Mini Photoshop Tool

Introduction

Major Features

• Free and open source
• Using SOTA models: U2Net and LaMa
• Support 2 running option: library or web app
• Support CPU and GPU (default: CPU)
• Support: Background Removal and Object Removal (web app usage)

TODO

• [] Support batch processing (for library usage)
• [] Post processing for image smoothing
• [] Optimize processing time with large image

Installation

pip install mini-photoshop

Library Usage

import cv2
import warnings
warnings.simplefilter('ignore')
from matplotlib import pyplot as plt

from mini_photoshop.model import InpaintModel, SalientModel


device = "cpu"
img_path = "PASTE IMAGE PATH HERE"
# model_name can be "u2net_lite", "u2net", "u2net_human_seg"
salient_model = SalientModel(model_name='u2net_lite', device=device)
inpaint_model = InpaintModel(device=device)

original_image = cv2.imread(img_path)
original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
original_mask = salient_model(original_image)

# Create a copy of the image and set all background values to 255 (white)
bg_removed_result = original_image.copy()
bg_removed_result[original_mask == 0] = 255

inpaint_result = inpaint_model(original_image, original_mask*255)
inpaint_result = cv2.cvtColor(inpaint_result, cv2.COLOR_BGR2RGB)

fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(20, 7))
ax[0].imshow(original_image)
ax[1].imshow(bg_removed_result)
ax[2].imshow(inpaint_result)
for a in ax:
    a.axis("off")
plt.show()

Web App Usage

Run command

mini-photoshop

and then go to: http://localhost:8501/