def rotate(image, angle, center=None, scale=1.0):
assert image.dtype == np.uint8
"""Copy paste of imutils method but with INTER_NEAREST and BORDER_CONSTANT flags"""
# grab the dimensions of the image
(h, w) = image.shape[:2]
# if the center is None, initialize it as the center of
# the image
if center is None:
center = (w // 2, h // 2)
# perform the rotation
M = cv.getRotationMatrix2D(center, angle, scale)
rotated = cv.warpAffine(
image,
M,
(w, h),
flags=cv.INTER_NEAREST,
borderMode=cv.BORDER_CONSTANT,
borderValue=0,
)
# return the rotated image
return rotated
def rotate_90(self, image):
#cv2.resize(image, (800, 800))
#cv2.imshow('dddimage', image)
#cv2.waitKey(0)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.bitwise_not(gray)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
coords = np.column_stack(np.where(thresh > 0))
angle = cv2.minAreaRect(coords)[-1]
if angle < -45:
angle = -(90 + angle)
else:
angle = -angle
angle = 90
(h, w) = image.shape[:2]
#print(h, w)
center = (w / 2, h / 2)
M = cv2.getRotationMatrix2D(center, angle, 1.0)
rotated = cv2.warpAffine(image, M, (w, h))
#cv2.resize(rotated, (800, 1000))
#cv2.imshow('rotated', rotated)
#cv2.waitKey(0)
file_path_name = 'img/rotate_img.jpg'
cv2.imwrite(file_path_name, rotated)
return file_path_name
def deskew(img):
skew_img = cv2.bitwise_not(img) # Invert image
# grab the (x, y) coordinates of all pixel values that
# are greater than zero, then use these coordinates to
# compute a rotated bounding box that contains all
# coordinates
coords = np.column_stack(np.where(skew_img > 0))
angle = cv2.minAreaRect(coords)[-1]
# the `cv2.minAreaRect` function returns values in the
# range [-90, 0); as the rectangle rotates clockwise the
# returned angle trends to 0 -- in this special case we
# need to add 90 degrees to the angle
if angle < -45:
angle = -(90 + angle)
# otherwise, just take the inverse of the angle to make
# it positive
else:
angle = -angle
# rotate the image to deskew it
(h, w) = img.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, angle, 1.0)
rotated = cv2.warpAffine(img, M, (w, h),
flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
return angle, rotated
def correct_skew(image, delta=1, limit=5):
def determine_score(arr, angle):
data = inter.rotate(arr, angle, reshape=False, order=0)
histogram = np.sum(data, axis=1)
score = np.sum((histogram[1:] - histogram[:-1]) ** 2)
return histogram, score
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
scores = []
angles = np.arange(-limit, limit + delta, delta)
for angle in angles:
histogram, score = determine_score(thresh, angle)
scores.append(score)
best_angle = angles[scores.index(max(scores))]
(h, w) = image.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, best_angle, 1.0)
rotated = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC, \
borderMode=cv2.BORDER_REPLICATE)
return best_angle, rotated
def random_image_rotate(img, rotation_center):
angle_deg = _random_normal_crop(1, 10)[0]
M = cv2.getRotationMatrix2D(rotation_center, angle_deg, 1.0)
nimg = cv2.warpAffine(img, M, dsize=img.shape[0:2])
if len(nimg.shape) < 3:
nimg = nimg[:, :, np.newaxis]
return nimg #.reshape(img.shape)
def rotate_image(img, angle, crop):
h, w = img.shape[:2]
# 旋转角度的周期是360°
angle %= 360
# 用OpenCV内置函数计算仿射矩阵
M_rotate = cv.getRotationMatrix2D((w / 2, h / 2), angle, 1)
# 得到旋转后的图像
img_rotated = cv.warpAffine(img, M_rotate, (w, h))
# 如果需要裁剪去除黑边
if crop:
angle_crop = angle % 180 # 对于裁剪角度的等效周期是180°
if angle_crop > 90: # 并且关于90°对称
angle_crop = 180 - angle_crop
theta = angle_crop * np.pi / 180.0 # 转化角度为弧度
hw_ratio = float(h) / float(w) # 计算高宽比
tan_theta = np.tan(theta) # 计算裁剪边长系数的分子项
numerator = np.cos(theta) + np.sin(theta) * tan_theta
r = hw_ratio if h > w else 1 / hw_ratio # 计算分母项中和宽高比相关的项
denominator = r * tan_theta + 1 # 计算分母项
crop_mult = numerator / denominator # 计算最终的边长系数
w_crop = int(round(crop_mult * w)) # 得到裁剪区域
h_crop = int(round(crop_mult * h))
x0 = int((w - w_crop) / 2)
y0 = int((h - h_crop) / 2)
img_rotated = crop_image(img_rotated, x0, y0, w_crop, h_crop)
return img_rotated
def get_frame(self) -> np.array:
# calculate the ROI points around self.pos
x_0, x_1, y_0, y_1 = self.calculate_ROI()
# add border to the ROI
y_0 += int(self.crop_dimension / 2)
x_0 += int(self.crop_dimension / 2)
y_1 += int(self.crop_dimension * 3 / 2)
x_1 += int(self.crop_dimension * 3 / 2)
# crop out and copy of the area surrounding self.pos plus a border
frame_to_rotate = self.borderd_picture[y_0:y_1, x_0:x_1].copy()
# rotate the frame to the given viewangle and extract the resultframe
shape = frame_to_rotate.shape
rotation = cv2.getRotationMatrix2D((shape[0] / 2, shape[1] / 2),
self.angle, 1)
# avoid overflow
self.avoid_overflow()
# Perform actual rotation on cropped part of the image using the rotation matrix.
# This will cut off parts of the image that don't fit a recangular bound (worst case: 45° rotation).
# This is why we added a border to the cropped out area.
result = cv2.warpAffine(frame_to_rotate, rotation,
(self.cols, self.rows))
# crop out the actual area of self.crop_dimension, cutting away the border
return result[int(self.crop_dimension / 2 +
self.aspect_ratio_diff):int(self.crop_dimension * 3 /
2),
int(self.crop_dimension / 2):int(self.crop_dimension *
3 / 2)]
def rotate_image(self, image, angle):
"""
Rotates the given image by the input angle in the counter-clockwise direction
Parameters
----------
image : ndim np.array
image to be rotated
angle : float
angle of rotation as degrees.
Returns
-------
rotated image as np.array
"""
# create an tuple that contains height/2, width/2
image_center = tuple(np.array(image.shape[1::-1]) / 2)
# rot_mat 2x3 rotation mattrix
rot_mat = cv2.getRotationMatrix2D(image_center, angle, 1.0)
# apply the affine transformation to the image
# size of the output image image.shape[1::-1]
result = cv2.warpAffine(image,
rot_mat,
image.shape[1::-1],
flags=cv2.INTER_LINEAR)
return result
def alignment_matrix(self) -> np.array:
"""
Returns a transformation matrix which can be used to align images
based on the position of the eyes.
"""
# Get the center of the eyes
left_eye = Point.mean(self.left_eye)
right_eye = Point.mean(self.right_eye)
face_square = self.square()
# Compute tilt
delta_y = right_eye.y - left_eye.y
delta_x = right_eye.x - left_eye.x
angle = np.degrees(np.arctan2(delta_y, delta_x))
# Normalized eye positions
out_left_eye_x, out_left_eye_y = 0.3, 0.2
out_right_eye_x, out_right_eye_y = 1.0 - out_left_eye_x, 1.0 - out_left_eye_y
# Compute scale of output image
dist = np.sqrt((delta_x**2) + (delta_y**2))
out_dist = (out_right_eye_x - out_left_eye_x) * face_square.width
scale = out_dist / dist
# Compute rotation center point
eyes_center = Point.mean([left_eye, right_eye])
# Compute rotation matrix
matrix = cv2.getRotationMatrix2D(eyes_center, angle, scale)
# Update translation components
matrix[0, 2] += (face_square.width * 0.5 - eyes_center.x)
matrix[1, 2] += (face_square.height * out_left_eye_y - eyes_center.y)
return matrix
def cropRois(image, rects, multHeight=0.73, multWidth=0.97, topHeightCrop=30):
crops = []
data = {}
# TODO cut off angle outliers here too
angles = []
for r in rects:
box = rect2Box(r)
W = r[1][0]
H = r[1][1]
Xs = [i[0] for i in box]
Ys = [i[1] for i in box]
x1 = min(Xs)
x2 = max(Xs)
y1 = min(Ys)
y2 = max(Ys)
rotated = False
angle = r[2]
if angle < -45:
angle += 90
rotated = True
# calc the centroid
center = (int((x1 + x2) / 2), int((y1 + y2) / 2))
size = (int((x2 - x1)), int((y2 - y1)))
#cv2.circle(image, center, 2, 255, -1)
M = cv2.getRotationMatrix2D((size[0] / 2, size[1] / 2), angle, 1.0)
# prepare the crop
cropped = cv2.getRectSubPix(image, size, center)
cropped = cv2.warpAffine(cropped, M, size)
croppedW = W if not rotated else H
croppedH = H if not rotated else W
ratio = float(croppedW) / (croppedH)
area = float(croppedW) * croppedH
# if in the ratio
if (ratio > 2 and ratio < 16):
#text = "{0:.2f}-{1:.2f} ".format(ratio, area)
#cv2.putText(image, text, center, cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
croppedRotated = cv2.getRectSubPix(
cropped,
(int(croppedW * multWidth),
int(croppedH *
multHeight if croppedH < topHeightCrop else croppedH *
0.9)), (size[0] / 2, size[1] / 2))
# save the angles to calc the avg/std
angles.append(angle)
# save the crops
crops.append(croppedRotated)
# will process from top to bottom, so save it to sort later
data[y1] = [croppedRotated, area, ratio, angle]
return data, np.mean(np.array(angles)), np.std(np.array(angles))
def rotate(xb, yb, angle):
M_rotate = cv2.getRotationMatrix2D((size / 2, size / 2), angle, 1)
xb = cv2.warpAffine(xb, M_rotate, (size, size))
yb = cv2.warpAffine(yb, M_rotate, (size, size))
return xb, yb
def rotate_image(image, angle):
image_center = tuple(np.array(image.shape[1::-1]) / 2)
rot_mat = cv2.getRotationMatrix2D(image_center, angle, 1.0)
result = cv2.warpAffine(image,
rot_mat,
image.shape[1::-1],
flags=cv2.INTER_LINEAR)
return result
def rotate(img, angle, rotPoint=None):
(height, width) = img.shape[:2]
if rotPoint is None:
rotPoint = (width//2, height//2)
rotMat = cv.getRotationMatrix2D(rotPoint, angle, 1.0)
dimensions = (width, height)
return cv.warpAffine(img, rotMat, dimensions)
def crop_and_show_images():
show_images = True
video_path = Path(
"/Users/sebastian/PycharmProjects/forgerydetection/722_458")
with open(video_path / "relative_bb.json", "r") as f:
relative_bb = json.load(f)
shape_predictor = dlib.shape_predictor(
"/Users/sebastian/PycharmProjects/forgerydetection/shape_predictor_68_face_landmarks.dat"
)
for image in tqdm(sorted(video_path.glob("*.png"))):
img = cv2.imread(str(image))
total_width, total_height = len(img[0]), len(img)
# show video_crop image
if show_images:
cv2.imshow(str(image.name), img)
cv2.waitKey(0)
cv2.destroyAllWindows()
relative_bb_values = relative_bb[str(image.with_suffix("").name)]
x, y, w, h = calculate_relative_bb(total_width, total_height,
relative_bb_values)
# show local bb_image
new_image = img[y:y + w, x:x + h]
if show_images:
cv2.imshow(str(image.name), new_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
# show aligned global_image
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
box = dlib.rectangle(left=x, top=y, right=x + w, bottom=y + h)
shape = shape_predictor(gray, box)
shape = shape_to_np(shape)
left_eye = np.mean(shape[36:42], axis=0)
right_eye = np.mean(shape[42:48], axis=0)
dx, dy = right_eye - left_eye
# Calculate rotation angle with x & y component of the eyes vector
angle = np.rad2deg(np.arctan2(dy, dx))
center = np.mean(shape, axis=0)
R = cv2.getRotationMatrix2D(tuple(center), angle, 1.0)
aligned_image = cv2.warpAffine(img, R, (total_width, total_height))
if show_images:
cv2.imshow(str(image.name), aligned_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
aligned_image = aligned_image[y:y + w, x:x + h]
if show_images:
cv2.imshow(str(image.name), aligned_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
def rotate(image, angle, center=None, scale=1.0):
(h, w) = image.shape[:2]
if center is None:
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, angle, scale)
rotated = cv2.warpAffine(image, M, (w, h))
return rotated
def rotation(image, angle):
__img = cv.imread(image)
__rows,__cols,__colors = __img.shape
__M = cv.getRotationMatrix2D((__cols/2,__rows/2),angle,1)
__img = cv.warpAffine(__img,__M,(__cols,__rows))
return __img
def rotate(self):
angle = np.random.randint(-25, 25)
rows, cols = self.input_image.shape
m = getRotationMatrix2D(center=(cols / 2, rows / 2),
angle=angle,
scale=1)
return warpAffine(self.input_image, m,
(cols, rows)), warpAffine(self.label, m,
(cols, rows))
def nDegreeRotation(self, image, degree):
""" NDEGREEROTATION FUNCTION """
(rows, cols) = image.shape[:2]
matrix = cv2.getRotationMatrix2D((cols / 2, rows / 2), degree, 1)
rotation = cv2.warpAffine(image,
matrix, (cols, rows),
borderValue=(255, 255, 255))
return rotation
def motion_blur(gray, degree, angle):
gray = np.array(gray)
M = cv2.getRotationMatrix2D((round(degree / 2), round(degree / 2)), angle, 1)
motion_blur_kernel = np.diag(np.ones(degree))
motion_blur_kernel = cv2.warpAffine(motion_blur_kernel, M, (degree, degree))
PSF = motion_blur_kernel / degree
blurred = cv2.filter2D(gray, -1, PSF)
blurred = cv2.normalize(blurred,None, 0, 255, cv2.NORM_MINMAX)
blurred = np.array(blurred, dtype=np.uint8)
return blurred,PSF
def rotate(self, image, angle=0, center=None, scale=0.9):
#rotate the image with "angle"
rows, cols = image.shape[:2]
while angle == 0:
angle = random.randrange(-180, 180)
if center is None:
center = (cols / 2, rows / 2)
#calculated the matrix
Martrix_rot = cv2.getRotationMatrix2D(center, angle, scale)
rotated = cv2.warpAffine(image, Martrix_rot, (cols, rows))
return rotated
def deskew(image):
coords = np.column_stack(np.where(image > 0))
angle = cv2.minAreaRect(coords)[-1]
if angle < -45:
angle = -(90 + angle)
else:
angle = -angle
(h, w) = image.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, angle, 1.0)
rotated = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
return rotated
def rot(image,image_width,image_height,angle):
image_heightNew = int(image_width * fabs(sin(radians(angle))) + image_height * fabs(cos(radians(angle))))
image_widthNew = int(image_height * fabs(sin(radians(angle))) + image_width * fabs(cos(radians(angle))))
center = (image_width/2,image_height/2)
M = cv2.getRotationMatrix2D(center, angle, 1)
M[0,2] += (image_widthNew - image_width)/2
M[1,2] += (image_heightNew - image_height)/2
rotated = cv2.warpAffine(image,M,(image_widthNew, image_heightNew), borderValue = (255, 255, 255))
return rotated
def rotate(img, angle, scale=1.0):
#旋转
height, width = img.shape[:2] #获取图像的高和宽
center = (width / 2, height / 2) #取图像的中点
M = cv.getRotationMatrix2D(center, angle, scale) #获得图像绕着某一点的旋转矩阵
rotated = cv.warpAffine(img, M, (height, width))
#cv.warpAffine()的第二个参数是变换矩阵,第三个参数是输出图像的大小
# cv.imshow("overturn-hv", rotated)
# cv.waitKey(0)
# cv.destroyAllWindows()
return rotated
def image_transformation(file):
# open image and apply filter
img = cv2.imdecode(numpy.frombuffer(file, numpy.uint8),
cv2.IMREAD_UNCHANGED)
img = cv2.resize(img, None, fx=1.2, fy=1.2, interpolation=cv2.INTER_CUBIC)
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
kernel = numpy.ones((1, 1), numpy.uint8)
img = cv2.dilate(img, kernel, iterations=1)
img = cv2.erode(img, kernel, iterations=1)
img = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)
# get coordinates
coords = numpy.column_stack(numpy.where(img > 0))
angle = cv2.minAreaRect(coords)[-1]
# the `cv2.minAreaRect` function returns values in the
# range [-90, 0); as the rectangle rotates clockwise the
# returned angle trends to 0 -- in this special case we
# need to add 90 degrees to the angle
if angle < -45:
angle = -(90 + angle)
# otherwise, just take the inverse of the angle to make
# it positive
else:
angle = -angle
(h, w) = img.shape[:2]
center = (w // 2, h // 2)
m = cv2.getRotationMatrix2D(center, angle, 1.0)
rotated = cv2.warpAffine(img,
m, (w, h),
flags=cv2.INTER_CUBIC,
borderMode=cv2.BORDER_REPLICATE)
img = rotated
ret, thresh_binary = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
value_thresh_binary = cv2.Laplacian(thresh_binary, cv2.CV_64F).var()
blur = cv2.GaussianBlur(img, (5, 5), 0)
# blur = gaussian_blur(img, 5)
# img = otsu(blur)
ret3, thresh_otsu = cv2.threshold(blur, 0, 255,
cv2.THRESH_BINARY + cv2.THRESH_OTSU)
value_otsu = cv2.Laplacian(thresh_otsu, cv2.CV_64F).var()
if value_thresh_binary > value_otsu:
print(value_thresh_binary)
img = thresh_binary
else:
print(value_otsu)
img = thresh_otsu
return img
def __make_batch(self, data):
if data is not None:
batch = []
resized_batch = []
for im_p in data:
original_image = cv.imread(im_p)
if self.__augmentation_settings:
if random.random(
) >= self.__augmentation_settings.blur_chance:
original_image = cv.GaussianBlur(
original_image, (3, 3),
self.__augmentation_settings.blur_amount)
if random.random(
) >= self.__augmentation_settings.flip_chance:
original_image = cv.flip(original_image,
random.randint(-1, 1))
if random.random(
) >= self.__augmentation_settings.rotation_chance:
rows, cols, c = original_image.shape
M = cv.getRotationMatrix2D(
(cols / 2, rows / 2),
random.random() *
self.__augmentation_settings.rotation_ammount, 1)
original_image = cv.warpAffine(original_image, M,
(cols, rows))
batch.append(
cv.cvtColor(original_image, cv.COLOR_BGR2RGB) / 127.5 -
1.0)
if self.__secondary_size:
resized_batch.append(
cv.cvtColor(
cv.resize(
original_image,
dsize=(self.__secondary_size[0],
self.__secondary_size[1]),
interpolation=(cv.INTER_AREA if (
original_image.shape[0] > self.
__secondary_size[1] and original_image.
shape[1] > self.__secondary_size[0]
) else cv.INTER_CUBIC)), cv.COLOR_BGR2RGB) /
127.5 - 1.0)
if batch: self.__batches.append(np.array(batch).astype(np.float32))
if resized_batch:
self.__resized_batches.append(
np.array(resized_batch).astype(np.float32))
def img_rotate(img, angle):
'''
@param:原始图像
@param:旋转角度
return dstimg,M
'''
h, w, = img.shape[0], img.shape[1]
cx, cy = int(w / 2), int(h / 2)
# 仿射变换矩阵
M = cv2.getRotationMatrix2D((cx, cy), angle, 1)
rotated = cv2.warpAffine(img, M, (w, h))
# cv2.imshow('rotate',rotated)
# cv2.waitKey()
return rotated, M
def draw_image(src, dst, x, y, rotation):
(h, w) = src.shape[:2]
bounds = np.array([[-w / 2, -h / 2], [w / 2, -h / 2], [w / 2, h / 2],
[-w / 2, h / 2]])
for i, v in enumerate(bounds):
bounds[i] = rotate(v, rotation) + (x, y)
x_min, x_max = min(np.concatenate(bounds[:, :1])), max(
np.concatenate(bounds[:, :1]))
y_min, y_max = min(np.concatenate(bounds[:, 1:])), max(
np.concatenate(bounds[:, 1:]))
M = cv2.getRotationMatrix2D((w / 2, h / 2), np.rad2deg(rotation), 1.0)
src_rotated = cv2.warpAffine(src, M,
(int(x_max - x_min), int(y_max - y_min)))
(h, w) = src_rotated.shape[:2]
blit_image(src_rotated, dst, int(x - w / 2), int(y - h / 2))
def __rotation(self, image, angle, center=None, scale=1.0):
"""
旋转图片
:param image:待处理图像
:param angle:角度
:param center:中心点
:param scale:
:return:the rotated image
"""
(height, width) = image.shape[:2] # 长宽
if center is None: # 默认中心为中点
center = (width // 2, height // 2)
Matrix = getRotationMatrix2D(center, angle, scale)
rotated = warpAffine(image, Matrix, (width, height))
return rotated
def dataset_augmentation(images_array):
dataset = []
for image in images_array:
horizontal_flipped_img = cv2.flip(image, 1)
dataset.append(horizontal_flipped_img)
vertical_flipped_img = cv2.flip(image, 0)
dataset.append(vertical_flipped_img)
angles = [10, 15, 20]
for angle in angles:
height, width = image.shape[:2]
matrix = cv2.getRotationMatrix2D((int(width/2), int(height/2)), angle, 1)
rotated_img = cv2.warpAffine(image, matrix, (width, height))
dataset.append(rotated_img)
return np.array(dataset)
def imageRotate(image, angle, central):
#图像旋转操作, 同时返回图像中心旋转后的坐标
row, col = image.shape[:2]
# print(image.shape)
#getRotationMatrix2D()，这个函数需要三个参数，
ang = angle - 90
#旋转中心，旋转角度（逆 时针），旋转后图像的缩放比例，比如下例为1：
M = cv2.getRotationMatrix2D((central[0], central[1]), ang, 1)
#第一个参数为输入图像，第二个参数为仿射变换矩阵，第三个参数为变换后大小,第四个参数为边界外填充颜色
dst = cv2.warpAffine(image, M, (row, col), borderValue=(255, 255, 255))
central = np.array(central)
central = np.r_[central, [1]]
trans_c = np.matmul(np.array(M), central.T)
ret = tuple(trans_c)
ret1 = int(ret[0])
ret2 = int(ret[1])
return dst, (ret1, ret2)
