123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118 |
- import cv2
- import numpy as np
- from PIL import Image
- import math
- import numpy as np
- # import torch
- # from torchvision import transforms
- def rotate_image(image, angle, center = None, scale = 1.0):
- (h, w) = image.shape[:2]
- if center is None:
- center = (w / 2, h / 2)
- # Perform the rotation
- M = cv2.getRotationMatrix2D(center, angle, scale)
- rotated = cv2.warpAffine(image, M, (w, h))
- return rotated
- class watermark_image:
- def __init__(self, logo_path, size=0.3, oritation="DR", margin=(5,20,20,20), angle=15, rgb_weight=(0,1,1.5), input_frame_shape=None) -> None:
- logo_image = cv2.imread(logo_path, cv2.IMREAD_UNCHANGED)
- h,w,c = logo_image.shape
- if angle%360 != 0:
- new_h = w*math.sin(angle/180*math.pi) + h*math.cos(angle/180*math.pi)
- pad_h = int((new_h-h)//2)
-
- padding = np.zeros((pad_h, w, c), dtype=np.uint8)
- logo_image = cv2.vconcat([logo_image, padding])
- logo_image = cv2.vconcat([padding, logo_image])
-
- logo_image = rotate_image(logo_image, angle)
- print(logo_image.shape)
- self.logo_image = logo_image
-
- if self.logo_image.shape[2] < 4:
- print("No alpha channel found!")
- self.logo_image = self.__addAlpha__(self.logo_image) #add alpha channel
- self.size = size
- self.oritation = oritation
- self.margin = margin
- self.ori_shape = self.logo_image.shape
- self.resized = False
- self.rgb_weight = rgb_weight
- self.logo_image[:, :, 2] = self.logo_image[:, :, 2]*self.rgb_weight[0]
- self.logo_image[:, :, 1] = self.logo_image[:, :, 1]*self.rgb_weight[1]
- self.logo_image[:, :, 0] = self.logo_image[:, :, 0]*self.rgb_weight[2]
- if input_frame_shape is not None:
- logo_w = input_frame_shape[1] * self.size
- ratio = logo_w / self.ori_shape[1]
- logo_h = int(ratio * self.ori_shape[0])
- logo_w = int(logo_w)
- size = (logo_w, logo_h)
- self.logo_image = cv2.resize(self.logo_image, size, interpolation = cv2.INTER_CUBIC)
- self.resized = True
- if oritation == "UL":
- self.coor_h = self.margin[1]
- self.coor_w = self.margin[0]
- elif oritation == "UR":
- self.coor_h = self.margin[1]
- self.coor_w = input_frame_shape[1] - (logo_w + self.margin[2])
- elif oritation == "DL":
- self.coor_h = input_frame_shape[0] - (logo_h + self.margin[1])
- self.coor_w = self.margin[0]
- else:
- self.coor_h = input_frame_shape[0] - (logo_h + self.margin[3])
- self.coor_w = input_frame_shape[1] - (logo_w + self.margin[2])
- self.logo_w = logo_w
- self.logo_h = logo_h
- self.mask = self.logo_image[:,:,3]
- self.mask = cv2.bitwise_not(self.mask//255)
-
- def apply_frames(self, frame):
- if not self.resized:
- shape = frame.shape
- logo_w = shape[1] * self.size
- ratio = logo_w / self.ori_shape[1]
- logo_h = int(ratio * self.ori_shape[0])
- logo_w = int(logo_w)
- size = (logo_w, logo_h)
- self.logo_image = cv2.resize(self.logo_image, size, interpolation = cv2.INTER_CUBIC)
- self.resized = True
- if self.oritation == "UL":
- self.coor_h = self.margin[1]
- self.coor_w = self.margin[0]
- elif self.oritation == "UR":
- self.coor_h = self.margin[1]
- self.coor_w = shape[1] - (logo_w + self.margin[2])
- elif self.oritation == "DL":
- self.coor_h = shape[0] - (logo_h + self.margin[1])
- self.coor_w = self.margin[0]
- else:
- self.coor_h = shape[0] - (logo_h + self.margin[3])
- self.coor_w = shape[1] - (logo_w + self.margin[2])
- self.logo_w = logo_w
- self.logo_h = logo_h
- self.mask = self.logo_image[:,:,3]
- self.mask = cv2.bitwise_not(self.mask//255)
-
- original_frame = frame[self.coor_h:(self.coor_h+self.logo_h), self.coor_w:(self.coor_w+self.logo_w),:]
- blending_logo = cv2.add(self.logo_image[:,:,0:3],original_frame,mask = self.mask)
- frame[self.coor_h:(self.coor_h+self.logo_h), self.coor_w:(self.coor_w+self.logo_w),:] = blending_logo
- return frame
-
- def __addAlpha__(self, image):
- shape = image.shape
- alpha_channel = np.ones((shape[0],shape[1],1),np.uint8)*255
- return np.concatenate((image,alpha_channel),2)
|