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import os
import pickle
from random import random
import streamlit as st
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import numpy as np
import pandas as pd
import torch
from utils.mp4Io import mp4Io_reader
from utils.seqIo import seqIo_reader
import pandas as pd
from PIL import Image
from pathlib import Path
from transformers import AutoProcessor, AutoModel
from tempfile import NamedTemporaryFile
from tqdm import tqdm
from sklearn.metrics import accuracy_score, classification_report
from utils.utils import create_embeddings_csv_io, process_dataset_in_mem, multiclass_merge_and_filter_bouts, generate_embeddings_stream_io
# --server.maxUploadSize 3000
def get_io_reader(uploaded_file):
if uploaded_file.name[-3:]=='seq':
with NamedTemporaryFile(suffix="seq", delete=False) as temp:
temp.write(uploaded_file.getvalue())
sr = seqIo_reader(temp.name)
else:
with NamedTemporaryFile(suffix="mp4", delete=False) as temp:
temp.write(uploaded_file.getvalue())
sr = mp4Io_reader(temp.name)
return sr
def get_unique_labels(label_list: list[str]):
label_set = set()
for label in label_list:
individual_labels = label.split('||')
for individual_label in individual_labels:
label_set.add(individual_label)
return list(label_set)
def get_smoothed_predictions(svm_model, test_embeds):
test_pred = svm_model.predict(test_embeds)
test_prob = svm_model.predict_proba(test_embeds)
bout_threshold = 30
proximity_threshold = 2
predictions = multiclass_merge_and_filter_bouts(test_pred, bout_threshold, proximity_threshold)
return predictions
if "embeddings_df" not in st.session_state:
st.session_state.embeddings_df = None
if "smoothed_predictions" not in st.session_state:
st.session_state.smoothed_predictions = None
st.session_state.test_labels = []
st.title('batik: frame classifier')
st.text("Upload files to apply trained classifier on.")
with st.form('embedding_generation_settings'):
seq_file = st.file_uploader("Choose a video file", type=['seq', 'mp4'])
annot_files = st.file_uploader("Choose an annotation File", type=['annot','csv'], accept_multiple_files=True)
downsample_rate = st.number_input('Downsample Rate',value=4)
submit_embed_settings = st.form_submit_button('Create Embeddings', type='secondary')
st.markdown("**(Optional)** Upload embeddings if not generating above.")
embeddings_csv = st.file_uploader("Choose a .csv File", type=['csv'])
if submit_embed_settings and seq_file is not None and annot_files is not None:
video_embeddings, video_frames = generate_embeddings_stream_io([seq_file],
"SLIP",
downsample_rate,
False)
fnames = [seq_file.name]
embeddings_df = create_embeddings_csv_io(out="file",
fnames=fnames,
embeddings=video_embeddings,
frames=video_frames,
annotations=[annot_files],
test_fnames=None,
views=None,
conditions=None,
downsample_rate=downsample_rate)
st.session_state.embeddings_df = embeddings_df
elif embeddings_csv is not None:
embeddings_df = pd.read_csv(embeddings_csv)
st.session_state.embeddings_df = embeddings_df
else:
st.text('Please upload file(s).')
st.divider()
st.markdown("Upload classifier model.")
pickled_file = st.file_uploader("Choose a .pkl File", type=['pkl'])
if pickled_file is not None:
with NamedTemporaryFile(suffix='pkl', delete=False) as temp:
temp.write(pickled_file.getvalue())
with open(temp.name, 'rb') as pickled_model:
svm_clf = pickle.load(pickled_model)
else:
svm_clf = None
st.divider()
if st.session_state.embeddings_df is not None and svm_clf is not None:
st.subheader("specify dataset labels")
label_list = st.session_state.embeddings_df['Label'].to_list()
unique_label_list = get_unique_labels(label_list)
with st.form('apply_model_settings'):
st.text("Select label(s):")
specified_classes = st.multiselect("Label(s) included:", options=unique_label_list)
apply_model = st.form_submit_button("Apply Model")
if apply_model:
if 'Test' in st.session_state.embeddings_df:
test_videos = True
else:
print(f'shape of df: {st.session_state.embeddings_df.shape[0]}')
test_videos_array = [True for i in range(st.session_state.embeddings_df.shape[0])]
st.session_state.embeddings_df['Test'] = test_videos_array
test_videos = True
kwargs = {'embeddings_df' : st.session_state.embeddings_df,
'specified_classes' : specified_classes,
'classes_to_remove' : None,
'max_class_size' : None,
'animal_state' : None,
'view' : None,
'shuffle_data' : False,
'test_videos' : test_videos}
train_embeds, train_labels, train_images, test_embeds, test_labels, test_images =\
process_dataset_in_mem(**kwargs)
# get predictions from embeddings
with st.spinner("Model application in progress..."):
smoothed_predictions = get_smoothed_predictions(svm_clf, test_embeds)
# save variables to state
st.session_state.smoothed_predictions = smoothed_predictions
st.session_state.test_labels = test_labels
if st.session_state.smoothed_predictions is not None:
# Convert labels to numerical values
label_to_appear_first = 'other'
unique_labels = set(st.session_state.test_labels)
unique_labels.discard(label_to_appear_first)
label_to_index = {label_to_appear_first: 0}
label_to_index.update({label: idx + 1 for idx, label in enumerate(unique_labels)})
index_to_label = {idx: label for label, idx in label_to_index.items()}
numerical_labels_test = np.array([label_to_index[label] for label in st.session_state.test_labels])
print("Label Valence: ", label_to_index)
#smoothed_predictions test labels
if len(st.session_state.smoothed_predictions) > 0:
test_accuracy = accuracy_score(numerical_labels_test, st.session_state.smoothed_predictions)
else:
test_accuracy = 0 # If no predictions meet the threshold, set accuracy to 0
# test_accuracy = accuracy_score(numerical_labels_test, test_pred)
report = classification_report(numerical_labels_test,
st.session_state.smoothed_predictions,
target_names=[index_to_label[idx] for idx in range(len(index_to_label))],
output_dict=True)
report_df = pd.DataFrame(report).transpose()
st.text(f"Eval Accuracy: {test_accuracy}")
st.subheader("Classification Report:")
st.dataframe(report_df)
# create figure (behavior raster)
fig, ax = plt.subplots()
raster = ax.imshow(st.session_state.smoothed_predictions.reshape((1,st.session_state.smoothed_predictions.size)),
aspect='auto',
interpolation='nearest',
cmap=ListedColormap(['white'] + [(random(),random(),random()) for i in range(len(index_to_label) - 1)]))
ax.set_yticklabels([])
ax.set_xlabel('frames')
cbar = fig.colorbar(raster)
labels = [label_to_appear_first] + list(unique_labels)
spacing = (len(labels) - 1)/len(labels)
start = spacing/2
ticks = [start] + [start + spacing*i for i in range(1,len(labels))]
cbar.set_ticks(ticks=ticks, labels = labels)
st.pyplot(fig)
# save generated annotations
annotations = [labels[x] for x in st.session_state.smoothed_predictions]
annotations_df = pd.DataFrame(annotations, columns=['label'])
csv = annotations_df.to_csv(header=False).encode("utf-8")
output_file_name = st.text_input("Output File Name:","output")
st.download_button("Download annotations as .csv",
data=csv,
file_name=f"{output_file_name}.csv")
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