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import datetime
import logging
import streamlit as st
from streamlit.delta_generator import DeltaGenerator
import cv2
import numpy as np
from input.input_observation import InputObservation
from input.input_validator import get_image_datetime, is_valid_email, is_valid_number
m_logger = logging.getLogger(__name__)
m_logger.setLevel(logging.INFO)
'''
A module to setup the input handling for the whale observation guidance tool
both the UI elements (setup_input_UI) and the validation functions.
'''
allowed_image_types = ['jpg', 'jpeg', 'png', 'webp']
# an arbitrary set of defaults so testing is less painful...
# ideally we add in some randomization to the defaults
spoof_metadata = {
"latitude": 23.5,
"longitude": 44,
"author_email": "[email protected]",
"date": None,
"time": None,
}
def setup_input(
viewcontainer: DeltaGenerator=None,
_allowed_image_types: list=None, ) -> InputObservation:
"""
Sets up the input interface for uploading an image and entering metadata.
It provides input fields for an image upload, lat/lon, author email, and date-time.
In the ideal case, the image metadata will be used to populate location and datetime.
Parameters:
viewcontainer (DeltaGenerator, optional): The Streamlit container to use for the input interface. Defaults to st.sidebar.
_allowed_image_types (list, optional): List of allowed image file types for upload. Defaults to allowed_image_types.
Returns:
InputObservation: An object containing the uploaded image and entered metadata.
"""
if viewcontainer is None:
viewcontainer = st.sidebar
if _allowed_image_types is None:
_allowed_image_types = allowed_image_types
viewcontainer.title("Input image and data")
# 1. Input the author email
author_email = viewcontainer.text_input("Author Email", spoof_metadata.get('author_email', ""))
if author_email and not is_valid_email(author_email):
viewcontainer.error("Please enter a valid email address.")
# 2. Image Selector
uploaded_files = viewcontainer.file_uploader("Upload an image", type=allowed_image_types, accept_multiple_files=True)
observations = {}
images = {}
if uploaded_files is not None:
for file in uploaded_files:
viewcontainer.title(f"Metadata for {file.name}")
# Display the uploaded image
# load image using cv2 format, so it is compatible with the ML models
file_bytes = np.asarray(bytearray(file.read()), dtype=np.uint8)
filename = file.name
image = cv2.imdecode(file_bytes, 1)
# Extract and display image date-time
image_datetime = None # For storing date-time from image
image_datetime = get_image_datetime(file)
m_logger.debug(f"image date extracted as {image_datetime} (from {uploaded_files})")
# 3. Latitude Entry Box
latitude = viewcontainer.text_input("Latitude for "+filename, spoof_metadata.get('latitude', ""))
if latitude and not is_valid_number(latitude):
viewcontainer.error("Please enter a valid latitude (numerical only).")
m_logger.error(f"Invalid latitude entered: {latitude}.")
# 4. Longitude Entry Box
longitude = viewcontainer.text_input("Longitude for "+filename, spoof_metadata.get('longitude', ""))
if longitude and not is_valid_number(longitude):
viewcontainer.error("Please enter a valid longitude (numerical only).")
m_logger.error(f"Invalid latitude entered: {latitude}.")
# 5. Date/time
## first from image metadata
if image_datetime is not None:
time_value = datetime.datetime.strptime(image_datetime, '%Y:%m:%d %H:%M:%S').time()
date_value = datetime.datetime.strptime(image_datetime, '%Y:%m:%d %H:%M:%S').date()
else:
time_value = datetime.datetime.now().time() # Default to current time
date_value = datetime.datetime.now().date()
## if not, give user the option to enter manually
date_option = st.sidebar.date_input("Date for "+filename, value=date_value)
time_option = st.sidebar.time_input("Time for "+filename, time_value)
observation = InputObservation(image=file, latitude=latitude, longitude=longitude,
author_email=author_email, date=image_datetime, time=None,
date_option=date_option, time_option=time_option)
observations[file.name] = observation
images[file.name] = image
st.session_state.images = images
st.session_state.files = uploaded_files
return observations
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