MareArts Computer Vision Study.: pillow

Showing posts with label pillow. Show all posts

9/19/2023

2/26/2023

Drawing rectangle and text on image using cv2, pillow (python code)

Draw text & rectangle on image

Ver.1 pillow

from PIL import Image, ImageDraw, ImageFont
import json
# Display the image in Jupyter Notebook
from IPython.display import display


def draw_rect_text(base_dir, json_fn):
    # Load JSON file
    with open(base_dir+json_fn, 'r') as f:
        data = json.load(f)

    # Load image
    img = Image.open(base_dir+data['image_file_name']+'.jpg')

    # Create a draw object
    draw = ImageDraw.Draw(img)

    # Loop through OCR data and draw rectangles and text
    for ocr in data['ocr']:
        left = ocr['left']
        top = ocr['top']
        right = ocr['right']
        bottom = ocr['bottom']
        text = ocr['text']

        # Draw rectangle
        draw.rectangle((left, top, right, bottom), outline=(255, 0, 0), width=2)

        # Draw text
        font = ImageFont.truetype('arial.ttf', size=14)
        text_size = draw.textsize(text, font=font)
        text_x = left
        text_y = top - text_size[1]
        draw.text((text_x, text_y), text, font=font, fill=(255, 0, 0))

    # Display image
    # img.show() #now window
    display(img) #display on notebook

Ver .2 cv2

import cv2
import json

def draw_rect_text(base_dir, json_fn):
    # Load JSON file
    with open(base_dir+json_fn, 'r') as f:
        data = json.load(f)

    print(base_dir+data['image_file_name']+'.jpg')
    # Load image
    img = cv2.imread(base_dir+data['image_file_name']+'.jpg')

    # Loop through OCR data and draw rectangles and text
    for ocr in data['ocr']:
        left = ocr['left']
        top = ocr['top']
        right = ocr['right']
        bottom = ocr['bottom']
        text = ocr['text']
        
        # Draw rectangle
        cv2.rectangle(img, (left, top), (right, bottom), (0, 0, 255), 2)
        
        # Draw text
        font = cv2.FONT_HERSHEY_SIMPLEX
        font_scale = 0.5
        thickness = 1
        text_size = cv2.getTextSize(text, font, font_scale, thickness)[0]
        text_x = left
        text_y = top - text_size[1]
        cv2.putText(img, text, (text_x, text_y), font, font_scale, (0, 0, 255), thickness)
        
    # Display image
    cv2.imshow('Image', img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

Thank you.

🙇🏻‍♂️

www.marearts.com

1/23/2023

12/08/2021

Rotation(orientation) correction when uploading photos from mobile (python, pillow)

Try to use ImageOps function.

Solution is simple refer to below code:

#pip install Pillow
from PIL import Image, ImageOps
image = Image.open('img.jpg') #open image
#### orient fix
image = ImageOps.exif_transpose(image)
#### go futher!

Thank you.

🙇🏻‍♂️

www.marearts.com

10/02/2020

No module named 'PIL'

pip install --upgrade pip

pip install pillow

8/08/2019

PIL to string, string to PIL (python)

It's simple example source code for that:

PIL to string(base64)
- PIL open image
- image to byte
- byte to string (base64)

string(base64) to PIL
- string to byte
- PIL load byte

--

import base64
import io
from PIL import Image

#open file using PIL
pil_img = Image.open('IMG_0510.jpg')
width, height = pil_img.size
print(width, height)


#get image data as byte
buffer = io.BytesIO()
pil_img.save(buffer, format=pil_img.format)
buffer_value = buffer.getvalue()

#byte to string
base64_str = base64.b64encode(buffer_value)

#read string to image buffer
buffer2 = base64.b64decode(base64_str)
pil_img2 = Image.open(io.BytesIO(buffer2))
width2, height2 = pil_img2.size
print(width2, height2)


#check first & second image
pil_img.show()
pil_img2.show()

--

here, another source code for :
OpenCV -> PIL -> resize -> OpenCV

http://study.marearts.com/2019/06/opencv-pil-resize-opencv.html

6/10/2019

OpenCV -> PIL -> resize -> OpenCV

Some simple code for this processing

1. Read image by OpenCV
2. Conver from OpenCV to PIL image
3. some processing using PIL, ex)resize
4. Conver from PIL to OpenCV

Check this code.

import cv2
from PIL import Image
import numpy

#target resize
r_x = 100
r_y = 100


#read image using opencv
cv_img_o = cv2.imread('A.png')

#conver mat to pil
cv_img = cv2.cvtColor(cv_img_o, cv2.COLOR_BGR2RGB)
im_pil = Image.fromarray(cv_img)

#resize pil
im_pil = im_pil.resize((r_x,r_y), Image.ANTIALIAS)

#convert pil to mat
cv_img_r = numpy.array(im_pil)

# Convert RGB to BGR
cv_img_r = cv2.cvtColor(cv_img_r, cv2.COLOR_RGB2BGR)
#cv_img_r = cv_img_r[:, :, ::-1].copy()

cv2.namedWindow('origin',0)
cv2.imshow('origin', cv_img_o)

cv2.namedWindow('resize',0)
cv2.imshow('resize', cv_img_r)

cv2.waitKey(0)

2/23/2019

64base image to pil image and upload s3 bucket on aws lambda

This article is about how to convert base64 string image to byte and invoke to PIL image.
Then we can handle image whatever we want to ex) image resize ..

base64 image come from img_base64 = event['base64Image']
and then convert to byte data imgdata = base64.b64decode(img_base64)

then it can save to image file and load to Image.open(filename)
or
invoke to pil image directly : img2 = Image.open(io.BytesIO(imgdata))

In the source code, there is also example how to upload image to s3 bucket.
refer to below code.
//

import sys
sys.path.append("/opt")

import json
import boto3
import os
import io
from PIL import Image
import base64



ACCESS_KEY = os.environ.get('ACCESS_KEY')
SECRET_KEY = os.environ.get('SECRET_KEY')

def uploadToS3(bucket, s3_path, local_path):
    s3_client = boto3.client(
        's3',
        aws_access_key_id=ACCESS_KEY,
        aws_secret_access_key=SECRET_KEY,
    )
    s3_client.upload_file(local_path, bucket, s3_path)
    
    
    
def lambda_handler(event, context):
    
    #base64 image string
    img_base64 = event['base64Image']
    #string to byte
    imgdata = base64.b64decode(img_base64)
    
    #save some image file
    with open("/tmp/imageToSave.png", "wb") as fh:
        fh.write(imgdata)
    
    #open("/tmp/imageToSave.png",'rb')
    uploadToS3("input-image1", "imageToSave.png", "/tmp/imageToSave.png")
    
    #load image file to pil
    img = Image.open("/tmp/imageToSave.png")
    width, height = img.size
    
    #load 
    img2 = Image.open(io.BytesIO(imgdata))
    width2, height2 = img2.size
    
    # TODO implement
    return {
        'statusCode': 200,
        'body': json.dumps('Hello leon it is from Lambda!'),
        'width file': width,
        'heihgt file' : height,
        'width pil': width2,
        'heihgt pil' : height2
    }

//

These method is referenced by:
https://stackoverflow.com/questions/16214190/how-to-convert-base64-string-to-image
https://stackoverflow.com/questions/11727598/pil-image-open-working-for-some-images-but-not-others
https://stackoverflow.com/questions/16214190/how-to-convert-base64-string-to-image
https://stackoverflow.com/questions/6444548/how-do-i-get-the-picture-size-with-pil

Thanks for effort.

MareArts Computer Vision Study.

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