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from ultralytics import YOLO
import base64
import cv2
import io
import numpy as np
from ultralytics.utils.plotting import Annotator
import streamlit as st
from streamlit_image_coordinates import streamlit_image_coordinates
import pandas as pd
import ollama
import bs4
import tempfile
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.document_loaders import CSVLoader
from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import OllamaEmbeddings
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
def set_background(image_file1,image_file2):
with open(image_file1, "rb") as f:
img_data1 = f.read()
b64_encoded1 = base64.b64encode(img_data1).decode()
with open(image_file2, "rb") as f:
img_data2 = f.read()
b64_encoded2 = base64.b64encode(img_data2).decode()
style = f"""
<style>
.stApp{{
background-image: url(data:image/png;base64,{b64_encoded1});
background-size: cover;
}}
.st-emotion-cache-6qob1r{{
background-image: url(data:image/png;base64,{b64_encoded2});
background-size: cover;
border: 5px solid rgb(14, 17, 23);
}}
</style>
"""
st.markdown(style, unsafe_allow_html=True)
set_background('pngtree-city-map-navigation-interface-picture-image_1833642.png','2024-05-18_14-57-09_5235.png')
st.title("Traffic Flow and Optimization Toolkit")
sb = st.sidebar # defining the sidebar
sb.markdown("🛰️ **Navigation**")
page_names = ["PS1", "PS2", "PS3","Chat with Results"]
page = sb.radio("", page_names, index=0)
st.session_state['n'] = sb.slider("Number of ROIs",1,5)
if page == 'PS1':
uploaded_file = st.file_uploader("Choose a video...", type=["mp4", "mpeg"])
model = YOLO('yolov8n.pt')
if uploaded_file is not None:
with tempfile.NamedTemporaryFile(delete=False) as temp:
temp.write(uploaded_file.read())
if 'roi_list1' not in st.session_state:
st.session_state['roi_list1'] = []
if "all_rois1" not in st.session_state:
st.session_state['all_rois1'] = []
classes = model.names
done_1 = st.button('Selection Done')
while len(st.session_state["all_rois1"]) < st.session_state['n']:
cap = cv2.VideoCapture(temp.name)
while not done_1:
ret,frame=cap.read()
cv2.putText(frame,'SELECT ROI',(100,100),cv2.FONT_HERSHEY_SIMPLEX, 1,(0,0,255),4)
if not ret:
st.write('ROI selection unsuccessfull')
break
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
value = streamlit_image_coordinates(frame,key='numpy',width=750)
st.session_state["roi_list1"].append([int(value['x']*2.55),int(value['y']*2.55)])
st.write(st.session_state["roi_list1"])
if cv2.waitKey(0)&0xFF==27:
break
cap.release()
st.session_state["all_rois1"].append(st.session_state["roi_list1"])
st.session_state["roi_list1"] = []
done_1 = False
st.write('ROI indices: ',st.session_state["all_rois1"][0])
cap = cv2.VideoCapture(temp.name)
st.write("Detection started")
st.session_state['fps'] = cap.get(cv2.CAP_PROP_FPS)
st.write(f"FPS OF VIDEO: {st.session_state['fps']}")
avg_list = []
count = 0
frame_placeholder = st.empty()
st.session_state["data1"] = {}
for i in range(len(st.session_state["all_rois1"])):
st.session_state["data1"][f"ROI{i}"] = []
while cap.isOpened():
ret,frame=cap.read()
if not ret:
break
count += 1
if count % 3 != 0:
continue
k = 0
for roi_list_here1 in st.session_state["all_rois1"]:
max = [0,0]
min = [10000,10000]
roi_list_here = roi_list_here1[1:]
for i in range(len(roi_list_here)):
if roi_list_here[i][0] > max[0]:
max[0] = roi_list_here[i][0]
if roi_list_here[i][1] > max[1]:
max[1] = roi_list_here[i][1]
if roi_list_here[i][0] < min[0]:
min[0] = roi_list_here[i][0]
if roi_list_here[i][1] < min[1]:
min[1] = roi_list_here[i][1]
frame_cropped = frame[min[1]:max[1],min[0]:max[0]]
roi_corners = np.array([roi_list_here],dtype=np.int32)
mask = np.zeros(frame.shape,dtype=np.uint8)
mask.fill(255)
channel_count = frame.shape[2]
ignore_mask_color = (255,)*channel_count
cv2.fillPoly(mask,roi_corners,0)
mask_cropped = mask[min[1]:max[1],min[0]:max[0]]
roi = cv2.bitwise_or(frame_cropped,mask_cropped)
#roi = frame[roi_list_here[0][1]:roi_list_here[1][1],roi_list_here[0][0]:roi_list_here[1][0]]
number = []
results = model.predict(roi)
for r in results:
boxes = r.boxes
counter = 0
for box in boxes:
counter += 1
name = classes[box.cls.numpy()[0]]
conf = str(round(box.conf.numpy()[0],2))
text = name+""+conf
bbox = box.xyxy[0].numpy()
cv2.rectangle(frame,(int(bbox[0])+min[0],int(bbox[1])+min[1]),(int(bbox[2])+min[0],int(bbox[3])+min[1]),(0,255,0),2)
cv2.putText(frame,text,(int(bbox[0])+min[0],int(bbox[1])+min[1]-5),cv2.FONT_HERSHEY_SIMPLEX, 1,(0,0,255),2)
number.append(counter)
avg = sum(number)/len(number)
stats = str(round(avg,2))
if count%10 == 0:
st.session_state["data1"][f"ROI{k}"].append(avg)
k+=1
cv2.putText(frame,stats,(min[0],min[1]),cv2.FONT_HERSHEY_SIMPLEX, 1,(0,0,0),4)
cv2.polylines(frame,roi_corners,True,(255,0,0),2)
cv2.putText(frame,'The average number of vehicles in the Regions of Interest',(100,100),cv2.FONT_HERSHEY_SIMPLEX, 1,(0,0,255),4)
frame_placeholder.image(frame,channels='BGR')
cap.release()
st.write("The resultant data is:")
st.write(st.session_state.data1)
else:
st.error('PLEASE UPLOAD', icon="🚨")
elif page == "PS3":
uploaded_file1 = st.file_uploader("Choose a video...", type=["mp4", "mpeg"])
model1 = YOLO("yolov8n.pt")
model2 = YOLO("best.pt")
if uploaded_file1 is not None:
with tempfile.NamedTemporaryFile(delete=False) as temp:
temp.write(uploaded_file1.read())
if 'roi_list2' not in st.session_state:
st.session_state['roi_list2'] = []
if "all_rois2" not in st.session_state:
st.session_state['all_rois2'] = []
classes = model1.names
done_2 = st.button('Selection Done')
while len(st.session_state["all_rois2"]) < st.session_state['n']:
cap = cv2.VideoCapture(temp.name)
while not done_2:
ret,frame=cap.read()
cv2.putText(frame,'SELECT ROI',(100,100),cv2.FONT_HERSHEY_SIMPLEX, 1,(0,0,255),4)
if not ret:
st.write('ROI selection has concluded')
break
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
value = streamlit_image_coordinates(frame,key='numpy',width=750)
st.session_state["roi_list2"].append([int(value['x']*2.5),int(value['y']*2.5)])
st.write(st.session_state["roi_list2"])
if cv2.waitKey(0)&0xFF==27:
break
cap.release()
st.session_state["all_rois2"].append(st.session_state["roi_list2"])
st.session_state["roi_list2"] = []
done_2 = False
st.write('ROI indices: ',st.session_state["all_rois2"][0])
cap = cv2.VideoCapture(temp.name)
st.write("Detection started")
avg_list = []
count = 0
frame_placeholder = st.empty()
st.session_state.data = {}
for i in range(len(st.session_state["all_rois2"])):
st.session_state["data"][f"ROI{i}"] = []
for i in range(len(st.session_state['all_rois2'])):
st.session_state.data[f"ROI{i}"] = []
while cap.isOpened():
ret,frame=cap.read()
if not ret:
break
count += 1
if count % 3 != 0:
continue
# rois = []
k = 0
for roi_list_here1 in st.session_state["all_rois2"]:
max = [0,0]
min = [10000,10000]
roi_list_here = roi_list_here1[1:]
for i in range(len(roi_list_here)-1):
if roi_list_here[i][0] > max[0]:
max[0] = roi_list_here[i][0]
if roi_list_here[i][1] > max[1]:
max[1] = roi_list_here[i][1]
if roi_list_here[i][0] < min[0]:
min[0] = roi_list_here[i][0]
if roi_list_here[i][1] < min[1]:
min[1] = roi_list_here[i][1]
frame_cropped = frame[min[1]:max[1],min[0]:max[0]]
roi_corners = np.array([roi_list_here],dtype=np.int32)
mask = np.zeros(frame.shape,dtype=np.uint8)
mask.fill(255)
channel_count = frame.shape[2]
ignore_mask_color = (255,)*channel_count
cv2.fillPoly(mask,roi_corners,0)
mask_cropped = mask[min[1]:max[1],min[0]:max[0]]
roi = cv2.bitwise_or(frame_cropped,mask_cropped)
#roi = frame[roi_list_here[0][1]:roi_list_here[1][1],roi_list_here[0][0]:roi_list_here[1][0]]
number = []
results = model1.predict(roi)
results_pothole = model2.predict(source=frame)
for r in results:
boxes = r.boxes
counter = 0
for box in boxes:
counter += 1
name = classes[box.cls.numpy()[0]]
conf = str(round(box.conf.numpy()[0],2))
text = name+conf
bbox = box.xyxy[0].numpy()
cv2.rectangle(frame,(int(bbox[0])+min[0],int(bbox[1])+min[1]),(int(bbox[2])+min[0],int(bbox[3])+min[1]),(0,255,0),2)
cv2.putText(frame,text,(int(bbox[0])+min[0],int(bbox[1])+min[1]-5),cv2.FONT_HERSHEY_SIMPLEX, 0.4,(0,0,255),2)
number.append(counter)
for r in results_pothole:
masks = r.masks
boxes = r.boxes.cpu().numpy()
xyxys = boxes.xyxy
confs = boxes.conf
if masks is not None:
shapes = np.ones_like(frame)
for mask,conf,xyxy in zip(masks,confs,xyxys):
polygon = mask.xy[0]
if conf >= 0.49 and len(polygon)>=3:
cv2.fillPoly(shapes,pts=np.int32([polygon]),color=(0,0,255,0.5))
frame = cv2.addWeighted(frame,0.7,shapes,0.3,gamma=0)
cv2.rectangle(frame,(int(xyxy[0]),int(xyxy[1])),(int(xyxy[2]),int(xyxy[3])),(0,0,255),2)
cv2.putText(frame,'Pothole '+str(conf),(int(xyxy[0]),int(xyxy[1])-5),cv2.FONT_HERSHEY_SIMPLEX, 1,(0,0,255),2)
avg = sum(number)/len(number)
stats = str(round(avg,2))
if count % 10 == 0:
st.session_state.data[f"ROI{k}"].append(avg)
k+=1
cv2.putText(frame,stats,(min[0],min[1]),cv2.FONT_HERSHEY_SIMPLEX, 1,(0,0,0),4)
cv2.polylines(frame,roi_corners,True,(255,0,0),2)
if counter >= 5:
cv2.putText(frame,'!!CONGESTION MORE THAN '+str(counter)+' Objects',(min[0]+20,min[1]+20),cv2.FONT_HERSHEY_SIMPLEX, 1,(0,0,0),4)
cv2.polylines(frame,roi_corners,True,(255,0,0),2)
cv2.putText(frame,'Objects in the Regions of Interest',(100,100),cv2.FONT_HERSHEY_SIMPLEX, 1,(0,0,255),4)
frame_placeholder.image(frame,channels='BGR')
cap.release()
st.write("The result is:")
st.write(st.session.data)
else:
st.error('PLEASE UPLOAD', icon="🚨")
elif page == "PS2":
st.header("CLICK ON RUN SCRIPT TO START A TRAFFIC SIMULATION")
script = st.button("RUN SCRIPT")
st.error("This simulation, Unfortunately was not optimized for deployed but still works for locally run version", icon="🚨")
st.write("check out this [link](https://github.com/pranavsrinivasa/Traffic-Optimization-Projects)")
elif page == "Chat with Results":
st.title('Chat with the Results')
st.write("Please upload the relevant CSV data to get started")
st.error("This model, Unfortunately was not optimized for deployed but still works for locally run version", icon="🚨")
st.write("check out this [link](https://github.com/pranavsrinivasa/Traffic-Optimization-Projects)")
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