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imagecompare.py
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imagecompare.py
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# from https://stackoverflow.com/questions/5524179/how-to-detect-motion-between-two-pil-images-wxpython-webcam-integration-exampl
import math, sys, numpy as np
import PIL.Image, PIL.ImageChops
sys.modules["Image"] = PIL.Image
sys.modules["ImageChops"] = PIL.ImageChops
#from scipy.misc import imread
#from scipy.linalg import norm
#from scipy import sum, average
DEFAULT_DEVICE_WIDTH = 640
DEFAULT_DEVICE_HEIGHT = 480
"""
class Images:
def __init__(self, image1, image2, threshold=98, grayscale=True):
if type(image1) == str:
self.image1 = sys.modules["Image"].open(image1)
self.image2 = sys.modules["Image"].open(image2)
if grayscale:
self.image1 = self.DoGrayscale(imread(image1).astype(float))
self.image2 = self.DoGrayscale(imread(image2).astype(float))
else:
self.image1 = imread(image1).astype(float)
self.image2 = imread(image2).astype(float)
self.threshold = threshold
def DoComparison(self, image1=None, image2=None):
if image1: image1 = self.Normalize(image1)
else: image1 = self.Normalize(self.image1)
if image2: image2 = self.Normalize(image2)
else: image2 = self.Normalize(self.image2)
diff = image1 - image2
m_norm = sum(abs(diff))
z_norm = norm(diff.ravel(), 0)
return (m_norm, z_norm)
def DoGrayscale(self, arr):
if len(arr.shape) == 3:
return average(arr, -1)
else:
return arr
def Normalize(self, arr):
rng = arr.max()-arr.min()
amin = arr.min()
return (arr-amin)*255/rng
"""
class Images2:
def __init__(self, image1, image2, threshold=98, grayscale=True):
self.image1 = image1
if type(image1) == str:
self.image1 = sys.modules["Image"].open(self.image1)
self.image2 = image2
if type(image2) == str:
self.image2 = sys.modules["Image"].open(image2)
self.threshold = threshold
def DoComparison(self, image1=None, image2=None):
if not image1: image1 = self.image1
if not image2: image2 = self.image2
diffs = sys.modules["ImageChops"].difference(image1, image2)
return self.ImageEntropy(diffs)
def ImageEntropy(self, image):
w,h = image.size
a = np.array(image.convert('RGB')).reshape((w*h,3))
h,e = np.histogramdd(a, bins=(16,)*3, range=((0,256),)*3)
prob = h/np.sum(h) # normalize
prob = prob[prob>0] # remove zeros
return -np.sum(prob*np.log2(prob))
def OldImageEntropy(self, image):
histogram = image.histogram()
histlength = sum(histogram)
probability = [float(h) / histlength for h in histogram]
return -sum([p * math.log(p, 2) for p in probability if p != 0])
class Images3:
def __init__(self, image1, image2, threshold=8):
self.image1 = image1
if type(image1) == str:
self.image1 = sys.modules["Image"].open(self.image1)
self.image2 = image2
if type(image2) == str:
self.image2 = sys.modules["Image"].open(image2)
self.threshold = threshold
def DoComparison(self, image1=None, image2=None):
if not image1: image1 = self.image1
if not image2: image2 = self.image2
image = image1
monoimage1 = image1.convert("P", palette=sys.modules["Image"].ADAPTIVE, colors=2)
monoimage2 = image2.convert("P", palette=sys.modules["Image"].ADAPTIVE, colors=2)
imgdata1 = monoimage1.getdata()
imgdata2 = monoimage2.getdata()
changed = 0
i = 0
acc = 3
while i < DEFAULT_DEVICE_WIDTH * DEFAULT_DEVICE_HEIGHT:
now = imgdata1[i]
prev = imgdata2[i]
if now != prev:
x = (i % DEFAULT_DEVICE_WIDTH)
y = (i / DEFAULT_DEVICE_HEIGHT)
try:
#if self.view == "normal":
image.putpixel((x,y), (0,0,256))
#else:
# monoimage.putpixel((x,y), (0,0,256))
except:
pass
changed += 1
i += 1
percchange = float(changed) / float(DEFAULT_DEVICE_WIDTH * DEFAULT_DEVICE_HEIGHT)
return percchange