Reputation: 11
Im trying to insert one picture(transparent .png) into another on certain coordinates.
While the solution from How to add an image over another image using x,y coordinates?
frame[y: y+insert_size[1], x: x+insert_size[0]] = image
(where insert_size - width and height of inserted picture) works, i also dont want black pixels(thats how opencv represents transparent pixels) on the final image.
I wrote a function that iterates pixel by pixel, and while it works - it is horribly slow(it completes about 2 image inserts per second), code:
def insert_image(frame, image, insert_coordinates, masked_value):
img_height = len(image)
img_width = len(image[0])
mask = np.ndarray((3,), np.uint8, buffer=np.array(masked_value))
y_diff = 0 #current vertical position in insert picture
for y, line in enumerate(frame):
if y_diff == img_height-1:
continue #interested until last row
if y < insert_coordinates[1] or y > insert_coordinates[1]+img_height:
continue #interested only in rows that will be changed
else:
x_diff = 0 #current horizontal position in insert picture
for x, col in enumerate(line):
if x_diff == img_width-1:
continue #interested until last column
if x < insert_coordinates[0] or x > insert_coordinates[0]+img_width:
continue #interested only in columns that will be changed
else:
if (image[y_diff][x_diff] != mask).all():
frame[y][x] = image[y_diff][x_diff] #setting pixel value if its not of masked value
x_diff += 1
y_diff += 1
return frame
maybe there is a smarter way to do so? opencv version 4.5.0 numpy version 1.20.0rc1
UPDATE: By "insert" i do mean assign a pixel value from image to some pixel of frame. i added data and code for reproducible example(also modified function so its a bit faster):
code, requires opencv-python and numpy modules: example.py
import cv2
import numpy as np
import copy
def insert_image_v2(frame, image, insert_coordinates, masked_value):
img_height = len(image)
img_width = len(image[0])
mask = np.ndarray((3,), np.uint8, buffer=np.array(masked_value))
y_diff = 0
for y in range(insert_coordinates[1], insert_coordinates[1]+img_height, 1):
x_diff = 0
for x in range(insert_coordinates[0], insert_coordinates[0]+img_width, 1):
if (image[y_diff][x_diff] != mask).all():
frame[y][x] = image[y_diff][x_diff]
x_diff += 1
y_diff += 1
return frame
if __name__ == "__main__":
frame = cv2.imread('frame.png')
image = cv2.imread('image.png')
insert_size = (image.shape[0], image.shape[1])
insert_coordinates = (100, 100)
x = insert_coordinates[0]
y = insert_coordinates[1]
result1 = copy.deepcopy(frame)
result1[y: y+insert_size[1], x: x+insert_size[0]] = image
result2 = insert_image_v2(frame, image, insert_coordinates, [0,0,0])
cv2.imshow('result1', result1)
cv2.imshow('result2', result2)
cv2.imwrite('result1.jpg', result1)
cv2.imwrite('result2.jpg', result2)
print()
Upvotes: 1
Views: 193
Reputation: 11
Found a solution in Image alpha composite with OpenCV, it is about 20 times faster than what i had. code:
import cv2
import numpy as np
import time
def insert_image_v2(frame, image, insert_coordinates):
x = insert_coordinates[0]
y = insert_coordinates[1]
insert_size = (image.shape[1], image.shape[0])
background = frame[y: y+insert_size[1], x: x+insert_size[0]]
foreground = image
kernel = np.ones((5,5), np.uint8)
image_gray = cv2.cvtColor(foreground, cv2.COLOR_BGR2GRAY)
ret, mask = cv2.threshold(image_gray, 1, 255, cv2.THRESH_BINARY_INV)
opening = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
output = np.zeros(foreground.shape, dtype=foreground.dtype)
for i in range(3):
output[:,:,i] = background[:,:,i] * (opening/255)+foreground[:,:,i]*(1-opening/255)
frame[y: y+insert_size[1], x: x+insert_size[0]] = output
return frame
if __name__ == "__main__":
frame = cv2.imread('frame.png')
image = cv2.imread('image_1.png')
insert_size = (image.shape[0], image.shape[1])
insert_coordinates = (100, 100)
t1 = time.time()
frame = insert_image_v2(frame, image, insert_coordinates)
t2 = time.time()
print(f"{t2-t1}")
cv2.imshow('img', frame)
cv2.waitKey(0)
Upvotes: 0