| import streamlit as st |
| import os |
| import pandas as pd |
| from dif import * |
| from PIL import Image |
| import shutil |
|
|
| st.set_page_config( |
| page_title="Duplicate Image Finder", |
| page_icon="🖼", |
| layout="wide", |
| initial_sidebar_state="auto", |
| ) |
|
|
| @st.cache(persist=True,allow_output_mutation=False,show_spinner=True,suppress_st_warning=True) |
| def clean_directory(dir): |
| shutil.rmtree(dir) |
| os.makedirs(dir) |
|
|
| single_folder_upload_path = "single_uploads/" |
| multi_folder1_upload_path = "multi_uploads/folder_1/" |
| multi_folder2_upload_path = "multi_uploads/folder_2/" |
|
|
| clean_directory(single_folder_upload_path) |
| clean_directory(multi_folder1_upload_path) |
| clean_directory(multi_folder2_upload_path) |
|
|
| top_image = Image.open('static/banner_top__.jpg') |
| bottom_image = Image.open('static/banner_bottom.png') |
|
|
| st.sidebar.image(top_image,use_column_width='auto') |
| selection_choice = st.sidebar.selectbox('Search for duplicate Images under? 🎯',["Two Directories","Single Directory"]) |
| st.sidebar.image(bottom_image,use_column_width='auto') |
|
|
| st.title("👨💻 Duplicate Image Finder 📷") |
| st.info('✨ Supports all popular image formats 📷 - PNG, JPG, BMP 😉') |
|
|
| if selection_choice == "Single Directory": |
| uploaded_files = st.file_uploader("Upload Images 🚀", type=["png","jpg","bmp","jpeg"], accept_multiple_files=True) |
| with st.spinner(f"Working... 💫"): |
| if uploaded_files: |
| for uploaded_file in uploaded_files: |
| with open(os.path.join(single_folder_upload_path,uploaded_file.name),"wb") as f: |
| f.write((uploaded_file).getbuffer()) |
|
|
| search = dif("single_uploads/") |
|
|
| dup_imgs = [key for key in search.result.keys()] |
| low_res_imgs = [str(img.split("/")[-1]) for img in search.lower_quality] |
| stats_metrics = [search.stats[key] for key in search.stats.keys()] |
| time_metrics = [stats_metrics[2][key] for key in stats_metrics[2].keys()] |
|
|
| similarity_grade = str(stats_metrics[3]) |
| similarity_mse = str(stats_metrics[4]) |
| total_imgs_searched = str(stats_metrics[5]) |
| total_imgs_found = str(stats_metrics[6]) |
| strt_datetime = str(time_metrics[0])+ " " + str(time_metrics[1]) |
| end_datetime = str(time_metrics[2])+ " " + str(time_metrics[3]) |
| secs_elapsed = str(time_metrics[-1]) |
|
|
| df = pd.DataFrame(columns = ['names of duplicate images']) |
| df['names of duplicate images'] = dup_imgs |
| df['names of lowest quality images'] = low_res_imgs |
|
|
| if len(total_imgs_searched) != 0: |
| col1, col2, col3 = st.columns(3) |
| col1.metric("Total Images Searched", total_imgs_searched) |
| col2.metric("Duplicate Images Found", total_imgs_found) |
| col3.metric("Lowest Quality Images Found", len(low_res_imgs)) |
|
|
| col1.metric("Similarity Grade", similarity_grade.title()) |
| col2.metric("Similarity MSE", similarity_mse) |
| col3.metric("Seconds Elapsed", secs_elapsed) |
| with col2: |
| st.markdown("<br>", unsafe_allow_html=True) |
| st.dataframe(df) |
|
|
| else: |
| st.warning('⚠ Please upload your images! 😯') |
|
|
| if selection_choice == "Two Directories": |
| main_col1, main_col2 = st.columns(2) |
| with main_col1: |
| multi_folder1_uploaded_files = st.file_uploader("Upload Images (folder 1)🖼", type=["png","jpg","bmp","jpeg"], accept_multiple_files=True) |
|
|
| with main_col2: |
| multi_folder2_uploaded_files = st.file_uploader("Upload Images (folder 2)🖼", type=["png","jpg","bmp","jpeg"], accept_multiple_files=True) |
|
|
| with st.spinner(f"Working... 💫"): |
| if multi_folder1_uploaded_files and multi_folder2_uploaded_files: |
| for uploaded_file in multi_folder1_uploaded_files: |
| with open(os.path.join(multi_folder1_upload_path,uploaded_file.name),"wb") as f: |
| f.write((uploaded_file).getbuffer()) |
|
|
| for uploaded_file in multi_folder2_uploaded_files: |
| with open(os.path.join(multi_folder2_upload_path,uploaded_file.name),"wb") as f: |
| f.write((uploaded_file).getbuffer()) |
|
|
| search = dif("multi_uploads/folder_1/", "multi_uploads/folder_2/") |
|
|
| dup_imgs = [key for key in search.result.keys()] |
| low_res_imgs = [str(img.split("/")[-1]) for img in search.lower_quality] |
| stats_metrics = [search.stats[key] for key in search.stats.keys()] |
| time_metrics = [stats_metrics[2][key] for key in stats_metrics[2].keys()] |
|
|
| similarity_grade = str(stats_metrics[3]) |
| similarity_mse = str(stats_metrics[4]) |
| total_imgs_searched = str(stats_metrics[5]) |
| total_imgs_found = str(stats_metrics[6]) |
| strt_datetime = str(time_metrics[0])+ " " + str(time_metrics[1]) |
| end_datetime = str(time_metrics[2])+ " " + str(time_metrics[3]) |
| secs_elapsed = str(time_metrics[-1]) |
|
|
| df = pd.DataFrame(columns = ['names of duplicate images']) |
| df['names of duplicate images'] = dup_imgs |
| df['names of lowest quality images'] = low_res_imgs |
|
|
| if len(total_imgs_searched) != 0: |
| col1, col2, col3 = st.columns(3) |
| col1.metric("Total Images Searched", total_imgs_searched) |
| col2.metric("Duplicate Images Found", total_imgs_found) |
| col3.metric("Lowest Quality Images Found", len(low_res_imgs)) |
|
|
| col1.metric("Similarity Grade", similarity_grade.title()) |
| col2.metric("Similarity MSE", similarity_mse) |
| col3.metric("Seconds Elapsed", secs_elapsed) |
| with col2: |
| st.markdown("<br>", unsafe_allow_html=True) |
| st.dataframe(df) |
| else: |
| st.warning('⚠ Please upload your images! 😯') |
|
|
|
|
| st.markdown("<br><hr><center>Made with ❤️ by PROXIMA.PK ✨</center><hr>", unsafe_allow_html=True) |
|
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