def search(self, image1_pixels, x_start, y_start, image2_pixels):
self.reset_search()
block_size = self.block_size
pass_step = self.pass_step
subimage_1_pixels = ImageConverter.sub_pixels(image1_pixels, x_start, y_start, x_start+block_size, y_start+block_size)
p = pass_step
ds = p/2
s = 1
xs = x_start
ys = y_start
xs_min = -1
ys_min = -1
best_local_MAD = 10000
best_global_MAD = 10000
best_x = -1
best_y = -1
klog("Check block from %d, %d" %(x_start, y_start))
while ds >= 1:
for x in [xs, xs+ds, xs-ds]:
for y in [ys, ys+ds, ys-ds]:
if not ImageComparator.is_valid_coordinate(x, y, block_size, image2_pixels):
continue
MAD = self.calculate_MAD(subimage_1_pixels, image2_pixels, x, y, x+block_size, y+block_size)
if MAD < best_local_MAD:
best_local_MAD = MAD
xs_min = x
ys_min = y
#Check if the local MAD is the best global MAD
if MAD < best_global_MAD:
best_global_MAD = MAD
best_x = x
best_y = y
s += 1
ds -= 1
xs = xs_min
ys = ys_min
print "-"
return best_x, best_y, best_global_MAD, self._MAD_checks_count
def search(self, image1_pixels, x_start, y_start, image2_pixels):
self.reset_search()
block_size = self.block_size
pass_step = self.pass_step
#subimage_1 = image1.copy(x_start, y_start, block_size, block_size)
subimage_1_pixels = ImageConverter.sub_pixels(image1_pixels, x_start, y_start, x_start+block_size, y_start+block_size)
p = pass_step
ds = math.pow(2, math.floor( math.log(p,2))-1 )
s = 1
xs = x_start
ys = y_start
xs_min = -1
ys_min = -1
best_local_MAD = 10000
best_global_MAD = 10000
best_x = -1
best_y = -1
while ds >= 1:
if ds != 1:
for (x,y) in [(xs, ys), (xs-ds,ys), (xs+ds, ys), (xs, ys+ds), (xs,ys-ds)]:
if not ImageComparator.is_valid_coordinate(x, y, block_size, image2_pixels):
continue
MAD = self.calculate_MAD(subimage_1_pixels, image2_pixels, x, y, x+block_size, y+block_size)
if MAD < best_local_MAD:
best_local_MAD = MAD
xs_min = x
ys_min = y
#Check if the local MAD is the best global MAD
if MAD < best_global_MAD:
best_global_MAD = MAD
best_x = x
best_y = y
s += 1
#Check if the xs_min and ys_min are the central point of the fives checked
if xs_min == xs and ys_min == ys:
ds /= 2
xs = xs_min
ys = ys_min
else: #ds==1 last step
for x in [xs, xs+ds, xs-ds]:
for y in [ys, ys+ds, ys-ds]:
#TODO: CODE HERE IS COPY-PASTED!! THIS SUCKS! secondo me va bene!
if not ImageComparator.is_valid_coordinate(x, y, block_size, image2_pixels):
continue
MAD = self.calculate_MAD(subimage_1_pixels, image2_pixels, x, y, x+block_size, y+block_size)
if MAD < best_local_MAD:
best_local_MAD = MAD
xs_min = x
ys_min = y
#Check if the local MAD is the best global MAD
if MAD < best_global_MAD:
best_global_MAD = MAD
best_x = x
best_y = y
ds = 0
s += 1
print "ds: %d" %ds
return best_x, best_y, best_global_MAD, self._MAD_checks_count
def search(self, image1_pixels, x_start, y_start, image2_pixels):
self.reset_search()
block_size = self.block_size
pass_step = self.pass_step
subimage_1_pixels = ImageConverter.sub_pixels(image1_pixels, x_start, y_start, x_start+block_size, y_start+block_size)
p = pass_step
ds = p/2+1
s = 1
xs = x_start
ys = y_start
best_local_MAD = 10000
xs_min = -1
ys_min = -1
best_local_a_MAD = 10000
xs_a_min = -1
ys_a_min = -1
best_global_MAD = 10000
best_x = -1
best_y = -1
#klog("Check block from %d, %d" %(x_start, y_start))
while True:
for (x,y) in [ (xs,ys), (xs-ds,ys), (xs+ds,ys) ]:
if not ImageComparator.is_valid_coordinate(x, y, block_size, image2_pixels):
continue
MAD = self.calculate_MAD(subimage_1_pixels, image2_pixels, x, y, x+block_size, y+block_size)
if MAD < best_local_a_MAD:
best_local_a_MAD = MAD
xs_a_min = x
ys_a_min = y
#Check if the local MAD is the best global MAD
if MAD < best_global_MAD:
best_global_MAD = MAD
best_x = x
best_y = y
for (x,y) in [ (xs_a_min,ys_a_min), (xs_a_min,ys_a_min-ds), (xs_a_min, ys_a_min+ds) ]:
if not ImageComparator.is_valid_coordinate(x, y, block_size, image2_pixels):
continue
#Calculate the MAD
MAD = self.calculate_MAD(subimage_1_pixels, image2_pixels, x, y, x+block_size, y+block_size)
if MAD < best_local_MAD:
best_local_MAD = MAD
xs_min = x
ys_min = y
#Check if the local MAD is the best global MAD
if MAD < best_global_MAD:
best_global_MAD = MAD
best_x = x
best_y = y
if ds == 1:
break
s += 1
ds = math.ceil( ds/2 )
# klog("ds: %d" %(ds))
xs = xs_min
ys = ys_min
best_local_MAD = 10000 #reset
best_local_a_MAD = 10000 #reset
#klog("-")
#klog("MADs check count: %d" %MAD_checks_count)
return best_x, best_y, best_global_MAD, self._MAD_checks_count
def search(self, image1_pixels, x_start, y_start, image2_pixels):
self.reset_search()
block_size = self.block_size
margin_size = self.margin_size
best_MAD = 1000000
best_x = None
best_y = None
subimage_1_pixels = ImageConverter.sub_pixels(image1_pixels, x_start, y_start, x_start+block_size, y_start+block_size)
#Start with the center
if ImageComparator.is_valid_coordinate(x_start, y_start, block_size, image2_pixels):
MAD = self.calculate_MAD(subimage_1_pixels, image2_pixels, x_start, y_start, x_start+block_size, y_start+block_size)
if MAD < best_MAD:
#klog("Best MAD found: %f, at (%f,%f)" % (MAD, px, py))
best_MAD = MAD
best_x = x_start
best_y = y_start
if best_MAD == 0:
return best_x, best_y, best_MAD, self._MAD_checks_count
for py in range(y_start-margin_size, y_start+margin_size):
if py < 0:
continue #il blocco esce in su dall'immagine, avanza con il prossimo py incrementato
#CHECK!!
if not ImageComparator.is_valid_coordinate(0, py, block_size, image2_pixels):
break #il blocco esce in giu dall'immagine, esci
for px in range(x_start-margin_size, x_start+margin_size):
if px < 0:
continue #il blocco esce a sinistra dall'immagine, avanza con il prossimo px incrementato
#CHECK!!
if not ImageComparator.is_valid_coordinate(px, py, block_size, image2_pixels):
break #il blocco esce in giu dall'immagine, esci
#if px+block_size > image2.width():
# break #il blocco esce a destra dall'immagine, esci
#klog("Valuating block (%f,%f)" %(px, py))
MAD = self.calculate_MAD(subimage_1_pixels, image2_pixels, px, py, px+block_size, py+block_size)
if MAD < best_MAD:
#klog("Best MAD found: %f, at (%f,%f)" % (MAD, px, py))
best_MAD = MAD
best_x = px
best_y = py
if best_MAD == 0:
return best_x, best_y, best_MAD, self._MAD_checks_count
return best_x, best_y, best_MAD, self._MAD_checks_count
