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4a505a6 07a7845 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 | # Python In-built packages
from pathlib import Path
import PIL
# External packages
import streamlit as st
# Local Modules
import settings
import helper
# Setting page layout
st.set_page_config(
page_title="OIViz",
layout="wide",
initial_sidebar_state="expanded"
)
# Main page heading
st.title("OIViz 📬🤖")
st.markdown("I'm OIViz, an AI-powered video analytics tool that can help you analyze videos and images.")
# Sidebar
st.sidebar.header("Model Configuration")
# Model Options
model_type = st.sidebar.radio(
"Select Task", ['Detection', 'Segmentation', 'Pose'])
confidence = float(st.sidebar.slider(
"Select Model Confidence", 25, 100, 40)) / 100
# Selecting Detection Or Segmentation
if model_type == 'Detection':
model_path = Path(settings.DETECTION_MODEL)
elif model_type == 'Segmentation':
model_path = Path(settings.SEGMENTATION_MODEL)
elif model_type == 'Pose':
model_path = Path(settings.POSE_MODEL)
# Load Pre-trained ML Model
try:
model = helper.load_model(model_path)
except Exception as ex:
st.error(f"Unable to load model. Check the specified path: {model_path}")
st.error(ex)
st.sidebar.header("New / Existing Video")
new_existing = st.sidebar.radio("Select Video/Image Type", ['New', 'Existing'])
st.sidebar.header("Image/Video Configuration")
source_radio = st.sidebar.radio(
"Select Source", settings.SOURCES_LIST)
source_img = None
#If new video is selected
if new_existing == 'New':
# If image is selected
if source_radio == settings.IMAGE:
source_img = st.sidebar.file_uploader(
"Choose an image...", type=("jpg", "jpeg", "png", 'bmp', 'webp'))
col1, col2 = st.columns(2)
with col1:
try:
if source_img is None:
default_image_path = str(settings.DEFAULT_IMAGE)
default_image = PIL.Image.open(default_image_path)
st.image(default_image_path, caption="Default Image",
use_column_width=True)
else:
uploaded_image = PIL.Image.open(source_img)
st.image(source_img, caption="Uploaded Image",
use_column_width=True)
except Exception as ex:
st.error("Error occurred while opening the image.")
st.error(ex)
with col2:
if source_img is None:
default_detected_image_path = str(settings.DEFAULT_DETECT_IMAGE)
default_detected_image = PIL.Image.open(
default_detected_image_path)
st.image(default_detected_image_path, caption='Detected Image',
use_column_width=True)
else:
if st.sidebar.button('Detect Objects'):
res = model.predict(uploaded_image,
conf=confidence
)
boxes = res[0].boxes
res_plotted = res[0].plot()[:, :, ::-1]
st.image(res_plotted, caption='Detected Image',
use_column_width=True)
try:
with st.expander("Detection Results"):
for box in boxes:
st.write(box.data)
except Exception as ex:
# st.write(ex)
st.write("No image is uploaded yet!")
elif source_radio == settings.VIDEO:
#helper.play_stored_video(confidence, model)
helper.try_displaying_whole_movie(confidence, model)
elif source_radio == settings.WEBCAM:
helper.play_webcam(confidence, model)
elif source_radio == settings.RTSP:
helper.play_rtsp_stream(confidence, model)
elif source_radio == settings.YOUTUBE:
helper.play_youtube_video(confidence, model)
else:
st.error("Please select a valid source type!")
elif new_existing=='Existing':
# display a summary of previously stored outputs from YOLOv8
helper.display_summary(project, name)
video_path = st.sidebar.file_uploader(
"Choose a video...", type=("mp4", "avi", "mov", "wmv", "flv", "mkv"))
if video_path is not None:
helper.play_stored_video(confidence, model, video_path)
else:
st.error("Please select a valid video file!")
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