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VisualCorrespondenceHumanStudy-CUB / image_utils.py
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import json
import os
import pickle
import random
from glob import glob
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import streamlit as st
from PIL import Image
@st.cache(allow_output_mutation=True, max_entries=10, ttl=3600)
def load_query(image_path):
image = Image.open(image_path)
width, height = image.size
new_width = width
new_height = height
left = (width - new_width) / 2
top = (height - new_height) / 2
right = (width + new_width) / 2
bottom = (height + new_height) / 2
# Crop the center of the image
cropped_image = image.crop(
(left + 5, top + 40, right - 2125, bottom - (2805))
).resize((300, 300))
return cropped_image
# CHM ############################################################################
@st.cache(allow_output_mutation=True, max_entries=10, ttl=3600)
def load_chm_nns(image_path):
image = Image.open(image_path)
width, height = image.size
new_width = width
new_height = height
left = (width - new_width) / 2
top = (height - new_height) / 2
right = (width + new_width) / 2
bottom = (height + new_height) / 2
# Crop the center of the image
cropped_image = image.crop((left + 525, top + 2830, right - 0, bottom - (10)))
return cropped_image
@st.cache(allow_output_mutation=True, max_entries=10, ttl=3600)
def load_chm_corrs(image_path):
image = Image.open(image_path)
width, height = image.size
new_width = width
new_height = height
left = (width - new_width) / 2
top = (height - new_height) / 2
right = (width + new_width) / 2
bottom = (height + new_height) / 2
# Crop the center of the image
cropped_image = image.crop((left + 15, top + 1835, right - 45, bottom - 445))
return cropped_image
# CHM ############################################################################
# KNN ############################################################################
@st.cache(allow_output_mutation=True, max_entries=10, ttl=3600)
def load_knn_nns(image_path):
image = Image.open(image_path)
width, height = image.size
new_width = width
new_height = height
left = (width - new_width) / 2
top = (height - new_height) / 2
right = (width + new_width) / 2
bottom = (height + new_height) / 2
# Crop the center of the image
cropped_image = image.crop((left + 525, top + 40, right - 10, bottom - (2805)))
return cropped_image
# KNN ############################################################################
# EMD ############################################################################
@st.cache(allow_output_mutation=True, max_entries=10, ttl=3600)
def load_emd_nns(image_path):
image = Image.open(image_path)
width, height = image.size
new_width = width
new_height = height
left = (width - new_width) / 2
top = (height - new_height) / 2
right = (width + new_width) / 2
bottom = (height + new_height) / 2
# Crop the center of the image
cropped_image = image.crop((left + 525, top + 480, right - 5, bottom - (2365)))
return cropped_image
@st.cache(allow_output_mutation=True, max_entries=10, ttl=3600)
def load_emd_corrs(image_path):
image = Image.open(image_path)
width, height = image.size
new_width = width
new_height = height
left = (width - new_width) / 2
top = (height - new_height) / 2
right = (width + new_width) / 2
bottom = (height + new_height) / 2
# Crop the center of the image
cropped_image = image.crop((left + 90, top + 880, right - 75, bottom - 1438))
return cropped_image
# EMD ############################################################################
@st.cache()
def convert_df(df):
return df.to_csv().encode("utf-8")