'batman')
Label_classify(correct_fea, 'batman')
print 'list', list1
print '2nd list', list2
temp_list1 = []
temp_list2 = []
last_list = []
ret_list1 = []
ret_list2 = []
ret_val = []
print list1
print list2
list11 = list1.values()
list12 = list2.values()
for file12 in files_name:
print 'in loop'
names = list1[file12]
names2 = list2[file12]
print 'got the list'
list1_new = remove_num(names)
list2_new = remove_num(names2)
list1_set = set(list1_new)
list2_set = set(list2_new)
print 'occ table made'
o1 = make_occurence_table(list1_set, list1_new)
o2 = make_occurence_table(list2_set, list2_new)
for labels in o2:
if o1.has_key(labels):
ret_temp = o2[labels]
o2[labels] = ret_temp + o1[labels]
max_mata = o2.values()
val_ans = max(max_mata)
for ans_ret in o2:
if o2[ans_ret] == val_ans:
ret_val.append(ans_ret)
temp_list2 = ret_key(o1, 2)
temp_list1 = ret_key(o2, 2)
last_list = list(set(temp_list1).intersection(set(temp_list2)))
return list(set(ret_val))
#for file12 in files_name:
#list3[file12] = []
#names = list1[file12]
#names_new = remove_num(names)
#names2 = list2[file12]
#names2_new = remove_num(names2)
#l1 = list(set(names_new).intersection(names2_new))
#l1 = remove_num(l1)
#l1=list(set(l1))
#list3[file12] = l1
#print 'last',list3
#return list3
except Exception, e:
print 'Exception', e
return list()
def image_calc(img):
'''s1 = str2
s4 = s1.split('path')[1]
s4 = s4[1:]
s4 = s4.split('>')[0]
str2 = s4.replace('//', '\\')
print 'image s',str2'''
try:
#img = cv2.imdecode(str2
#img = cv2.imread(str2)
global list1
global list2
global files_name
correct_fea = Gist_feat_last.singleImage2(img)
feat = HOG_feat2.hog_call(img)
annoyfeature = np.concatenate((correct_fea,feat))
#print 'hog',feat.shape
print 'Feature extracted'
Label_classify2(feat,'batman')
Label_classify(correct_fea,'batman')
#print 'list',list1
#print '2nd list',list2
temp_list1 = []
temp_list2 = []
last_list=[]
ret_list1 = []
ret_list2 = []
print list1
print list2
for file12 in files_name:
print 'in loop'
names = list1[file12]
names2 = list2[file12]
print 'got the list'
list1 = remove_num(names)
list2 = remove_num(names2)
list1_set = set(list1)
list2_set = set(list2)
print 'occ table made'
o1 = make_occurence_table(list1_set,list1)
o2 = make_occurence_table(list2_set,list2)
temp_list2 = ret_key(o1,2)
temp_list1 = ret_key(o2,2)
last_list = list(set(list1).intersection(set(list2)))
#lab1,distances = final_labels(temp_list1,'Hog')
#lab2,distances2 = final_labels(temp_list2,'Gist')
#hog_min = distances.min()
#gist_min = distances2.min()
#print 'lst',last_list,annoyfeature
print 'b4 annoy'
lab1,distances = combine_labels(last_list,annoyfeature)
print "after annoy"
mindist = distances.min()
for i in lab1:
if lab1[i] == hog_min:
ret_list2.append(i)
#for i in lab2:
# if lab2[i] == gist_min:
# ret_list1.append(i)
#print 'last',list3
#last_list = list(set(ret_list1).intersection(ret_list2))
return ret_list2
except Exception,e:
return 'Image not found',e