def main():
#make the image
img = np.zeros((128, 128), np.uint8)
img[:, :] += 100
#make the region
region1 = region_node()
for i in range(10, 31):
for j in range(10, 41):
region1.add_p((i, j), [200])
his = region_similar.histogram(region1.region_value, 25)
region1.colour_his = his / his.sum()
region2 = region_node()
for i in range(30, 61):
for j in range(20, 31):
region2.add_p((i, j), [50])
his = region_similar.histogram(region2.region_value, 25)
region2.colour_his = his / his.sum()
region3 = region_node()
for i in range(10, 21):
for j in range(40, 51):
region3.add_p((i, j), [150])
his = region_similar.histogram(region3.region_value, 25)
region3.colour_his = his / his.sum()
region1.merge(region3)
region1.merge(region2)
#draw region
print "region1's colour_his is:"
print region1.colour_his
print "region1's size is:" + str(region1.size)
cv.rectangle(img, (region1.bounding_box[0][1], region1.bounding_box[0][0]),
(region1.bounding_box[1][1], region1.bounding_box[1][0]), 255)
cv.imshow('region1', img)
cv.waitKey(0)
cv.destroyAllWindows()
'''
def main():
#make the image
img = np.zeros((128,128),np.uint8)
img[:,:] += 100
#make the region
region1 = region_node()
for i in range(10,31):
for j in range(10,41):
region1.add_p((i,j),[200])
his = region_similar.histogram(region1.region_value,25)
region1.colour_his = his/his.sum()
region2 = region_node()
for i in range(30,61):
for j in range(20,31):
region2.add_p((i,j),[50])
his = region_similar.histogram(region2.region_value,25)
region2.colour_his = his/his.sum()
region3 = region_node()
for i in range(10,21):
for j in range(40,51):
region3.add_p((i,j),[150])
his = region_similar.histogram(region3.region_value,25)
region3.colour_his = his/his.sum()
region1.merge(region3)
region1.merge(region2)
#draw region
print "region1's colour_his is:"
print region1.colour_his
print "region1's size is:"+str(region1.size)
cv.rectangle(
img,
(region1.bounding_box[0][1],region1.bounding_box[0][0]),
(region1.bounding_box[1][1],region1.bounding_box[1][0]),
255)
cv.imshow('region1',img)
cv.waitKey(0)
cv.destroyAllWindows()
'''
def initial_set(img, initial_regions, bins, similar_coef, similar_set,
region_set, bb_set):
width = img.shape[1]
height = img.shape[0]
img_size = img.size
for i in range(0, len(initial_regions)):
pixel = (i // width, i - (i // width) * width)
color = img[pixel[0], pixel[1]]
region = disjoin_set.find(initial_regions[i])
if region_set.has_key(region):
region_set[region].add_p(pixel, color)
else:
region_set[region] = region_node()
region_set[region].add_p(pixel, color)
for region in region_set:
his = region_similar.histogram(region_set[region].region_value,
bins)
region_set[region].colour_his = his / his.sum()
bb_set.append(region_set[region].bounding_box)
for i in range(0, len(initial_regions)):
raw = i // width
column = i - raw * width
if column < width - 1:
j = i + 1
region1 = disjoin_set.find(initial_regions[i])
region2 = disjoin_set.find(initial_regions[j])
if region1 != region2:
selective_search.make_pair(img_size, similar_set,
region_set, similar_coef,
region1, region2)
if raw < height - 1:
j = (raw + 1) * width + column
region1 = disjoin_set.find(initial_regions[i])
region2 = disjoin_set.find(initial_regions[j])
if region1 != region2:
selective_search.make_pair(img_size, similar_set,
region_set, similar_coef,
region1, region2)
if raw < height - 1 and column < width - 1:
j = (raw + 1) * width + (column + 1)
region1 = disjoin_set.find(initial_regions[i])
region2 = disjoin_set.find(initial_regions[j])
if region1 != region2:
selective_search.make_pair(img_size, similar_set,
region_set, similar_coef,
region1, region2)
def initial_set(img,initial_regions,bins,similar_coef,similar_set,region_set,bb_set):
width = img.shape[1]
height = img.shape[0]
img_size = img.size
for i in range(0,len(initial_regions)):
pixel = (i//width,i-(i//width)*width)
color = img[pixel[0],pixel[1]]
region = disjoin_set.find(initial_regions[i])
if region_set.has_key(region):
region_set[region].add_p(pixel,color)
else:
region_set[region] = region_node()
region_set[region].add_p(pixel,color)
for region in region_set:
his = region_similar.histogram(
region_set[region].region_value,
bins)
region_set[region].colour_his = his/his.sum()
bb_set.append(region_set[region].bounding_box)
for i in range(0,len(initial_regions)):
raw = i//width
column = i-raw*width
if column < width-1:
j = i+1
region1 = disjoin_set.find(initial_regions[i])
region2 = disjoin_set.find(initial_regions[j])
if region1!=region2:
selective_search.make_pair(
img_size,
similar_set,
region_set,
similar_coef,
region1,
region2
)
if raw < height-1:
j = (raw+1)*width+column
region1 = disjoin_set.find(initial_regions[i])
region2 = disjoin_set.find(initial_regions[j])
if region1!=region2:
selective_search.make_pair(
img_size,
similar_set,
region_set,
similar_coef,
region1,
region2
)
if raw < height-1 and column < width-1:
j = (raw+1)*width+(column+1)
region1 = disjoin_set.find(initial_regions[i])
region2 = disjoin_set.find(initial_regions[j])
if region1!=region2:
selective_search.make_pair(
img_size,
similar_set,
region_set,
similar_coef,
region1,
region2
)