Spaces:
Sleeping
Sleeping
File size: 3,887 Bytes
74cd228 f29a77e 74cd228 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 |
import matplotlib
matplotlib.use("Agg")
# Libraries
import streamlit as st
# Utility
import time
import io
import argparse
import sys
import os.path
import subprocess
import tempfile
import logging
# Viz
import altair as alt
import av
# ML
import numpy as np
import pandas as pd
import cv2 as cv
from PIL import Image, ImageOps
from tqdm import tqdm
# Custom
import inference
from app_utils import *
def run_app():
# Options
st.set_option("deprecation.showfileUploaderEncoding", False)
# Counter variable
views_counter = 0
# Increment the counter on page/view access
views_counter += 1
# Display the counter value
st.write("Number of Views:", views_counter)
# App documentation
st.title("MIT Count Fish Counter")
st.text("Upload a video file to detect and count fish")
video_url = "yolo2_out_py.mp4" # Replace with the actual video URL or file path
video_bytes = open(video_url, "rb").read()
col1, col2 = st.columns(2)
## Col1 #########################################
with col1:
## Initial visualizations
# Historical values
st.altair_chart(
plot_historical_data(pd.read_csv("herring_count_all.csv")),
use_container_width=True,
)
# Locations
st.subheader("Map of Fishery Locations")
st.map(
pd.DataFrame(
np.random.randn(5, 2) / [50, 50] + [42.41, -71.38],
columns=["lat", "lon"],
)
)
## Col2 #########################################
with col2:
# Replace the loading message with the video
st.subheader("Example of processed video")
st.video(video_bytes)
st.subheader("Upload your own video...")
# Initialize
img_types = ["jpg", "png", "jpeg"]
video_types = ["mp4", "avi"]
uploaded_file = st.file_uploader(
"Select an image or video file...", type=img_types + video_types
)
# Display uploaded file
if uploaded_file is not None:
if str(uploaded_file.type).split("/")[-1] in img_types:
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded image", use_column_width=True)
# TBD: Inference code to run and display for single image
elif str(uploaded_file.type).split("/")[-1] in video_types:
# Display uploaded file
st.video(uploaded_file)
# Convert streamlit video object to OpenCV format to run inferences
tfile = tempfile.NamedTemporaryFile(delete=False)
tfile.write(uploaded_file.read())
vf = cv.VideoCapture(tfile.name)
# Run inference
with st.spinner("Running inference..."):
frames, counts, timestamps = inference.main(vf)
logging.info("INFO: Completed running inference on frames")
st.balloons()
# Convert OpenCV Numpy frames in-memory to IO Bytes for streamlit
streamlit_video_file = frames_to_video(frames=frames, fps=11)
st.video(streamlit_video_file) # Show processed video\
st.download_button(
label="Download processed video",
data=streamlit_video_file,
mime="mp4",
file_name="processed_video.mp4",
)
df_counts_time = pd.DataFrame(
data={"fish_count": counts, "timestamps": timestamps[1:]}
)
st.altair_chart(
plot_count_date(dataframe=df_counts_time),
use_container_width=True,
)
else:
st.write("No file uploaded")
if __name__ == "__main__":
run_app()
|