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"""
TOC:
0) IMPORTS
1) METADATA
2) UPLOAD
3) ANNOTATIONS
-1) MAIN
"""
# gradio run.py --demo-name=my_demo
##################################################
# 0) IMPORTS
##################################################
# baselayer
import os
from io import BytesIO
import argparse
# web
import gradio as gr
# image processing
from tkinter import Tk, filedialog
from pathlib import Path
from PIL import Image, ExifTags
from PIL.ExifTags import TAGS
# data science
import numpy as np
import pandas as pd
# export
import csv
# from transformers import AutoImageProcessor, AutoModelForImageClassification
# import torch
# Load model
# processor = AutoImageProcessor.from_pretrained("victor/animals-classifier")
# model = AutoModelForImageClassification.from_pretrained("victor/animals-classifier")
# model.eval()
##################################################
# 1) METADATA
##################################################
# this one works with PIL but we don't get all the metadata
def decode_utf16_little_endian(binary_data):
try:
# Decode the binary data as UTF-16 Little Endian
# print(f"Test:{binary_data.decode('utf-16-le')}")
# print(f"Type:{type(binary_data)}")
decoded_text = binary_data.decode("utf-16-le").rstrip("\x00")
except Exception as e:
decoded_text = "Encoded"
return decoded_text
'''
def get_exif(list_file_paths):
metadata_all_file = {}
df = pd.DataFrame()
for file_path in list_file_paths:
metadata = {}
metadata["name"] = file_path.split("/")[-1]
print(file_path)
try:
image = Image.open(file_path)
exifdata = image._getexif()
if exifdata is not None:
print(len(exifdata.items()))
for tagid, value in exifdata.items():
# print(tagid, value)
# print(f"Value:{value}")
tagname = str(TAGS.get(tagid, tagid))
# value = exifdata.get(tagid)
# Handle binary data
if isinstance(value, bytes):
# print(f"Value bytes {value}")
# print(f"Value bytes {type(value)}")
# print(f"Value str {decode_utf16_little_endian(value)}")
value = decode_utf16_little_endian(value)
print(tagname)
print(type(tagname))
print(value)
if type(tagname) is not str:
print(">>>>>>>>>>>> here " + type(tagname))
try:
metadata[str(tagname)] = value
except:
try:
metadata[repr(tagname)] = value
except:
pass
else:
metadata[tagname] = value
"""
for key in metadata.keys():
if type(key) is not str:
try:
metadata[str(key)] = metadata[key]
except:
try:
metadata[repr(key)] = metadata[key]
except:
pass
del metadata[key]
"""
# print(f"\t{metadata}")
print(metadata)
print(pd.DataFrame([metadata]))
df = pd.concat([df, pd.DataFrame([metadata])], ignore_index=True)
# new_row = {"name": file_path, **metadata}
# df = pd.concat([df, pd.DataFrame([new_row])], ignore_index=True)
# metadata_all_file[file_path] = metadata
else:
return "No EXIF metadata found."
except Exception as e:
return f"Error : {e}"
print(pd.concat([df, pd.DataFrame([metadata])], ignore_index=True))
print(f"FINAL DF \n \n \n {df}")
return df
'''
import pandas as pd
from PIL import Image
from PIL.ExifTags import TAGS
def decode_utf16_little_endian(value):
try:
return value.decode("utf-16le").strip()
except:
return value # Fallback to the original value if decoding fails
def extract_particular_value_from_exif_file(metadata, tagname, value):
pass
def get_exif(list_file_paths):
df = pd.DataFrame()
for file_path in list_file_paths:
metadata = {"name": file_path.split("/")[-1]}
print(file_path)
try:
image = Image.open(file_path)
exifdata = image._getexif()
if exifdata is not None:
for tagid, value in exifdata.items():
tagname = TAGS.get(tagid, str(tagid)) # Ensure tagname is a string
print(type(tagname))
if isinstance(value, bytes):
value = decode_utf16_little_endian(value)
if isinstance(value, dict):
# for subkey, subvalue in value.items():
# metadata[f"{tagname}_{subkey}"] = subvalue
# else:
# metadata[tagname] = value
value = str(value)
print(value)
print(type(value))
metadata[tagname] = value # All keys are now strings
print(metadata)
if all(isinstance(k, str) for k in metadata.keys()):
df = pd.concat([df, pd.DataFrame([metadata])], ignore_index=True)
else:
print("Skipping metadata with non-string keys.")
else:
print(f"No EXIF metadata found for {file_path}")
except Exception as e:
print(f"Error processing {file_path}: {e}")
print(f"FINAL DF:\n{df}")
return df
##################################################
# 2) UPLOAD
##################################################
def get_file_names(files_):
"""
Get a list of the name of files splitted to get only the proper name
Input: Uploaded files
Output: ['name of file 1', 'name of file 2']"""
return [file.name for file in files_]
##################################################
# 3) ANNOTATIONS
##################################################
def get_annotation(files_):
"""
Get the label and accuracy from pretrained (or futur custom model)
Input: Uploaded files
Output: Df that contains: file_name | label | accuracy
"""
# df = pd.DataFrame(columns=["file_name", "label", "accuracy"])
df_exif = get_exif(get_file_names(files_))
return df_exif
def update_dataframe(df):
return df # Simply return the modified dataframe
def df_to_csv(df_, encodings=None):
"""
Get the df and convert it as an gradio file output ready for download
Input: DF created
Output: gr.File()
"""
if encodings is None:
encodings = ["utf-8", "utf-8-sig", "latin1", "iso-8859-1", "cp1252"]
for encoding in encodings:
try:
df_.to_csv("output.csv", encoding=encoding, index=False)
# print(f"File saved successfully with encoding: {encoding}")
return gr.File(value="output.csv", visible=True)
except Exception as e:
print(f"Failed with encoding {encoding}: {e}")
##################################################
# -1) MAIN
##################################################
def process_files(files_):
"""
Main function
- Get uploaded files
- Get annotations # TODO
- Get the corresponding df
- Get the csv output
"""
df = get_annotation(files_)
return df
with gr.Blocks() as interface:
gr.Markdown("# Wildlife.ai Annotation tools")
# Upload data
with gr.Row():
upload_btn = gr.UploadButton(
"Upload raw data",
file_types=["image", "video"],
file_count="multiple",
)
update_btn = gr.Button("Modify raw data")
download_raw_btn = gr.Button("Generate raw data as csv")
download_modified_btn = gr.Button("Generate new data as a csv")
# Get results
gr.Markdown("## Results")
df = gr.DataFrame(interactive=False)
download_raw_btn.click(
fn=df_to_csv,
inputs=[df],
outputs=gr.File(visible=False),
)
gr.Markdown("## Modified results")
df_modified = gr.DataFrame(interactive=True)
download_modified_btn.click(
fn=df_to_csv,
inputs=[df_modified],
outputs=gr.File(visible=False),
show_progress=False,
)
# gr.Markdown("## Extract as CSV")
# Buttons
upload_btn.upload(fn=process_files, inputs=upload_btn, outputs=df)
update_btn.click(fn=update_dataframe, inputs=df, outputs=df_modified)
if __name__ == "__main__":
interface.launch(debug=True)
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