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from PIL import Image, ImageDraw
from ultralytics import YOLO
import streamlit as st
import tempfile
import cv2
import numpy as np
import base64
# Initialize YOLO model
model = YOLO("best.pt")
# Function to perform object detection on an image
def detect_objects_image(image):
results = model(image)
result = results[0]
output = []
num_potholes_detected = 0
num_cracks_detected = 0
num_alligator_cracks_detected = 0
for box in result.boxes:
x1, y1, x2, y2 = [round(x) for x in box.xyxy[0].tolist()]
class_id = box.cls[0].item()
prob = round(box.conf[0].item(), 2)
class_name = result.names[class_id]
output.append([x1, y1, x2, y2, class_name, prob])
# Count detections by class
if class_name == "pothole":
num_potholes_detected += 1
elif class_name == "crack":
num_cracks_detected += 1
elif class_name == "alligator-crack":
num_alligator_cracks_detected += 1
return output, num_potholes_detected, num_cracks_detected, num_alligator_cracks_detected
# Function to process and annotate a video
def process_video(video_path, output_path, frame_interval):
cap = cv2.VideoCapture(video_path)
fps = int(cap.get(cv2.CAP_PROP_FPS))
frame_interval_count = int(fps * frame_interval)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
frame_count = 0
detections_summary = {
'potholes': 0,
'cracks': 0,
'alligator_cracks': 0
}
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
if frame_count % frame_interval_count == 0:
image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
detections, num_potholes, num_cracks, num_alligator_cracks = detect_objects_image(image)
detections_summary['potholes'] += num_potholes
detections_summary['cracks'] += num_cracks
detections_summary['alligator_cracks'] += num_alligator_cracks
draw = ImageDraw.Draw(image)
for detection in detections:
x1, y1, x2, y2, class_name, prob = detection
draw.rectangle([x1, y1, x2, y2], outline="red", width=3)
text = f"{class_name} {prob:.2f}"
draw.text((x1, y1), text, fill="red")
annotated_frame = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
else:
annotated_frame = frame
out.write(annotated_frame)
frame_count += 1
cap.release()
out.release()
return detections_summary
# Function to generate a download link for a file
def get_download_link(file_path, text, file_type):
with open(file_path, 'rb') as f:
file_bytes = f.read()
file_b64 = base64.b64encode(file_bytes).decode()
download_link = f'<a href="data:{file_type};base64,{file_b64}" download="{text}">{text}</a>'
return download_link
# Streamlit app
def main():
st.title("Road Condition Inspection")
st.subheader("Upload an image or video to detect objects")
# File uploader for image and video
uploaded_file = st.file_uploader("Choose a file...", type=["jpg", "jpeg", "png", "mp4"])
if uploaded_file is not None:
file_type = uploaded_file.type
temp_file = tempfile.NamedTemporaryFile(delete=False, suffix="." + uploaded_file.name.split('.')[-1])
temp_file.write(uploaded_file.read())
temp_file.close()
if file_type.startswith("image"):
image = Image.open(temp_file.name)
st.image(image, caption='Uploaded Image', use_column_width=True)
if st.button('Detect Objects (Image)'):
detections, num_potholes, num_cracks, num_alligator_cracks = detect_objects_image(image)
draw = ImageDraw.Draw(image)
for detection in detections:
x1, y1, x2, y2, class_name, prob = detection
draw.rectangle([x1, y1, x2, y2], outline="red", width=3)
text = f"{class_name} {prob:.2f}"
draw.text((x1, y1), text, fill="red")
st.image(image, caption='Annotated Image', use_column_width=True)
st.subheader("Detection Summary")
if num_potholes > 0:
st.write(f"Potholes Detected: {num_potholes}")
if num_cracks > 0:
st.write(f"Cracks Detected: {num_cracks}")
if num_alligator_cracks > 0:
st.write(f"Alligator Cracks Detected: {num_alligator_cracks}")
annotated_image_path = temp_file.name.replace(".", "_annotated.")
image.save(annotated_image_path)
st.markdown(get_download_link(annotated_image_path, "Download Annotated Image", "image/png"), unsafe_allow_html=True)
elif file_type.startswith("video"):
video_bytes = open(temp_file.name, 'rb').read()
st.video(video_bytes)
if st.button('Detect Objects (Video)'):
annotated_video_path = temp_file.name.replace(".", "_annotated") + ".mp4"
detections_summary = process_video(temp_file.name, annotated_video_path, frame_interval=1)
st.subheader("Annotated Video Download")
st.markdown(get_download_link(annotated_video_path, "Download Annotated Video", "video/mp4"), unsafe_allow_html=True)
st.subheader("Detection Summary")
if detections_summary['potholes'] > 0:
st.write(f"Total Potholes Detected: {detections_summary['potholes']}")
if detections_summary['cracks'] > 0:
st.write(f"Total Cracks Detected: {detections_summary['cracks']}")
if detections_summary['alligator_cracks'] > 0:
st.write(f"Total Alligator Cracks Detected: {detections_summary['alligator_cracks']}")
if __name__ == '__main__':
main()
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