def images_equal(image1, image2, acceptable_rms = 10):
try:
img1 = Image.open(image1)
img2 = Image.open(image2)
except:
return False
try:
diff = ImageChops.difference(img1, img2)
except ValueError:
return False
h = ImageChops.difference(img1, img2).histogram()
sq = (value*((idx%256)**2) for idx, value in enumerate(h))
sum_of_squares = sum(sq)
rms = math.sqrt(sum_of_squares/float(img1.size[0] * img1.size[1]))
return rms <= acceptable_rms
def autocrop(im, bgcolor, borderWidth=0):
if im.mode != 'RGB':
im = im.convert('RGB')
bg = Image.new('RGB', im.size, bgcolor)
diff = ImageChops.difference(im, bg)
bbox = diff.getbbox()
if bbox:
if borderWidth > 0:
(x0, y0, x2, y2) = bbox
if x0 > borderWidth:
x0 = x0 - borderWidth
else:
x0 = 0
if y0 > borderWidth:
y0 = y0 - borderWidth
else:
y0 = 0
if x2 + borderWidth < im.size[0]:
x2 = x2 + borderWidth
else:
x2 = im.size[0]
if y2 + borderWidth < im.size[1]:
y2 = y2 + borderWidth
else:
y2 = im.size[1]
bbox = (x0, y0, x2, y2)
return im.crop(bbox)
return im
def testExecute(self):
"""
"""
self.run()
plugin = self.getPlugin()
################################################################################
# Compare XSDataResults
################################################################################
strExpectedOutput = self.readAndParseFile(
self.getReferenceDataOutputFile())
EDVerbose.DEBUG("Checking obtained result...")
xsDataResultObtained = plugin.getDataOutput()
EDAssert.strAlmostEqual(self.xsDataResultReference.marshal(),
xsDataResultObtained.marshal(),
"XSDataResult output are the same")
################################################################################
# Compare image Files
################################################################################
outputData = Image.open(self.outputFile)
referenceData = Image.open(
os.path.join(self.getTestsDataImagesHome(),
os.path.basename(self.outputFile)))
delta = ImageChops.difference(outputData, referenceData)
deltaColor = delta.getextrema()
i = 0
for color in deltaColor:
EDAssert.lowerThan(
color[1], 12, "Maximum tone variation is %s for channel %s" %
(color[1], "RGBA"[i]))
i += 1
def refresh(self, image, bbox=None):
# find the difference from the current framebuffer
bbox = ImageChops.difference(image, self.image).getbbox()
if bbox != None:
(left, top, right, bottom) = bbox
right *= 8
right /= 8
left /= 8
left *= 8
top /= 8
bottom += 7
bottom /= 8
# for each Nx8 row segment
for row in xrange(top, bottom):
y = row * 8
update = ''
# build a horizontal array of 8-bit vertical stripes
for x in xrange(left, right, 8):
# get an 8x8 chunk and rotate into the display's vertical address orientation
chunk = image.crop(
(x, y, x + 8, y + 8)).transpose(Image.ROTATE_270)
# add this 8x8 chunk to the update
update += chunk.tostring()
# write out the update for this Nx8 row
self.display.graphicWrite(update, left * 4 + row)
# update the framebuffer
self.image = image
def refresh(self, image, bbox=None):
# find the difference from the current framebuffer
if bbox == None:
bbox = ImageChops.difference(image, self.image).getbbox()
if bbox != None:
(left, top, right, bottom) = bbox
left /= 8
left *= 8
right *= 8
right /= 8
# get the pixels to be updated
update = image.crop((left, 0, right, self.display.H))
# rotate the update into the display's vertical address orientation
update = update.transpose(Image.ROTATE_270).transpose(
Image.FLIP_LEFT_RIGHT)
# send the update
update = update.tostring()
self.write(update, left * (self.display.H / 8))
# update the framebuffer
self.image = image
def repeat():
global capture
global camera_index
global first_frame
global last_frame
global frame_decount
global last_bubble_time
frame = cv.QueryFrame(capture)
cv.ShowImage("w1", frame)
c = cv.WaitKey(10)
image = Image.fromstring("RGB", [frame.width, frame.height], frame.tostring())
if not first_frame:
diff = ImageChops.difference(image, last_frame)
h = diff.histogram()
sq = (value*(idx**2) for idx, value in enumerate(h))
sum_of_squares = sum(sq)
rms = math.sqrt(sum_of_squares/float(image.size[0] * last_frame.size[1]))
if rms > 579.00 and frame_decount == 0:
now = time.time()
delta = (now - last_bubble_time)
row_key = '-'.join(['row',str(now)])
table.put(row_key, {'info:delta': str(delta), 'info:date': str(datetime.datetime.now()).split('.')[0], 'info:rms': str(rms), 'info:height': str(frame.height), 'info:width': str(frame.width)})
print("Gas Release Detected: (Delta: %f)" % (delta))
print("Record Saved -> %s" % (row_key))
frame_decount = 40
last_bubble_time = now
elif frame_decount > 0:
frame_decount = frame_decount - 1
last_frame = image
first_frame = False
def avgentropy(imgset, imgs):
idxs = range(0, 30)
entropies = []
# precalculate all entropies
for i in range(0, 29):
diff = ImageChops.difference(imgs[i], imgs[i+1])
entropies.append(image_entropy(diff))
print entropies
avg = []
# average of the entropy of the difference with previous & next images.
avg.append(entropies[0])
for i in range(1,29):
avg.append((entropies[i-1] + entropies[i]) / 2.)
print avg[-1]
avg.append(entropies[-1])
#idxs = range(0, 30)
#OFFSET = 1
#avg = []
# compare all images in the specified range
#for i in idxs:
# ent = []
# idxs2 = range(max(0, i-OFFSET), min(i+OFFSET+1, 30))
# for j in idxs2:
# if i != j:
# diff = ImageChops.difference(imgs[i], imgs[j])
# ent.append(image_entropy(diff))
# avg.append(np.mean(ent))
# save a plot of the average entropy array
plt.plot(idxs, avg, 'bo-')
plt.axvline(result[imgset-1]-1, color="r")
plt.savefig("avg-%i-%i.png" % (imgset, len(idxs)))
plt.clf()
return avg
def imagecompare(imgfile1, imgfile2):
try:
import ImageChops, Image
except ImportError:
raise Exception, 'Python-Imaging package not installed'
try:
diffcount = 0.0
im1 = Image.open(imgfile1)
im2 = Image.open(imgfile2)
imgcompdiff = ImageChops.difference(im1, im2)
diffboundrect = imgcompdiff.getbbox()
imgdiffcrop = imgcompdiff.crop(diffboundrect)
data = imgdiffcrop.getdata()
seq = []
for row in data:
seq += list(row)
for i in xrange(0, imgdiffcrop.size[0] * imgdiffcrop.size[1] * 3, 3):
if seq[i] != 0 or seq[i+1] != 0 or seq[i+2] != 0:
diffcount = diffcount + 1.0
diffImgLen = imgcompdiff.size[0] * imgcompdiff.size[1] * 1.0
diffpercent = (diffcount * 100) / diffImgLen
return diffpercent
except IOError:
raise Exception, 'Input file does not exist'
def autocrop(im, bgcolor, borderWidth = 0):
if im.mode != 'RGB':
im = im.convert('RGB')
bg = Image.new('RGB', im.size, bgcolor)
diff = ImageChops.difference(im, bg)
bbox = diff.getbbox()
if bbox:
if borderWidth > 0:
(x0,y0,x2,y2) = bbox
if x0 > borderWidth:
x0 = x0 - borderWidth
else:
x0 = 0
if y0 > borderWidth:
y0 = y0 - borderWidth
else:
y0 = 0
if x2 + borderWidth < im.size[0]:
x2 = x2 + borderWidth
else:
x2 = im.size[0]
if y2 + borderWidth < im.size[1]:
y2 = y2 + borderWidth
else:
y2 = im.size[1]
bbox = (x0,y0,x2,y2)
return im.crop(bbox)
return im
def _equal(self, img1, img2):
"""
Checks if two screenshots are identical.
@param img1: first screenshot to check
@param img2: second screenshot to check
"""
return ImageChops.difference(img1, img2).getbbox() is None
def rmsdiff(im1, im2):
"Calculate the root-mean-square difference between two images"
h = ImageChops.difference(im1, im2).histogram()
# calculate rms
return math.sqrt(reduce(operator.add,
map(lambda h, i: h*(i**2), h, range(1024))
) / (float(im1.size[0]) * im1.size[1]))
def autocrop(im, bgcolor):
bg = Image.new(im.mode, im.size, bgcolor)
diff = ImageChops.difference(im, bg)
bbox = diff.getbbox()
print "bbox",bbox
if bbox: return im.crop(bbox) # cropped
else: return im # no contents
def recognize(img, bounds):
# read dataset of images for each letter
imgs = {}
datfile = open("ads.dat", "rt")
line = datfile.readline()
while line!="":
key = line[0]
if key not in imgs:
imgs[key] = []
imgs[key].append(Image.open(StringIO.StringIO(line[2:-1].decode("hex"))))
line = datfile.readline()
datfile.close()
# calculate difference with dataset for each boundbox
word = ""
for bound in bounds:
guess = []
total = (img.crop(bound).size)[0]*(img.crop(bound).size)[1]*1.0
for key in imgs:
for pattern in imgs[key]:
diff = ImageChops.difference(img.crop(bound), pattern.resize(img.crop(bound).size, Image.NEAREST))
pixels = list(diff.getdata())
samePixCnt = sum(i==0 for i in pixels)
guess.append([samePixCnt, key])
guess.sort(reverse=True)
word = word+guess[0][1]
print total, guess[0:3], guess[0][0]/total, guess[1][0]/total, guess[2][0]/total
print word
return word.replace("_", "")
def crop(image_file, bgcolor=ImageColor.getrgb("white")):
image = Image.open(image_file,'r')
mask = Image.new("RGB", image.size, bgcolor)
diff = ImageChops.difference(image, mask)
bbox = diff.getbbox()
new_image = image.crop(bbox)
new_image.save(image_file,"png")
def autofocus(self,step=5000):
if self.slide.pos[2] >= 0: step = -step
self.slide.moveZ(-step/2)
z_start = self.slide.pos[2]
self.frames.fillBuffer()
self.slide.displaceZ(step)
z_frames = self.frames.getBuffer()
#sample every kth plus its lth neighbor: for k=10,l=2 sample frame 0,10,20 and 2,12,22
k = 10
l = 2
sample_ind = [ind*k for ind in range(len(z_frames)/k)]
sample_ind2 = [ind*k+l for ind in range(len(z_frames)/k)]
f = [z_frames[ind] for ind in sample_ind]
f2 = [z_frames[ind] for ind in sample_ind2]
n = len(f)
diffs = []
for i in range(n-2):
diffs.append(ImageChops.difference(f[i],f2[i]))
motion = []
for f in diffs:
f = ImageChops.multiply(f,self.curr_mask)
motion.append(ImageStat.Stat(f).sum[0])
#g = Gnuplot.Gnuplot()
#g.plot(motion)
max_frame = scipy.argmax(motion)
max_focus = (max_frame/float(n))*step + z_start
self.slide.moveZ(max_focus)
return max_focus
def matchTemplate(searchImage, templateImage):
minScore = -1000
matching_xs = 0
matching_ys = 0
# convert images to "L" to reduce computation by factor 3 "RGB"->"L"
searchImage = searchImage.convert(mode="L")
templateImage = templateImage.convert(mode="L")
searchWidth, searchHeight = searchImage.size
templateWidth, templateHeight = templateImage.size
# make a copy of templateImage and fill with color=1
templateMask = Image.new(mode="L", size=templateImage.size, color=1)
# loop over each pixel in the search image
for xs in range(searchWidth - templateWidth + 1):
for ys in range(searchHeight - templateHeight + 1):
# for ys in range(10):
# set some kind of score variable to "All equal"
score = templateWidth * templateHeight
# crop the part from searchImage
searchCrop = searchImage.crop((xs, ys, xs + templateWidth, ys + templateHeight))
diff = ImageChops.difference(templateImage, searchCrop)
notequal = ImageChops.darker(diff, templateMask)
countnotequal = sum(notequal.getdata())
score -= countnotequal
if minScore < score:
minScore = score
matching_xs = xs
matching_ys = ys
if minScore > 100:
print "Location=", (matching_xs, matching_ys), "Score=", minScore
quit()
def equal(self, img1, img2, skip_area=None):
"""Compares two screenshots using Root-Mean-Square Difference (RMS).
@param img1: screenshot to compare.
@param img2: screenshot to compare.
@return: equal status.
"""
if not HAVE_PIL:
return None
# Trick to avoid getting a lot of screen shots only because the time in the windows
# clock is changed.
# We draw a black rectangle on the coordinates where the clock is locates, and then
# run the comparison.
# NOTE: the coordinates are changing with VM screen resolution.
if skip_area:
# Copying objects to draw in another object.
img1 = img1.copy()
img2 = img2.copy()
# Draw a rectangle to cover windows clock.
for img in (img1, img2):
self._draw_rectangle(img, skip_area)
# To get a measure of how similar two images are, we use
# root-mean-square (RMS). If the images are exactly identical,
# this value is zero.
diff = ImageChops.difference(img1, img2)
h = diff.histogram()
sq = (value * ((idx % 256)**2) for idx, value in enumerate(h))
sum_of_squares = sum(sq)
rms = math.sqrt(sum_of_squares/float(img1.size[0] * img1.size[1]))
# Might need to tweak the threshold.
return rms < 8
def _getBounds(size, glDispID, filename, scale, rotation, partRotation):
# Clear the drawing buffer with white
glClearColor(1.0, 1.0, 1.0, 1.0)
glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)
# Draw the piece in black
glColor3f(0, 0, 0)
adjustGLViewport(0, 0, size, size)
rotateToView(rotation, scale)
rotateView(*partRotation)
glCallList(glDispID)
# Use PIL to find the image's bounding box (sweet)
pixels = glReadPixels(0, 0, size, size, GL_RGB, GL_UNSIGNED_BYTE)
img = Image.fromstring("RGB", (size, size), pixels)
bg = bgCache.setdefault(size, Image.new("RGB", img.size, (255, 255, 255)))
box = ImageChops.difference(img, bg).getbbox()
if box is None:
return (0, 0, 0, 0, 0, 0) # Rendered entirely out of frame
# if filename:
# import os
# rawFilename = os.path.splitext(os.path.basename(filename))[0]
# img.save("C:\\lic\\tmp\\%s_%dx%d.png" % (rawFilename, box[2] - box[0], box[3] - box[1]))
# print filename + "box: " + str(box if box else "No box = shit")
# Find the bottom left corner inset, used for placing PLIItem quantity labels
data = img.load()
leftInset = _getLeftInset(data, size, box[1])
bottomInset = _getBottomInset(data, size, box[0])
return box + (leftInset - box[0], bottomInset - box[1])
def equal(self, img1, img2, skip_area=None):
"""Compares two screenshots using Root-Mean-Square Difference (RMS).
@param img1: screenshot to compare.
@param img2: screenshot to compare.
@return: equal status.
"""
if not HAVE_PIL:
return None
# Trick to avoid getting a lot of screen shots only because the time in the windows
# clock is changed.
# We draw a black rectangle on the coordinates where the clock is locates, and then
# run the comparison.
# NOTE: the coordinates are changing with VM screen resolution.
if skip_area:
# Copying objects to draw in another object.
img1 = img1.copy()
img2 = img2.copy()
# Draw a rectangle to cover windows clock.
for img in (img1, img2):
self._draw_rectangle(img, skip_area)
# To get a measure of how similar two images are, we use
# root-mean-square (RMS). If the images are exactly identical,
# this value is zero.
diff = ImageChops.difference(img1, img2)
h = diff.histogram()
sq = (value * ((idx % 256)**2) for idx, value in enumerate(h))
sum_of_squares = sum(sq)
rms = math.sqrt(sum_of_squares / float(img1.size[0] * img1.size[1]))
# Might need to tweak the threshold.
return rms < 8
def CreateContrast(self):
image = self.grab_screen('comparer.png')
diffImage = ImageChops.difference(self.baseImage, image)
self.save_image('diff.png', diffImage)
imageWidth, imageHeight = diffImage.size
outlist = [(0,0,0) for x in xrange(imageWidth*imageHeight)]
imgdata = list(diffImage.getdata())
for x in xrange(imageWidth):
for y in xrange(imageHeight):
color = imgdata[y*imageWidth+x]
## if color != (0,0,0):
## outlist[y*imageWidth+x] = (255,0,0)
if color[0] > 20 or color[1] > 20 or color[2] > 20:
outlist[y*imageWidth+x] = (255,0,0)
## print color
self.changedImage = Image.new('RGB', (imageWidth, imageHeight))
self.changedImage.putdata(outlist)
self.save_image('changed.png', self.changedImage)
def showImageDifference():
global testImage, refImage, diffImage, diffCanvas, croppedRefImage
xsize, ysize = testImage.size
croppedRefImage = refImage.crop((0, 0, xsize, ysize))
diffImage = ImageChops.difference(testImage, croppedRefImage)
diffCanvas = openImageWindow(diffImage, 'BOS Difference', diffCanvas)
return
def imagecompare(imgfile1, imgfile2):
try:
import ImageChops, Image
except ImportError:
raise Exception('Python-Imaging package not installed')
try:
diffcount = 0.0
im1 = Image.open(imgfile1)
im2 = Image.open(imgfile2)
imgcompdiff = ImageChops.difference(im1, im2)
diffboundrect = imgcompdiff.getbbox()
imgdiffcrop = imgcompdiff.crop(diffboundrect)
data = imgdiffcrop.getdata()
seq = []
for row in data:
seq += list(row)
for i in xrange(0, imgdiffcrop.size[0] * imgdiffcrop.size[1] * 3, 3):
if seq[i] != 0 or seq[i+1] != 0 or seq[i+2] != 0:
diffcount = diffcount + 1.0
diffImgLen = imgcompdiff.size[0] * imgcompdiff.size[1] * 1.0
diffpercent = (diffcount * 100) / diffImgLen
return diffpercent
except IOError:
raise Exception('Input file does not exist')
def game_logic(screen, dt, player):
global TRACES
im = screen_grab(box=PLAYABLE_BOX)
#return True
#im = screen_grab(box=PLAYABLE_BOX)
im_gray = ImageOps.grayscale(im)
coords = get_coords()
if coords[0] < 0 or coords[1] < 0:
print "Mouse out"
return False
if im.getpixel(END_GAME_OK) == (232, 97, 1):
return True
if im.getpixel(GET_READY) == (244, 198, 0) or im.getpixel(START_BUTTON) == (217, 83, 14):
return True
diff_array = asarray(ImageChops.difference(im_gray,BACKGROUND_GRAY))
#grayscale_array_to_pygame(screen, diff_array)
bird, pipes = find_elements(diff_array)
draw_game(screen, bird, pipes)
player.see(dt, bird, pipes)
pygame.draw.rect(screen, BLUE, [player.x - 3, player.y - 3, 6, 6])
font = pygame.font.SysFont("monospace", 15)
label = font.render("%d %d"%(player.speed_y, player.accel_y), 1, (255,255,0))
screen.blit(label, (10, FLOOR_Y + 26))
player.play()
return True
def equal(im1, im2):
diff = ImageChops.difference(im1, im2)
h = diff.histogram()
sq = (value*(idx**2) for idx, value in enumerate(h))
sum_of_squares = sum(sq)
rms = math.sqrt(sum_of_squares/float(im1.size[0] * im1.size[1]))
return rms
def convert(img):
paths = os.listdir(imgDataPath)
for i in range(len(paths)):
datum = Image.open(imgDataPath + paths[i])
if ImageChops.difference(img, datum).getbbox() is None:
return i
return -1
def compare_screenshots(self,approved_path, actual_path, diff_path ):
#
dataA = open(approved_path, 'rb').read()
dataB = open(actual_path, 'rb').read()
if hashlib.md5(dataA).hexdigest() != hashlib.md5(dataB).hexdigest():
#make one size
# ToDo
imageA = Image.open(approved_path)
imageB = Image.open(actual_path)
#if sizes not match - resize smalled
if ( imageA.size != imageB.size ):
#get max image size
w_size = max( [ imageA.size[0] , imageB.size[0] ] )
h_size = max( [ imageA.size[1] , imageB.size[1] ] )
#resize images with max size
imageA = imageA.resize( [ w_size , h_size ] ,Image.ANTIALIAS )
#imageA.size = [w_size , h_size]
imageB = imageB.resize( [ w_size , h_size ] ,Image.ANTIALIAS )
#imageB.size = [w_size , h_size]
#calculate diff
diff = ImageChops.difference(imageA, imageB)
#generate pretty diff image
light = Image.new('RGB', size=imageA.size, color=0xFFFFFF)
mask = Image.new('L', size=imageA.size, color=0xFF)
draw = ImageDraw.Draw(mask)
draw.rectangle((0, 0, imageA.size[0], imageA.size[1] ), fill=0x80)
imageA = Image.composite(imageA, light, mask)
imageA.paste(diff, (0,0), mask)
imageA.convert("RGB")
imageA.save( diff_path , "PNG" )
return True
return False
def _equal(self, img1, img2):
"""
Checks if two screenshots are identical.
@param img1: first screenshot to check
@param img2: second screenshot to check
"""
return ImageChops.difference(img1, img2).getbbox() is None
def recognize(img, bounds):
# read dataset of images for each letter
imgs = {}
datfile = open("ads.dat", "rt")
line = datfile.readline()
while line != "":
key = line[0]
if key not in imgs:
imgs[key] = []
imgs[key].append(
Image.open(StringIO.StringIO(line[2:-1].decode("hex"))))
line = datfile.readline()
datfile.close()
# calculate difference with dataset for each boundbox
word = ""
for bound in bounds:
guess = []
total = (img.crop(bound).size)[0] * (img.crop(bound).size)[1] * 1.0
for key in imgs:
for pattern in imgs[key]:
diff = ImageChops.difference(
img.crop(bound),
pattern.resize(img.crop(bound).size, Image.NEAREST))
pixels = list(diff.getdata())
samePixCnt = sum(i == 0 for i in pixels)
guess.append([samePixCnt, key])
guess.sort(reverse=True)
word = word + guess[0][1]
print total, guess[
0:3], guess[0][0] / total, guess[1][0] / total, guess[2][0] / total
print word
return word.replace("_", "")
def imageprocess(self):
while self.isrunning:
try:
self.currentframedata = urllib2.urlopen(self.config['cameraurl']).read()
except:
continue
if not self.dnd:
self.currentimage = Image.open(StringIO.StringIO(self.currentframedata))
if self.previousimage == None: self.previousimage = self.currentimage.copy()
framedifference = ImageStat.Stat(ImageChops.difference(self.currentimage, self.previousimage)).rms
self.rms = sum(framedifference)/len(framedifference)
self.previousimage = self.currentimage.copy()
self.currentimage = None
if self.rms > self.config['sensivity']:
self.show()
gtk.gdk.threads_enter()
try:
if self.get_visible():
self.set_title(str(datetime.datetime.now()))
framedataloader=gtk.gdk.PixbufLoader()
framedataloader.write(self.currentframedata)
framedataloader.close()
self.canvas.set_from_pixbuf(framedataloader.get_pixbuf())
finally:
gtk.gdk.threads_leave()
def processImage(self, im):
im = im.resize((320, 240))
im = ImageOps.grayscale(im)
im = im.filter(ImageFilter.BLUR)
if not self.prevImage:
self.prevImage = im
return
imd = ImageChops.difference(im, self.prevImage)
def mappoint(pix):
if pix > 20:
return 255
else:
return 0
imbw = imd.point(mappoint)
hist = imbw.histogram()
percentWhitePix = hist[-1] / (hist[0] + hist[-1])
motionDetected = (percentWhitePix > self.DETECTION_THRESHOLD)
self.factory.motionStateMachine.step(motionDetected)
motionSustained = self.factory.motionStateMachine.inSustainedMotion()
print '%d %d' % (motionDetected, motionSustained)
sys.stdout.flush()
self.prevImage = im
def difference(self, first, second):
import ImageChops
from hashlib import md5
difference = ImageChops.difference(first, second)
diff_box = difference.getbbox()
diffs = []
if diff_box is not None:
# If there is any difference, just retrieve the box
# that has changed in the new frame
image = first.crop((diff_box[0],
diff_box[1],
diff_box[2],
diff_box[3]))
size = (diff_box[2] - diff_box[0], diff_box[3] - diff_box[1])
position = (diff_box[0], diff_box[1])
hash = str(md5(image.tostring()).hexdigest())
diff = {
"hash": hash, # Hash of the diff for checking if portion is repeated (md5 string)
"image": image, # Portion of the image <Image>
"position": position, # Position of the portion in the frame (x, y)
"size": size # Size of the portion (x, y)
}
diffs.append(diff)
return diffs
def difference(img_a, img_b):
"""
Calculates the difference between two images, defined as the sum of the
variance of each color channel contained by the image resulting from their
subtraction.
"""
return sum(ImageStat.Stat(ImageChops.difference(img_a, img_b)).var)
def _dist(self, tile, reference_image):
""" Calculates the distance of an image to a reference image"""
reference_image = reference_image.resize(tile.size, Image.NEAREST)
xor = ImageChops.difference(tile, reference_image).getcolors()
for cnt, color in xor:
if color == 255:
return cnt
return 0
def compare_images(self, im1, im2):
"""
Calculate the exact difference between two images.
"""
im1 = Image.open(im1)
im2 = Image.open(im2)
if ImageChops.difference(im1, im2).getbbox() is not None:
raise AssertionError("Image: %s is different from baseline: %s" % (im1.filename, im2.filename))
def autocrop(img):
""" Crop white borders from image. """
bg = Image.new('1', img.size, 255)
diff = ImageChops.difference(img, bg)
bbox = diff.getbbox()
if bbox:
img = img.crop(bbox)
return img
def substract(self, inimage, orientation=5):
"""apply a substraction mask on the image"""
self.img = inimage
self.x, self.y = self.img.size
ImageFile.MAXBLOCK = self.x * self.y
self.mask(orientation)
k = ImageChops.difference(self.img, self.bigsig)
return k
def rmsdiff(im1, im2):
"Calculate the root-mean-square difference between two images"
diff = ImageChops.difference(im1, im2)
h = diff.histogram()
sq = (value * ((idx % 256)**2) for idx, value in enumerate(h))
sum_of_squares = sum(sq)
rms = math.sqrt(sum_of_squares / float(im1.size[0] * im1.size[1]))
return rms
def recalc(self):
if self._recalc == 1:
diff = ImageChops.difference(self.img, self.reference_img)
h = diff.histogram()
sq = (value*(idx**2) for idx, value in enumerate(h))
sum_of_squares = sum(sq)
self.similarity = math.sqrt(sum_of_squares/float(self.img.size[0] * self.img.size[1]))
self._recalc = 0
def getImgDiff(pic1, pic2):
img1 = Image.fromarray(pic1)
img2 = Image.fromarray(pic2)
#img1.show()
diff = ImageChops.difference(img1, img2)
return diff
def trim(image, border_color):
mask = Image.new(image.mode, image.size, border_color)
diff = ImageChops.difference(image, mask)
bounds = diff.getbbox()
if bounds:
return image.crop(bounds)
else:
return None
def DoComparison(self, image1=None, image2=None):
if not image1: image1 = self.image1
if not image2: image2 = self.image2
diffs = ImageChops.difference(image1, image2)
return {
"entropy": self.ImageEntropy(diffs),
"img": image2
}
def operationGrafic(imA,imB):
#imDest = ImageChops.multiply(imA, imB)
#imDest = ImageChops.difference(imA, imB)
#imDest = ImageChops.screen(imA, imB)
imDest = ImageChops.difference(imB, imA)
return imDest
def rmsdiff(im1, im2):
"Calculate the root-mean-square difference between two images"
diff = ImageChops.difference(im1, im2)
h = diff.histogram()
sq = (value * (idx ** 2) for idx, value in enumerate(h))
sum_of_squares = sum(sq)
rms = math.sqrt(sum_of_squares / float(im1.size[0] * im1.size[1]))
return rms
def calculate_rms_difference(self, im1, im2):
"""
Calculate the root-mean-square difference between two images.
If the images are exactly identical, the value is zero.
"""
h = ImageChops.difference(im1, im2).histogram()
# calculate rms
return math.sqrt(reduce(operator.add, map(lambda h, i: h * (i ** 2), h, range(256))) / (float(im1.size[0]) * im1.size[1]))
def rmsdiff(im1, im2):
"Calculate the root-mean-square difference between two images"
h = ImageChops.difference(im1, im2).histogram()
# calculate rms
return math.sqrt(reduce(operator.add,
map(lambda h, i: h*(i**2), h, range(256))) / (float(im1.size[0]) * im1.size[1]))
def do_difference(self):
"""usage: difference <image:pic1> <image:pic2>
Pop the two top images, push the difference image
"""
import ImageChops
image1 = self.do_pop()
image2 = self.do_pop()
self.push(ImageChops.difference(image1, image2))
def objectRecognize(dstImage, refImage, boundbox):
#
source = refImage.crop(boundbox)
dest = dstImage.crop(boundbox)
# Smooth operation
source = source.filter(ImageFilter.SMOOTH)
dest = dest.filter(ImageFilter.SMOOTH)
# Get difference between source image and reference image
imdiff = ImageChops.difference(source, dest)
# Increase the contrast of the difference image
enhancer = ImageEnhance.Contrast(imdiff)
imdiff = enhancer.enhance(1.1)
imdiff = imdiff.convert('1')
# Get bounding box of the object
box = imdiff.getbbox()
# bound box of recognized object
# Crop target object from the difference image
recogObject = imdiff.crop(box)
# Get global image statics
objStat = ImageStat.Stat(recogObject)
tarStat = ImageStat.Stat(imdiff)
# Get object region and object pixels occupied
cropPixel = objStat.count[0]
objectPixel = objStat.sum[0]
objectPixel = int(objectPixel) / 255
totalPixel = tarStat.count[0]
objectPixel2 = tarStat.sum[0]
objectPixel2 = int(objectPixel2) / 255
# Caculate object pixels percent
if float(cropPixel) > 0.0 and float(totalPixel) > 0.0:
percent = float(objectPixel) / float(cropPixel)
percent2 = float(objectPixel2) / float(totalPixel)
else:
percent = 0
percent = 0
print percent
print percent2
if percent >= 0.05 and percent2 >= 0.0005:
print "object detected\n"
return True
else:
print "nothing detected\n"
return False
return percent
def equal(self, img1, img2):
"""Compares two screenshots.
@param img1: screenshot to compare.
@param img2: screenshot to compare.
@return: equal status.
"""
if not HAVE_PIL:
return None
return ImageChops.difference(img1, img2).getbbox() is None
def isImagesIdentical(im1, im2):
if im1.size[0] == 0 or im1.size[1] == 0:
return False
if im2.size[0] == 0 or im2.size[1] == 0:
return False
bbox = ImageChops.difference(im1, im2).getbbox()
r = bbox is None
return r
def _diff_png(self, ref_path, result_path):
try:
import Image, ImageChops
except ImportError:
raise NotImplementedError
ref = Image.open(ref_path)
result = Image.open(result_path)
diff = ImageChops.difference(ref, result)
diff.save(result_path + '.diff', 'png')
def assertEqualImages(self, fn1, fn2):
if haveImageLibs:
im1 = Image.open(fn1)
im2 = Image.open(fn2)
return ImageChops.difference(im1, im2).getbbox() is None
else:
warnings.warn(
"**** IMPORT: Cannot import Image and/or ImageChops" +
", so Image comparisons in testAnalysis have been" +
" disabled.")
return True
def main():
'''Main Function'''
url1 = raw_input('Enter the link to the first image: ')
url2 = raw_input('Enter the link to the second image: ')
ext1 = extension(url1)
ext2 = extension(url2)
if string.upper(ext1) != string.upper(ext2):
print 'File-types dont match..!'
sys.exit(1)
file1 = 'im1' + ext1
file2 = 'im2' + ext2
urlretrieve(url1, file1)
urlretrieve(url2, file2)
diff_perc_seq = []
im1 = Image.open(file1)
im2 = Image.open(file2)
im1, im2 = make_even(im1, im2) #The image sizes are even now.
diff_image = ImageChops.difference(
im1,
im2) #diff_image contains the pixel level difference as an RGB tuple.
#getbbox returns box containing the non-zero regions of the image. If its none, difference is none..!
if diff_image.getbbox() is None:
print 'Mirror Images..! The images are 100% similar. Similarity Scale value - 100'
else:
pixel_tuple_seq = diff_image.getdata(
) #pixel_tuple_seq contains the list of difference pixel tuples(R, G, B).
pixel_rms_seq = map(
rms, pixel_tuple_seq
) #pixel_rms_seq contains the rms list of difference pixel tuples.
#The percentage of difference is found out using formula, perc = (value/255) * 100
for item in pixel_rms_seq:
diff_perc_seq.append(item / 255 * 100)
avg_diff = sum(diff_perc_seq) / len(
diff_perc_seq) #The total average difference is found out.
similarity = 100 - avg_diff
if avg_diff == 100:
print 'The images are completely dissimilar. Similarity scale value - 0'
else:
print 'The images are %.2f%% similar. Similarity scale value - %.2f ' % (
similarity, similarity)
os.system('rm ' + file1 + ' ' + file2)
def PreProcess(im):
im = NMode(im)
im1 = ndimage.grey_erosion(im, size=(10, 10))
scipy.misc.imsave("eroded.jpg", im1)
im1 = Image.open("eroded.jpg")
im = ImageOps.equalize(im, 0)
im = ImageChops.difference(im1, im)
#print ("image height %d and width %d\n"%(imh,imw))
im = GBinarization(im) #binarize the image
return im
def compare(self, img1, img2):
img = ImageChops.difference(img1, img2)
xsize, ysize = img.size
for s in range(0, xsize / 3):
s = s * 3
for m in range(0, ysize / 3):
m = m * 3
img.getpixel((s, m))
if g > 150:
return True
return False
def background_crop(image_name, new_name, bgcolor):
""" removes frame of bgcolor from and image and saves the image under new_name """
print('background_crop:' + image_name)
image = Image.open(image_name)
bg = Image.new("RGBA", image.size, bgcolor)
diff = ImageChops.difference(image, bg)
bbox = diff.getbbox()
if bbox:
new_image = image.crop(bbox)
new_image.save(new_name)
return bbox
def cropify(self):
"""Crops output images"""
print "crop images"
# Oh we are back!
for fpath in self._outpaths():
print "\t%s" % fpath
im = Image.open(fpath)
im = im.convert("RGBA")
bg = Image.new("RGBA", im.size, (255, 255, 255, 255))
diff = ImageChops.difference(im, bg)
bbox = diff.getbbox()
im2 = im.crop(bbox)
im2.save(fpath)
def cropImage(self, size):
""" Cropping image method will crop image based on the provided new size
@param size: the size of the new image
@param type: tuple (width, height)
"""
if self.__im.mode == "P":
self.__im = self.__im.convert("RGB")
bgColor = (255, 255, 255)
bg = Image.new("RGB", size, bgColor)
diff = ImageChops.difference(self.__im, bg)
bbox = diff.getbbox()
if bbox:
self.__im = self.__im.crop(bbox)
def demo1():
from PIL import Image
import ImageChops
path = "D:/code/image/image/"
im1 = Image.open(path + "1.jpg")
im2 = Image.open(path + "2.jpg")
diff = ImageChops.difference(im2, im1)
print diff
im1.show()
im2.show()
diff.show()
def apply(self):
keys = self._params.keys()
if ('width' in keys or 'height' in keys) and not 'crop' in keys:
if 'bgcolor' in keys:
bgcolor = hex_to_color(self._params['bgcolor'])
else:
bgcolor = (255, 255, 255, 255)
bg = Image.new(self._image.mode, self._image.size, bgcolor)
diff = ImageChops.difference(self._image, bg)
bbox = diff.getbbox()
return self._image.crop(bbox)
return self._image
def root_mean_square_difference(im1, im2):
try:
diff = ImageChops.difference(
im1, im2
) #Returns the absolute value of the difference between the two images
h = diff.histogram()
sq = (value * (idx**2) for idx, value in enumerate(h))
sum_of_squares = sum(sq)
rms = math.sqrt(sum_of_squares / float(im1.size[0] * im1.size[1]))
except ValueError:
return -1
else:
return rms