updates

VoucherVision_Reference.yaml

@@ -1,103 +0,0 @@
-# To use default value, set to null
-leafmachine:
-
-    use_RGB_label_images: True
-
-    do:
-        check_for_illegal_filenames: False 
-        check_for_corrupt_images_make_vertical: False
-    print:
-        verbose: True
-        optional_warnings: True
-
-    logging:
-        log_level: null
-
-
-    # Overall Project Input Settings
-    project:
-        # Image to Process
-        dir_images_local: 'D:\Dropbox\LM2_Env\VoucherVision_Datasets\2022_09_07_thru12_S3_jacortez_AllAsia' # 'D:/Dropbox/LM2_Env/VoucherVision_Datasets/Compare_Set_Easy_10imgs/imgs' #'D:\D_Desktop\Richie\Imgs' #'D:/Dropbox/LM2_Env/Image_Datasets/Acacia/Acacia_prickles_4-26-23_LANCZOS/images/short' #'D:\D_Desktop\Richie\Imgs' #'home/brlab/Dropbox/LM2_Env/Image_Datasets/Manuscript_Images' # 'D:\Dropbox\LM2_Env\Image_Datasets\SET_FieldPrism_Test\TESTING_OUTPUT\Images_Processed\REU_Field_QR-Code-Images\Cannon_Corrected\Images_Corrected' # 'F:\temp_3sppFamily' # 'D:/Dropbox/LM2_Env/Image_Datasets/GBIF_BroadSample_3SppPerFamily' # SET_Diospyros/images_short' # 'D:/Dropbox/LM2_Env/Image_Datasets/SET_Diospyros/images_short' #'D:\Dropbox\LM2_Env\Image_Datasets\GBIF_BroadSample_Herbarium'  #'D:/Dropbox/LM2_Env/Image_Datasets/SET_Diospyros/images_short'  # str | only for image_location:local | full path for directory containing images
-        # dir_images_local: 'D:/Dropbox/LM2_Env/VoucherVision_Datasets/Compare_Set_Easy_10imgs/imgs' #'D:\D_Desktop\Richie\Imgs' #'D:/Dropbox/LM2_Env/Image_Datasets/Acacia/Acacia_prickles_4-26-23_LANCZOS/images/short' #'D:\D_Desktop\Richie\Imgs' #'home/brlab/Dropbox/LM2_Env/Image_Datasets/Manuscript_Images' # 'D:\Dropbox\LM2_Env\Image_Datasets\SET_FieldPrism_Test\TESTING_OUTPUT\Images_Processed\REU_Field_QR-Code-Images\Cannon_Corrected\Images_Corrected' # 'F:\temp_3sppFamily' # 'D:/Dropbox/LM2_Env/Image_Datasets/GBIF_BroadSample_3SppPerFamily' # SET_Diospyros/images_short' # 'D:/Dropbox/LM2_Env/Image_Datasets/SET_Diospyros/images_short' #'D:\Dropbox\LM2_Env\Image_Datasets\GBIF_BroadSample_Herbarium'  #'D:/Dropbox/LM2_Env/Image_Datasets/SET_Diospyros/images_short'  # str | only for image_location:local | full path for directory containing images
-        image_location: 'local'
-
-        continue_run_from_partial_xlsx: 'D:\Dropbox\LM2_Env\VoucherVision_Datasets\POC_chatGPT__2022_09_07_thru12_S3_jacortez_AllAsia\2022_09_07_thru12_S3_jacortez_AllAsia\Transcription\transcribed.xlsx'
-        # continue_run_from_partial_xlsx: null
-
-        # Project Output Dir
-        dir_output: 'D:/Dropbox/LM2_Env/VoucherVision_Datasets/POC_chatGPT__2022_09_07_thru12_S3_jacortez_AllAsia' # 'D:/Dropbox/LM2_Env/Image_Datasets/TEST_LM2' # 'D:\D_Desktop\Richie\Richie_Out'
-        run_name: 'POC_chatGPT' #'images_short_TEST' #'images_short_landmark'
-
-        prefix_removal: 'MICH-V-'
-        suffix_removal: ''
-        catalog_numerical_only: True
-
-        # Embeddings and LLM
-        use_domain_knowledge: True
-        embeddings_database_name: 'EmbeddingsDB_all_asia_minimal_InRegion'
-        build_new_embeddings_database: False
-        path_to_domain_knowledge_xlsx: 'D:\Dropbox\LeafMachine2\leafmachine2\transcription\domain_knowledge/AllAsiaMinimalasof25May2023_2__InRegion.xlsx' #'D:/Dropbox/LeafMachine2/leafmachine2/transcription/domain_knowledge/AllAsiaMinimalasof25May2023_2__TRIMMEDtiny.xlsx' 
-        
-        batch_size: 500 #null # null = all
-        num_workers: 1 # int |DEFAULT| 4 # More is not always better. Most hardware loses performance after 4
-        
-    modules:
-        specimen_crop: True
-
-    LLM_version: 'chatGPT' # from 'chatGPT' OR 'PaLM'
-        
-    cropped_components:
-        # empty list for all, add to list to IGNORE, lowercase, comma seperated
-        # archival |FROM| 
-        #           ruler, barcode, colorcard, label, map, envelope, photo, attached_item, weights
-        # plant |FROM| 
-        #           leaf_whole, leaf_partial, leaflet, seed_fruit_one, seed_fruit_many, flower_one, flower_many, bud, specimen, roots, wood
-        do_save_cropped_annotations: True
-        save_cropped_annotations: ['label','barcode'] # 'save_all' to save all classes
-        save_per_image: False # creates a folder for each image, saves crops into class-names folders # TODO
-        save_per_annotation_class: True # saves crops into class-names folders
-        binarize_labels: False
-        binarize_labels_skeletonize: False
-
-    data:
-        save_json_rulers: False
-        save_json_measurements: False
-        save_individual_csv_files_rulers: False
-        save_individual_csv_files_measurements: False
-        include_darwin_core_data_from_combined_file: False
-        do_apply_conversion_factor: False ###########################
-
-    overlay:
-        save_overlay_to_pdf: True
-        save_overlay_to_jpgs: True
-        overlay_dpi: 300 # int |FROM| 100 to 300
-        overlay_background_color: 'black' # str |FROM| 'white' or 'black'
-
-        show_archival_detections: True
-        ignore_archival_detections_classes: []
-        show_plant_detections: True
-        ignore_plant_detections_classes: ['leaf_whole', 'specimen'] #['leaf_whole', 'leaf_partial', 'specimen']
-        show_segmentations: True
-        show_landmarks: True
-        ignore_landmark_classes: []
-
-        line_width_archival: 2 # int
-        line_width_plant: 6 # int
-        line_width_seg: 12 # int # thick = 12
-        line_width_efd: 6 # int # thick = 3
-        alpha_transparency_archival: 0.3  # float between 0 and 1
-        alpha_transparency_plant: 0
-        alpha_transparency_seg_whole_leaf: 0.4
-        alpha_transparency_seg_partial_leaf: 0.3
-
-    # Configure Archival Component Detector
-    archival_component_detector:
-        # ./leafmachine2/component_detector/runs/train/detector_type/detector_version/detector_iteration/weights/detector_weights
-        detector_type: 'Archival_Detector' 
-        detector_version: 'PREP_final'
-        detector_iteration: 'PREP_final'
-        detector_weights: 'best.pt'
-        minimum_confidence_threshold: 0.5
-        do_save_prediction_overlay_images: True
-        ignore_objects_for_overlay: [] # list[str] # list of objects that can be excluded from the overlay # all = null
-    

create_desktop_shortcut.py

@@ -1,69 +0,0 @@
-import os, sys
-import win32com.client
-import tkinter as tk
-from tkinter import filedialog
-from PIL import Image, ImageEnhance
-
-def create_shortcut():
-    # Request user's confirmation
-    confirmation = input("Do you want to create a shortcut for the VoucherVision? (y/n): ")
-
-    if confirmation.lower() != "y":
-        print("Okay, no shortcut will be created.")
-        return
-
-    # Get the script path
-    script_path = os.path.abspath(__file__)
-    #  Get the directory of the script
-    script_dir = os.path.dirname(script_path)
-
-    # Path to the icon file
-    icon_path = os.path.join(script_dir, 'img', 'icon.jpg')
-    img = Image.open(icon_path)  
-    enhancer = ImageEnhance.Color(img)
-    img_enhanced = enhancer.enhance(1.5) 
-    img_enhanced.save(os.path.join(script_dir, 'img', 'icon.ico'), format='ICO', sizes=[(256,256)])
-    icon_path_ico = os.path.join(script_dir, 'img', 'icon.ico')
-
-    # Construct the path to the static folder
-    static_dir = os.path.join(script_dir, "static")
-
-    # Ask for the name of the shortcut
-    shortcut_name = "Voucher Vision"
-
-    root = tk.Tk()
-    root.withdraw()  # Hide the main window
-
-    root.update()  # Ensures that the dialog appears on top
-    folder_path = filedialog.askdirectory(title="Choose location to save the shortcut")
-    print(f"Shortcut will be saved to {folder_path}")
-
-    venv_path = filedialog.askdirectory(title="Choose the location of your Python virtual environment")
-    print(f"Using virtual environment located at {venv_path}")
-
-    # Path to the activate script in the venv
-    activate_path = os.path.join(venv_path, "Scripts")
-
-    shortcut_path = os.path.join(folder_path, f'{shortcut_name}.lnk')
-
-    shell = win32com.client.Dispatch("WScript.Shell")
-    shortcut = shell.CreateShortCut(shortcut_path)
-    shortcut.Targetpath = "%windir%\System32\cmd.exe"
-    # The command activates the venv, navigates to the script's directory, then runs the script
-    # shortcut.Arguments = f'/K "{activate_path} & cd /D {os.path.dirname(script_path)} & streamlit run VoucherVisionEditor.py"'
-    # shortcut.Arguments = f'/K "{activate_path} & cd /D {static_dir} & start cmd /c python -m http.server & cd /D {script_dir} & streamlit run VoucherVisionEditor.py"'
-    streamlit_exe = os.path.join(venv_path, "Scripts","streamlit")
-    print(script_dir)
-    print(streamlit_exe)
-    activate_path = os.path.join(script_dir,"venv_VV","Scripts")
-    print(activate_path)
-    shortcut.Arguments = f'/K cd /D ""{activate_path}"" && activate && cd /D ""{script_dir}"" && python run_VoucherVision.py'
-    # Set the icon of the shortcut
-    shortcut.IconLocation = icon_path_ico
-
-    shortcut.save()
-
-    print(f"Shortcut created with the name '{shortcut_name}' in the chosen directory.")
-
-if __name__ == "__main__":
-    create_shortcut()

install_dependencies.sh

@@ -1,85 +0,0 @@
-#!/bin/bash
-
-# List of packages to be installed
-packages=(
-    wheel
-    gputil
-    streamlit
-    streamlit-extras
-    streamlit-elements==0.1.*
-    plotly
-    google-api-python-client
-    wikipedia
-    PyMuPDF
-    craft-text-detector
-    pyyaml
-    Pillow
-    bitsandbytes
-    accelerate
-    mapboxgl
-    pandas
-    matplotlib
-    matplotlib-inline
-    tqdm
-    openai
-    langchain
-    langchain-community
-    langchain-core
-    langchain_mistralai
-    langchain_openai
-    langchain_google_genai
-    langchain_experimental
-    jsonformer
-    vertexai
-    ctransformers
-    google-cloud-aiplatform
-    tiktoken
-    llama-cpp-python
-    openpyxl
-    google-generativeai
-    google-cloud-storage
-    google-cloud-vision
-    opencv-python
-    chromadb
-    chroma-migrate
-    InstructorEmbedding
-    transformers
-    sentence-transformers
-    seaborn
-    dask
-    psutil
-    py-cpuinfo
-    Levenshtein
-    fuzzywuzzy
-    opencage
-    geocoder
-    pycountry_convert
-)
-
-# Function to install a single package
-install_package() {
-    package=$1
-    echo "Installing $package..."
-    pip3 install $package
-    if [ $? -ne 0 ]; then
-        echo "Failed to install $package"
-        exit 1
-    fi
-}
-
-# Install each package individually
-for package in "${packages[@]}"; do
-    install_package $package
-done
-
-echo "All packages installed successfully."
-echo "Cloning and installing LLaVA..."
-
-
-cd vouchervision
-git clone https://github.com/haotian-liu/LLaVA.git
-cd LLaVA # Assuming you want to run pip install in the LLaVA directory
-pip install -e .
-git pull
-pip install -e .
-echo "LLaVA ready"