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Commit
·
34ad85b
1
Parent(s):
4d7b974
add js script to adapt to mobile devices
Browse files
app.py
CHANGED
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@@ -4,7 +4,6 @@ import numpy as np
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import os,sys
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import torch
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import cv2
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-
import base64
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import io
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import multiprocessing
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@@ -12,7 +11,8 @@ import random
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import time
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from loguru import logger
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from
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from lama_cleaner.model_manager import ModelManager
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from lama_cleaner.schema import Config
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@@ -99,11 +99,13 @@ model = None
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def model_process(image, mask, img_enhancer):
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global model,image_enhancer
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if mask.shape[0] == image.shape[1] and mask.shape[1] == image.shape[0] and mask.shape[0] != mask.shape[1]:
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# rotate image
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interpolation = cv2.INTER_CUBIC
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size_limit = 1080
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@@ -167,7 +169,7 @@ def model_process(image, mask, img_enhancer):
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log_info = f"image_enhancer_: {(time.time() - start) * 1000}ms, {res_np_img.shape} "
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logger.info(log_info)
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return image
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def resize_image(pil_image, new_width=400):
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width, height = pil_image.size
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@@ -181,9 +183,9 @@ model = ModelManager(
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)
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image_type = 'pil' # filepath'
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def predict(input, img_enhancer):
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if input is None:
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return None
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if image_type == 'filepath':
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# input: {'image': '/tmp/tmp8mn9xw93.png', 'mask': '/tmp/tmpn5ars4te.png'}
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origin_image_bytes = open(input["image"], 'rb').read()
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@@ -196,165 +198,12 @@ def predict(input, img_enhancer):
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mask_pil = input['mask']
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image = np.array(image_pil)
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mask = np.array(mask_pil.convert("L"))
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output = model_process(image, mask, img_enhancer)
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return output, [resize_image(
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css = '''
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.container {max-width: 1150px; margin: auto;padding-top: 1.5rem}
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#begin-btn {color: blue; font-size:20px;}
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#work-container {min-width: min(160px, 100%) !important;flex-grow: 0 !important}
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#op-container{margin: 0 auto; text-align: center;width:fit-content;min-width: min(150px, 100%);flex-grow: 0; flex-wrap: nowrap;}
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#erase-btn-container{margin: 0 auto; text-align: center;width:150px;border-width:3px;border-color:#2c9748}
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#erase-btn {padding:0;}
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#enhancer-checkbox{width:520px}
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#enhancer-tip{width:450px}
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#enhancer-tip-div{text-align: left}
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#image_output{margin: 0 auto; text-align: center;width:640px}
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#download-container{margin: 0 auto; text-align: center;width:fit-content; min-width: min(150px, 100%);flex-grow: 0; flex-wrap: nowrap;}
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#download-btn-container{margin: 0 auto; text-align: center;width: 100px;border-width:1px;border-color:#2c9748}
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#download-btn {padding:0;}
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#share-container{margin: 0 auto; text-align: center;width:fit-content; min-width: min(150px, 100%);flex-grow: 0; flex-wrap: nowrap;}
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#image_upload .touch-none{display: flex}
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@keyframes spin {
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from {
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transform: rotate(0deg);
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}
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to {
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transform: rotate(360deg);
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}
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}
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#share-btn-container {
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display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
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}
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#share-btn {
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all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;
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}
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#share-btn * {
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all: unset;
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}
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#share-btn-container div:nth-child(-n+2){
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width: auto !important;
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min-height: 0px !important;
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}
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#share-btn-container .wrap {
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display: none !important;
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}
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'''
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start_cleaner = """async() => {
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function isMobile() {
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try {
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document.createEvent("TouchEvent"); return true;
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} catch(e) {
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return false;
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}
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}
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var gradioEl = document.querySelector('body > gradio-app').shadowRoot;
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if (!gradioEl) {
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gradioEl = document.querySelector('body > gradio-app');
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}
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const group1 = gradioEl.querySelectorAll('#group_1')[0];
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const group2 = gradioEl.querySelectorAll('#group_2')[0];
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const image_upload = gradioEl.querySelectorAll('#image_upload')[0];
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const image_output = gradioEl.querySelectorAll('#image_output')[0];
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const image_output_container = gradioEl.querySelectorAll('#image-output-container')[0];
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const data_image = gradioEl.querySelectorAll('#image_upload [data-testid="image"]')[0];
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const data_image_div = gradioEl.querySelectorAll('#image_upload [data-testid="image"] > div')[0];
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image_output_container.setAttribute('style', 'width: 0px; height:0px; display:none;');
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if (isMobile()) {
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const group1_width = group1.offsetWidth;
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image_upload.setAttribute('style', 'width:' + (group1_width - 13*2) + 'px; min-height:none;');
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data_image.setAttribute('style', 'width: ' + (group1_width - 14*2) + 'px;min-height:none;');
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data_image_div.setAttribute('style', 'width: ' + (group1_width - 14*2) + 'px;min-height:none;');
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image_output.setAttribute('style', 'width: ' + (group1_width - 13*2) + 'px;min-height:none;');
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const enhancer = gradioEl.querySelectorAll('#enhancer-checkbox')[0];
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enhancer.style.display = "none";
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} else {
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max_height = 800;
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const container = gradioEl.querySelectorAll('.container')[0];
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container.setAttribute('style', 'max-width: 100%;');
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data_image.setAttribute('style', 'height: ' + max_height + 'px');
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data_image_div.setAttribute('style', 'min-height: ' + max_height + 'px');
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}
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if (!(gradioEl.parentNode)) {
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const share_btn_container = gradioEl.querySelectorAll('#share-btn-container')[0];
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share_btn_container.setAttribute('style', 'width: 0px; height:0px;');
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const share_btn_share_icon = gradioEl.querySelectorAll('#share-btn-share-icon')[0];
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share_btn_share_icon.setAttribute('style', 'width: 0px; height:0px;');
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}
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group1.style.display = "none";
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group2.style.display = "block";
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window['gradioEl'] = gradioEl;
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window['doCheckAction'] = 0;
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window['checkAction'] = function checkAction() {
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try {
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if (window['doCheckAction'] == 0) {
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var gallery_items = window['gradioEl'].querySelectorAll('#gallery .gallery-item');
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if (gallery_items && gallery_items.length == 2) {
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window.clearInterval(window['checkAction_interval']);
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window['doCheckAction'] = 1;
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gallery_items[gallery_items.length-1].click();
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}
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}
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} catch(e) {
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}
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}
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window['checkAction_interval'] = window.setInterval("window.checkAction()", 500);
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}"""
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download_img = """async() => {
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Date.prototype.Format = function (fmt) {
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var o = {
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"M+": this.getMonth() + 1,
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"d+": this.getDate(),
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"h+": this.getHours(),
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"m+": this.getMinutes(),
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"s+": this.getSeconds(),
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"q+": Math.floor((this.getMonth() + 3) / 3),
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"S": this.getMilliseconds()
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};
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if (/(y+)/.test(fmt))
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fmt = fmt.replace(RegExp.$1, (this.getFullYear() + "").substr(4 - RegExp.$1.length));
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for (var k in o)
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if (new RegExp("(" + k + ")").test(fmt)) fmt = fmt.replace(RegExp.$1, (RegExp.$1.length == 1) ? (o[k]) : (("00" + o[k]).substr(("" + o[k]).length)));
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return fmt;
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}
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var gradioEl = document.querySelector('body > gradio-app').shadowRoot;
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if (!gradioEl) {
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gradioEl = document.querySelector('body > gradio-app');
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}
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const out_image = gradioEl.querySelectorAll('#image_output img')[0];
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if (out_image) {
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var x=new XMLHttpRequest();
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x.open("GET", out_image.src, true);
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x.responseType = 'blob';
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x.onload = function(e){
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var url = window.URL.createObjectURL(x.response)
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var a = document.createElement('a');
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a.href = url;
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a.download = (new Date()).Format("yyyyMMdd_hhmmss");
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a.click();
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}
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x.send();
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}
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}"""
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image_blocks = gr.Blocks(css=css, title='Image Cleaner')
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with image_blocks as demo:
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with gr.Group(elem_id="
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with gr.Box():
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with gr.Row(elem_id="gallery_row"):
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with gr.Column(elem_id="gallery_col"):
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@@ -378,12 +227,18 @@ with image_blocks as demo:
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"""
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)
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with gr.Group(elem_id="
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with gr.Box(elem_id="work-container"):
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with gr.Row(elem_id="input-container"):
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with gr.Column():
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image_input = gr.Image(source='upload', elem_id="image_upload",tool='sketch', type=f'{image_type}',
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label="Upload", show_label=False).style(mobile_collapse=False)
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with gr.Row(elem_id="op-container").style(mobile_collapse=False, equal_height=True):
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with gr.Column(elem_id="erase-btn-container"):
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erase_btn = gr.Button(value = "Erase(⏬)",elem_id="erase-btn").style(
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loading_icon = gr.HTML(loading_icon_html, elem_id="loading-icon", visible=True)
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share_button = gr.Button("Share to community", elem_id="share-btn", visible=True)
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erase_btn.click(fn=predict, inputs=[image_input, img_enhancer], outputs=[image_out, gallery, download_container])
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download_button.click(None, [], [], _js=download_img)
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share_button.click(None, [], [], _js=share_js)
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begin_button.click(fn=None, inputs=[], outputs=[
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image_blocks.launch(server_name='0.0.0.0')
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import os,sys
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import torch
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import cv2
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import io
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import multiprocessing
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import time
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from loguru import logger
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from utils import *
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from share_btn import *
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from lama_cleaner.model_manager import ModelManager
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from lama_cleaner.schema import Config
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def model_process(image, mask, img_enhancer):
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global model,image_enhancer
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ori_image = image
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if mask.shape[0] == image.shape[1] and mask.shape[1] == image.shape[0] and mask.shape[0] != mask.shape[1]:
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# rotate image
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ori_image = np.transpose(image[::-1, ...][:, ::-1], axes=(1, 0, 2))[::-1, ...]
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image = ori_image
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original_shape = ori_image.shape
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interpolation = cv2.INTER_CUBIC
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size_limit = 1080
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log_info = f"image_enhancer_: {(time.time() - start) * 1000}ms, {res_np_img.shape} "
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logger.info(log_info)
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return image, Image.fromarray(ori_image)
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def resize_image(pil_image, new_width=400):
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width, height = pil_image.size
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)
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image_type = 'pil' # filepath'
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def predict(input, img_enhancer):
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if input is None:
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return None, [], gr.update(visible=False)
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if image_type == 'filepath':
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# input: {'image': '/tmp/tmp8mn9xw93.png', 'mask': '/tmp/tmpn5ars4te.png'}
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origin_image_bytes = open(input["image"], 'rb').read()
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mask_pil = input['mask']
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image = np.array(image_pil)
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mask = np.array(mask_pil.convert("L"))
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output, ori_image = model_process(image, mask, img_enhancer)
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return output, [resize_image(ori_image, new_width=400), resize_image(output, new_width=400)], gr.update(visible=True)
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image_blocks = gr.Blocks(css=css, title='Image Cleaner')
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| 205 |
with image_blocks as demo:
|
| 206 |
+
with gr.Group(elem_id="page_1", visible=True) as page_1:
|
| 207 |
with gr.Box():
|
| 208 |
with gr.Row(elem_id="gallery_row"):
|
| 209 |
with gr.Column(elem_id="gallery_col"):
|
|
|
|
| 227 |
"""
|
| 228 |
)
|
| 229 |
|
| 230 |
+
with gr.Group(elem_id="page_2", visible=False) as page_2:
|
| 231 |
with gr.Box(elem_id="work-container"):
|
| 232 |
with gr.Row(elem_id="input-container"):
|
| 233 |
with gr.Column():
|
| 234 |
image_input = gr.Image(source='upload', elem_id="image_upload",tool='sketch', type=f'{image_type}',
|
| 235 |
label="Upload", show_label=False).style(mobile_collapse=False)
|
| 236 |
+
with gr.Row(elem_id="scroll_x_row"):
|
| 237 |
+
with gr.Column(id="scroll_x_col"):
|
| 238 |
+
gr.HTML("""
|
| 239 |
+
<div id="scroll_x_container" style="width:100%;height:25px;"></div>
|
| 240 |
+
"""
|
| 241 |
+
)
|
| 242 |
with gr.Row(elem_id="op-container").style(mobile_collapse=False, equal_height=True):
|
| 243 |
with gr.Column(elem_id="erase-btn-container"):
|
| 244 |
erase_btn = gr.Button(value = "Erase(⏬)",elem_id="erase-btn").style(
|
|
|
|
| 265 |
loading_icon = gr.HTML(loading_icon_html, elem_id="loading-icon", visible=True)
|
| 266 |
share_button = gr.Button("Share to community", elem_id="share-btn", visible=True)
|
| 267 |
|
| 268 |
+
with gr.Row(elem_id="log_row"):
|
| 269 |
+
with gr.Column(id="log_col"):
|
| 270 |
+
gr.HTML("""
|
| 271 |
+
<div id="log_container" style="width:100%;height:auto;">
|
| 272 |
+
</div>
|
| 273 |
+
"""
|
| 274 |
+
)
|
| 275 |
+
|
| 276 |
erase_btn.click(fn=predict, inputs=[image_input, img_enhancer], outputs=[image_out, gallery, download_container])
|
| 277 |
download_button.click(None, [], [], _js=download_img)
|
| 278 |
share_button.click(None, [], [], _js=share_js)
|
| 279 |
|
| 280 |
+
begin_button.click(fn=None, inputs=[], outputs=[page_1, page_2], _js=start_cleaner)
|
| 281 |
|
| 282 |
image_blocks.launch(server_name='0.0.0.0')
|