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import streamlit as st
import cv2
import io
import numpy as np
from PIL import Image
from pipeline import pipeline
import pandas as pd
import logging
# Configure the logger
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s",
filename="logs.log",
)
# Create a logger
logger = logging.getLogger("streamlit_app")
code = """
<style>
.block-container{
max-width: 100%;
padding: 50px;
}
# [data-testid="stImage"], .e115fcil2, [data-testid="StyledFullScreenButton"], [data-testid="stFullScreenFrame"].e1vs0wn30, [data-testid="element-container"].e1f1d6gn4.element-container{
# width: fit-content !important;
# }
# [data-testid="stVerticalBlock"].e1f1d6gn2{
# flex-direction: row;
# flex-wrap: wrap;
# }
[data-testid="StyledFullScreenButton"]{
display: none;
}
[data-testid="stVerticalBlockBorderWrapper"], [data-testid="stVerticalBlock"]{
width: 100%;
}
.e115fcil2{
justify-content: center;
margin-top: 20px;
}
</style>
"""
st.html(code)
st.title("Automated Surveillance System")
# Main two columns
col1, col2 = st.columns([5, 5])
container = col2.container(height=800)
# columns to show zoomed images
col3, col4= container.columns([1,1])
# column to take file input and show detected objects.
with col1:
image = st.file_uploader("File upload", label_visibility="hidden")
logger.info("Image is uploaded successfully...")
if image is not None:
image = Image.open(io.BytesIO(image.getvalue()))
image = np.asarray(image)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imwrite("images/image.jpg", image)
image = cv2.imread("images/image.jpg")
logger.info("Image is store in image.jpg file...")
results = pipeline(image)
logger.info("getting results of image...")
for result in results:
image = cv2.rectangle(image, result['merged_boundries']['top_left'], result['merged_boundries']['bottom_right'], (255, 0, 0), 1)
st.image(image)
logger.info("image is loaded in app successfully...")
else:
try:
logger.info("default image is used...")
image = cv2.imread("images/default_img.jpg")
results = pipeline(image)
logger.info("getting results of image...")
for result in results:
image = cv2.rectangle(image, result['merged_boundries']['top_left'], result['merged_boundries']['bottom_right'], (255, 0, 0), 1)
st.image(image)
except Exception as e:
logger.error("Something went wrong in Straemlit application inference")
logger.error(e)
# Column to show zoomed images
if results is not None:
with col2:
results_1 = results[:len(results)//2]
results_2 = results[len(results)//2:]
with col4:
for result in results_1:
img = result['zoomed_img']
df = pd.DataFrame(columns=['Object Type', 'Distance', 'Activity'])
actual_width, actual_height = result['merged_boundries']['bottom_right'][0] - result['merged_boundries']['top_left'][0], result['merged_boundries']['bottom_right'][1] - result['merged_boundries']['top_left'][1]
for box in result['actual_boxes']:
top_left = (box['top_left'][0] - result['merged_boundries']['top_left'][0], (box['top_left'][1] - result['merged_boundries']['top_left'][1]))
bottom_right = (box['bottom_right'][0] - result['merged_boundries']['top_left'][0], (box['bottom_right'][1] - result['merged_boundries']['top_left'][1]))
bottom_right = (bottom_right[0]*img.shape[0]//(actual_height), bottom_right[1]*img.shape[1]//(actual_width))
top_left = (top_left[0]*img.shape[0]//(actual_height), top_left[1]*img.shape[1]//(actual_width))
img = cv2.rectangle(img, top_left, bottom_right, (255, 0, 0), 1)
img = cv2.putText(img, "ID: "+str(len(df)), top_left, 1, 1, (255, 255, 255))
df.loc[len(df)] = [box['class'], box['distance'], box['activity']]
st.image(img)
st.table(df)
with col3:
for result in results_2:
img = result['zoomed_img']
df = pd.DataFrame(columns=['Object Type', 'Distance', 'Activity'])
actual_width, actual_height = result['merged_boundries']['bottom_right'][0] - result['merged_boundries']['top_left'][0], result['merged_boundries']['bottom_right'][1] - result['merged_boundries']['top_left'][1]
for box in result['actual_boxes']:
top_left = (box['top_left'][0] - result['merged_boundries']['top_left'][0], (box['top_left'][1] - result['merged_boundries']['top_left'][1]))
bottom_right = (box['bottom_right'][0] - result['merged_boundries']['top_left'][0], (box['bottom_right'][1] - result['merged_boundries']['top_left'][1]))
bottom_right = (bottom_right[0]*img.shape[0]//(actual_height), bottom_right[1]*img.shape[1]//(actual_width))
top_left = (top_left[0]*img.shape[0]//(actual_height), top_left[1]*img.shape[1]//(actual_width))
img = cv2.rectangle(img, top_left, bottom_right, (255, 0, 0), 1)
img = cv2.putText(img, "ID: "+str(len(df)), top_left, 1, 1, (255, 255, 255))
df.loc[len(df)] = [box['class'], box['distance'], box['activity']]
st.image(img)
st.table(df)
logger.info("Results are load successfully...")
logging.info('\n') # Add a blank line
logging.info('\n') # Add a blank line
else:
logger.error("results are not found...")
logging.info('\n') # Add a blank line
logging.info('\n') # Add a blank line
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