def getFeatures(path, img=None, display=False):
if img is None:
img = mpimg.imread(path)
img = preproc.preproc(path, display=display)
'''img = cv2.imread('bw_image.png')
img = np.array(img, dtype=np.uint8)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
a=[]
x_coor=[]
indices = np.where(gray == [0])
print (indices)
coordinates = zip(indices[0], indices[1])
print (coordinates)
a=list(zip(indices[0], indices[1]))
print(len(a))
print(a[0])
for j in range(len(a)): # this loop makes a list of all x coordinates
x_coor.append((a[j][0]))
print(x_coor)
with open("file.txt", "w") as output:
output.write(str(a))'''
ratio = features.Ratio(img)
centroid = features.Centroid(img)
eccentricity, solidity = features.EccentricitySolidity(img)
skewness, kurtosis = features.SkewKurtosis(img)
retVal = (ratio, centroid, eccentricity, solidity, skewness, kurtosis)
return retVal
def getFeatures(path, img=None, display=False):
if img is None:
img = mpimg.imread(path)
img = preproc.preproc(path, display=display)
ratio = features.Ratio(img)
centroid = features.Centroid(img)
eccentricity, solidity = features.EccentricitySolidity(img)
skewness, kurtosis = features.SkewKurtosis(img)
retVal = (ratio, centroid, eccentricity, solidity, skewness, kurtosis)
return retVal
def getFeatures(path, img=None, display=False):
if img is None:
img = mpimg.imread(path)
# print("(ann)")
img = preproc.preproc(path, display=display)
ratio = features.Ratio(img)
centroid = features.Centroid(img)
eccentricity, solidity = features.EccentricitySolidity(img)
skewness, kurtosis = features.SkewKurtosis(img)
# print("(svm)")
aspect_ratio, bounding_rect_area, hull_area, contour_area = features.get_contour_features(
img, display=display)
retVal = (ratio, centroid, eccentricity, solidity, skewness, kurtosis,
aspect_ratio, hull_area / bounding_rect_area,
contour_area / bounding_rect_area)
return retVal
des_list = []
for im in genuine_image_features[i]:
image_path = genuine_image_paths + "/" + im['name']
preprocessed_image = preprocess_image(image_path)
hash = imagehash.phash(Image.open(image_path))
aspect_ratio, bounding_rect_area, convex_hull_area, contours_area = \
features.get_contour_features(preprocessed_image.copy(), display=False)
hash = int(str(hash), 16)
im['hash'] = hash
im['aspect_ratio'] = aspect_ratio
im['hull_area/bounding_area'] = convex_hull_area / bounding_rect_area
im['contour_area/bounding_area'] = contours_area / bounding_rect_area
im['ratio'] = features.Ratio(preprocessed_image.copy())
im['centroid_0'],im['centroid_1']=features.Centroid(preprocessed_image.copy())
im['eccentricity'],im['solidity']=features.EccentricitySolidity(preprocessed_image.copy())
(im['skewness_0'],im['skewness_1']),(im['kurtosis_0'],im['kurtosis_1']) = features.SkewKurtosis(preprocessed_image.copy())
# im_contour_features.append([hash, aspect_ratio, convex_hull_area / bounding_rect_area, contours_area / bounding_rect_area])
im_contour_features.append([aspect_ratio, convex_hull_area / bounding_rect_area, contours_area / bounding_rect_area, im['ratio'],im['centroid_0'],im['centroid_1'],im['eccentricity'],im['solidity'],im['skewness_0'],im['skewness_1'],im['kurtosis_0'],im['kurtosis_1']])
des_list.append(sift(preprocessed_image.copy(), image_path))
# print(len(des_list))
for im in forged_image_features[i]:
image_path = forged_image_paths + "/" + im['name']
preprocessed_image = preprocess_image(image_path)