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best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b81444d786fee3c4325e135c4f6c63c6df64402f517458b3186ea032e7edacc7
+size 87596350

main.py

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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()

requirements.txt

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+ultralytics
+gtts
+streamlit
+pillow