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)