<reponame>johnpaulbin/SimSwap

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])
       
        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       = (0, 0)
        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, 0.000001, 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 = 0.0
            self.logo_h = 0.0
            self.mask = self.logo_image[:,:,3]
            self.mask = cv2.bitwise_not(self.mask//255)
            
    def apply_frames(self, frame):
        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)


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])
       
        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       = (0, 0)
        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, 0.000001, 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 = 0.0
            self.logo_h = 0.0
            self.mask = self.logo_image[:,:,3]
            self.mask = cv2.bitwise_not(self.mask//255)
            
    def apply_frames(self, frame):
        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)
# import torch # from torchvision import transforms # Perform the rotation #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]) #add alpha channel