I have 2 images (icon.png and background.png). In the background.png image, there is a blank area which will be the place for the icon.png to be pasted using PIL (Python). However, the icon.png is a little bit bigger compared to the blank frame in the background.png. How can I paste and make the icon.png smaller so it can fit with the frame?
My code so far:
icon = Image.open("./icon.png")
background = Image.open("./background.png")
mask = Image.new("L", icon.size, 0)
draw = ImageDraw.Draw(mask)
draw.ellipse((0, 0) + icon.size, fill=255)
back_im = background.copy()
back_im.paste(icon, (200, 100), mask=mask)
back_im.save("./back_im.png")
Use resize after read the icon image to fit the desired size:
from PIL import Image, ImageDraw
iconSize=(200,100)
icon = Image.open("./icon.png")
icon=icon.resize(iconSize)
background = Image.open("./background.png")
mask = Image.new("L", icon.size, 0)
draw = ImageDraw.Draw(mask)
draw.ellipse((0, 0) + icon.size, fill=255)
back_im = background.copy()
# back_im.paste(icon, iconSize, mask=mask)
back_im.paste(icon, icon.size, mask=mask)
back_im.save("./back_im.png")
Default Image(befor cropping)
***How to convert this image to the cropped image ***
Image after cropping
You can use pillow module in python to create transparent background.
from PIL import Image
def convertImage():
img = Image.open("7OUBR.png")
img = img.convert("RGBA")
datas = img.getdata()
newData = []
for item in datas:
if item[0] == 255 and item[1] == 255 and item[2] == 255:
newData.append((255, 255, 255, 0))
else:
newData.append(item)
img.putdata(newData)
img.save("glass.png", format="png")
print("Successful")
convertImage()
I have been trying to remove background from a logo and use it as a watermark on product images.I tried to remove background from logo with masking but it also removing black fonts from logo. i need help in removing background without changing the logo. images are attached below. output image is the image i get as output with this code.
logo image: https://drive.google.com/file/d/1yMG6cDuPt8q3EqOJ4Amzp_czWq5hrGS5/view?usp=sharing
product image: https://drive.google.com/file/d/13SmkTgBtWD3yIJq-qGI0aZJ-hjaLbyuB/view?usp=sharing
output image: https://drive.google.com/file/d/1k-fQ9tPUEJKQXPdmdB2ajtliAAG4irNs/view?usp=sharing
this is my code
import cv2
import numpy as np
img = cv2.imread('images/1L2Z3A443AAMC.jpg')
logo = cv2.imread('water2.png')
logo = cv2.resize(logo,(int(img.shape[0]/1.2),int(img.shape[1]/2)))
logo_gray = cv2.cvtColor(logo,cv2.COLOR_BGR2GRAY)
ret,mask= cv2.threshold(logo_gray,245,255,cv2.THRESH_BINARY)
mask_inv = cv2.bitwise_not(mask)
logo_final = cv2.bitwise_and(logo,logo, mask=mask_inv)
h_img,w_img,_ = img.shape
h_logo,w_logo,_ = logo.shape
center_y = int(h_img/2)
center_x = int(w_img/2)
top_y = center_y - (int(h_logo/2))
left_x = center_x - (int(w_logo/2))
bottom_y = top_y + h_logo
right_x = left_x + w_logo
roi = img[top_y:bottom_y,left_x:right_x]
result = cv2.addWeighted(roi,1,logo_final,1,0)
img[top_y:bottom_y,left_x:right_x] = result
cv2.imwrite('sample.jpg',img)
In order to remove the background from a logo. We need to used masking. A masking for the foreground and another masking for the background.
logo = cv2.imread("water2.png", -1)
logo_mask = logo[:,:,3]
logo_mask_inv = cv2.bitwise_not(logo_mask)
logo = logo[:,:,0:3]
logo_background = cv2.bitwise_and(roi, roi, mask=logo_mask_inv)
logo_foreground = cv2.bitwise_and(logo, logo, mask=logo_mask)
final_logo = cv2.add(logo_background, logo_foreground)
I have an image that converted from PDF to PNG. The converted image contains several keywords that I wanted to extracted using OCR Tesseract.
Right now, I need to determine the ROI manually to crop the selected ROI. Since I have more than 5 ROI's to be applied, what would be the most efficient way to apply the ROI instead of doing it by try and error to find the exact location?
Below is the code:
def cropped(self, event):
#1st ROI
y = 20
x = 405
h = 230
w = 425
#2nd ROI
y1 = 30
x1 = 305
h1 = 330
w1 = 525
#open the converted image
image = cv2.imread("Output.png")
#perform image cropping
crop_image = image[x:w, y:h]
crop_image1 = image[x1:w1, y1:h1]
#save the cropped image
cv2.imwrite("Cropped.png", crop_image)
cv2.imwrite("Cropped1.png", crop_image1)
#open the cropped image and pass to the OCR engine
im = cv2.imread("Cropped.png")
im1 = cv2.imread("Cropped1.png")
## Do the text extraction here
you can use mouse event to select multiple ROI and crop based on the location
#!/usr/bin/env python3
import argparse
import cv2
import numpy as np
from PIL import Image
import os
drawing = False # true if mouse is pressed
ix,iy = -1,-1
refPt = []
img = ""
clone = ""
ROIRegion = []
# mouse callback function
def draw_rectangle(event,x,y,flags,param):
global ix,iy,drawing,img,clone,refPt, ROIRegion
if event == cv2.EVENT_LBUTTONDOWN:
drawing = True
ix,iy = x,y
refPt = [(x, y)]
ROIRegion.append(refPt)
#clone = img.copy()
elif event == cv2.EVENT_MOUSEMOVE:
if drawing == True:
img = clone.copy()
cv2.rectangle(img,(ix,iy),(x,y),(0,255,0),3)
a=x
b=y
if a != x | b != y:
cv2.rectangle(img,(ix,iy),(x,y),(0,0,0),-1)
elif event == cv2.EVENT_LBUTTONUP:
drawing = False
refPt.append((x,y))
img = clone.copy()
cv2.rectangle(img, (ix,iy),(x,y), (0, 255, 0), 2)
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True, help="Path to the image")
args = vars(ap.parse_args())
# load the image, clone it, and setup the mouse callback function
img = cv2.imread(args["image"])
img = np.array(img)
clone = img.copy()
cv2.namedWindow('image')
cv2.setMouseCallback('image',draw_rectangle)
while(1):
cv2.imshow('image',img)
k = cv2.waitKey(1) & 0xFF
if k == ord("r"):
del ROIRegion[-1]
del refPt[-1]
img = clone.copy()
elif k == 27:
break
#Do your cropping here
for region in range(len(ROIRegion)):
cv2.rectangle(img, ROIRegion[region][0],ROIRegion[region][1], (0, 255, 0), 2)
roi = clone[ROIRegion[region][0][1]:ROIRegion[region][1][1], ROIRegion[region][0][0]:ROIRegion[region][1][0]]
roi = cv2.cvtColor(roi, cv2.COLOR_BGR2RGB)
Here is one way in Python/OpenCV.
Read the input
Threshold on box outline color
Apply morphology to ensure closed
Get the external contours
Loop over each contour, get its bounding box, crop the region in the input and write the output
Input:
import cv2
import numpy as np
# read image
img = cv2.imread('text_boxes.jpg')
# threshold on box outline color
lowerBound = (80,120,100)
upperBound = (160,200,180)
thresh = cv2.inRange(img, lowerBound, upperBound)
# apply morphology to ensure regions are filled and remove extraneous noise
kernel = np.ones((3,3), np.uint8)
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
# get contours
contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
# get bounding boxes
i = 1
for cntr in contours:
# get bounding boxes
x,y,w,h = cv2.boundingRect(cntr)
crop = img[y:y+h, x:x+w]
cv2.imwrite("text_boxes_crop_{0}.png".format(i), crop)
i = i + 1
# save threshold
cv2.imwrite("text_boxes_thresh.png",thresh)
# show thresh and result
cv2.imshow("thresh", thresh)
cv2.waitKey(0)
cv2.destroyAllWindows()
Threshold image:
Cropped Images:
I am trying to make an image transparent.
This is my image
from PIL import Image
img = Image.open('Frame 0001.png')
img = img.convert("RGBA")
datas = img.getdata()
newData = []
for item in datas:
if item[0] == 255 and item[1] == 255 and item[2] == 255:
newData.append((255, 255, 255, 0))
else:
newData.append(item)
img.putdata(newData)
img.save("img2.png", "PNG")
My output image is this:
Well Googled a bit and found a package called cv2. Had a tough time installing that package, but the thing I was trying to do was possible
import cv2
img1=cv2.imread('m1.jpg')
img2=cv2.imread('logo.jpg')
dst=cv2.addWeighted(img1,0.7,img2,0.3,0)
cv2.imshow('dst',dst)
cv2.waitKey(0)
cv2.destroyAllWindows()