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| import time | |
| import sys | |
| import streamlit as st | |
| import string | |
| import os | |
| from io import StringIO | |
| import pdb | |
| import json | |
| import torch | |
| import requests | |
| import socket | |
| from streamlit_image_select import image_select | |
| use_case = {"1":"Image background removal","2":"Masking foreground for downstream inpainting task"} | |
| mask_types = {"blur - blurs background":"blur","map - makes the foreground white and rest black ":"map","rgba - makes background white":"rgba","green - makes the background green":"green"} | |
| APP_NAME = "hf/salient_object_detection" | |
| INFO_URL = "https://www.taskswithcode.com/stats/" | |
| TMP_DIR="tmp_dir" | |
| TMP_SEED = 1 | |
| def get_views(action): | |
| ret_val = 0 | |
| #return "{:,}".format(ret_val) | |
| hostname = socket.gethostname() | |
| ip_address = socket.gethostbyname(hostname) | |
| if ("view_count" not in st.session_state): | |
| try: | |
| app_info = {'name': APP_NAME,"action":action,"host":hostname,"ip":ip_address} | |
| res = requests.post(INFO_URL, json = app_info).json() | |
| print(res) | |
| data = res["count"] | |
| except: | |
| data = 0 | |
| ret_val = data | |
| st.session_state["view_count"] = data | |
| else: | |
| ret_val = st.session_state["view_count"] | |
| if (action != "init"): | |
| app_info = {'name': APP_NAME,"action":action,"host":hostname,"ip":ip_address} | |
| res = requests.post(INFO_URL, json = app_info).json() | |
| return "{:,}".format(ret_val) | |
| def construct_model_info_for_display(model_names): | |
| options_arr = [] | |
| #markdown_str = f"<div style=\"font-size:16px; color: #2f2f2f; text-align: left\"><br/><b>Models evaluated ({len(model_names)})</b><br/></div>" | |
| markdown_str = f"<div style=\"font-size:16px; color: #2f2f2f; text-align: left\"><br/><b>Model evaluated </b><br/></div>" | |
| markdown_str += f"<div style=\"font-size:2px; color: #2f2f2f; text-align: left\"><br/></div>" | |
| for node in model_names: | |
| options_arr .append(node["name"]) | |
| if (node["mark"] == "True"): | |
| markdown_str += f"<div style=\"font-size:16px; color: #5f5f5f; text-align: left\"> • Model: <a href=\'{node['paper_url']}\' target='_blank'>{node['name']}</a><br/> Code released by: <a href=\'{node['orig_author_url']}\' target='_blank'>{node['orig_author']}</a><br/> Model info: <a href=\'{node['sota_info']['sota_link']}\' target='_blank'>{node['sota_info']['task']}</a></div>" | |
| if ("Note" in node): | |
| markdown_str += f"<div style=\"font-size:16px; color: #a91212; text-align: left\"> {node['Note']}<a href=\'{node['alt_url']}\' target='_blank'>link</a></div>" | |
| markdown_str += "<div style=\"font-size:16px; color: #5f5f5f; text-align: left\"><br/></div>" | |
| markdown_str += "<div style=\"font-size:12px; color: #9f9f9f; text-align: left\"><b>Note:</b><br/>• Uploaded files are loaded into non-persistent memory for the duration of the computation. They are not cached</div>" | |
| markdown_str += "<div style=\"font-size:12px; color: #9f9f9f; text-align: left\"><br/><a href=\'https://github.com/taskswithcode/salient_object_detection_app.git\' target='_blank'>Github code</a> for this app</div>" | |
| return options_arr,markdown_str | |
| def init_page(): | |
| st.set_page_config(page_title='TWC - State-of-the-art model salient object detection (visually dominant objects in an image)', page_icon="logo.jpg", layout='centered', initial_sidebar_state='auto', | |
| menu_items={ | |
| 'About': 'This app was created by taskswithcode. http://taskswithcode.com' | |
| }) | |
| col,pad = st.columns([85,15]) | |
| with col: | |
| st.image("long_form_logo_with_icon.png") | |
| def run_test(config,input_file_name,display_area,uploaded_file,mask_type): | |
| global TMP_SEED | |
| display_area.text("Processing request...") | |
| try: | |
| if (uploaded_file is None): | |
| file_data = open(input_file_name, "rb") | |
| r = requests.post(config["SERVER_ADDRESS"], data={"mask":mask_type}, files={"test":file_data}) | |
| else: | |
| file_data = uploaded_file.read() | |
| file_name = f"{TMP_DIR}/{TMP_SEED}_{str(time.time()).replace('.','_')}_{uploaded_file.name}" | |
| TMP_SEED += 1 | |
| with open(file_name,"wb") as fp: | |
| fp.write(file_data) | |
| file_data = open(file_name, "rb") | |
| r = requests.post(config["SERVER_ADDRESS"], data={"mask":mask_type}, files={"test":file_data}) | |
| os.remove(file_name) | |
| print("Servers response:",r.status_code,len(r.content)) | |
| if (r.status_code == 200): | |
| size = "{:,}".format(len(r.content)) | |
| return {"response":r.content,"size":size} | |
| else: | |
| return {"error":f"API request failed {r.status_code}"} | |
| except Exception as e: | |
| st.error("Some error occurred during prediction" + str(e)) | |
| st.stop() | |
| return {"error":f"Exception in performing salient object detection: {str(e)}"} | |
| return {} | |
| def display_results(results,response_info,mask): | |
| main_sent = f"<div style=\"font-size:14px; color: #2f2f2f; text-align: left\">{response_info}<br/><br/></div>" | |
| body_sent = [] | |
| download_data = {} | |
| main_sent = main_sent + "\n" + '\n'.join(body_sent) | |
| st.markdown(main_sent,unsafe_allow_html=True) | |
| st.image(results["response"], caption=f'Output of salient object detection with mask: {mask}') | |
| st.session_state["download_ready"] = results["response"] | |
| get_views("submit") | |
| def init_session(): | |
| print("Init session") | |
| init_page() | |
| st.session_state["model_name"] = "insprynet" | |
| st.session_state["download_ready"] = None | |
| st.session_state["model_name"] = "ss_test" | |
| st.session_state["file_name"] = "default" | |
| st.session_state["mask_type"] = "blur" | |
| def app_main(app_mode,example_files,model_name_files,config_file): | |
| init_session() | |
| with open(example_files) as fp: | |
| example_file_names = json.load(fp) | |
| with open(model_name_files) as fp: | |
| model_names = json.load(fp) | |
| with open(config_file) as fp: | |
| config = json.load(fp) | |
| curr_use_case = use_case[app_mode].split(".")[0] | |
| curr_use_case = use_case[app_mode].split(".")[0] | |
| st.markdown("<h5 style='text-align: center;'>State-of-the-art model for salient object detection</h5>", unsafe_allow_html=True) | |
| st.markdown(f"<div style='color: #4f4f4f; text-align: left'>Use cases for salient object detection<br/> • {use_case['1']}<br/> • {use_case['2']}</div>", unsafe_allow_html=True) | |
| st.markdown(f"<div style='color: #9f9f9f; text-align: right'>views: {get_views('init')}</div>", unsafe_allow_html=True) | |
| try: | |
| with st.form('twc_form'): | |
| step1_line = "Upload an image or choose an example image below" | |
| uploaded_file = st.file_uploader(step1_line, type=["png","jpg","jpeg"]) | |
| selected_file_name = image_select("Select image", ["twc_samples/sample1.jpg", "twc_samples/sample2.jpg", "twc_samples/sample3.jpg", "twc_samples/sample4.jpg"]) | |
| st.write("") | |
| mask_type = st.selectbox(label=f'Select type of masking', | |
| options = list(dict.keys(mask_types)), index=0, key = "twc_mask_types") | |
| mask_type = mask_types[mask_type] | |
| st.write("") | |
| submit_button = st.form_submit_button('Run') | |
| options_arr,markdown_str = construct_model_info_for_display(model_names) | |
| input_status_area = st.empty() | |
| display_area = st.empty() | |
| if submit_button: | |
| start = time.time() | |
| if uploaded_file is not None: | |
| st.session_state["file_name"] = uploaded_file.name | |
| else: | |
| st.session_state["file_name"] = selected_file_name | |
| st.session_state["mask_type"] = mask_type | |
| display_area.empty() | |
| results = run_test(config,st.session_state["file_name"],display_area,uploaded_file,mask_type) | |
| with display_area.container(): | |
| if ("error" in results): | |
| st.error(results["error"]) | |
| else: | |
| device = 'GPU' if torch.cuda.is_available() else 'CPU' | |
| response_info = f"Computation time on {device}: {time.time() - start:.2f} secs for image size: {results['size']} bytes" | |
| display_results(results,response_info,mask_type) | |
| #st.json(results) | |
| st.download_button( | |
| label="Download results as png", | |
| data= st.session_state["download_ready"] if st.session_state["download_ready"] != None else "", | |
| disabled = False if st.session_state["download_ready"] != None else True, | |
| file_name= (st.session_state["model_name"] + "_" + st.session_state["mask_type"] + "_" + '_'.join(st.session_state["file_name"].split(".")[:-1]) + ".png").replace("/","_"), | |
| mime='image/png', | |
| key ="download" | |
| ) | |
| except Exception as e: | |
| st.error("Some error occurred during loading" + str(e)) | |
| st.stop() | |
| st.markdown(markdown_str, unsafe_allow_html=True) | |
| if __name__ == "__main__": | |
| app_main("1","sod_app_examples.json","sod_app_models.json","config.json") | |