First Commit

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,275 @@
+import numpy as np
+import pandas as pd
+from ultralytics import YOLO
+import streamlit as st
+import cv2
+import base64
+import time
+import shutil
+import os
+from PIL import Image
+import base64
+
+
+st.set_page_config(layout="wide",initial_sidebar_state="expanded",
+                   page_icon='🔎',page_title='Poth-Hole Detector')
+
+
+def get_video_base64(video_path):
+    with open(video_path, "rb") as file:
+        video_bytes = file.read()
+        base64_encoded = base64.b64encode(video_bytes).decode("utf-8")
+        return base64_encoded
+
+video_path = "deep1.mp4"
+video_base64 = get_video_base64(video_path)
+
+video_html = f"""
+	<style>
+	#myVideo {{
+		position: fixed;
+		right: 0;
+		bottom: 0;
+		min-width: 100%; 
+		min-height: 100%;
+	}}
+	.content {{
+		position: fixed;
+		bottom: 0;
+		background: rgba(0, 0, 0, 0.5);
+		color: #f1f1f1;
+		width: 100%;
+		padding: 20px;
+	}}
+
+	</style>
+
+	<video autoplay loop muted id="myVideo">
+		<source type="video/mp4" src="data:video/mp4;base64,{video_base64}">
+	</video>
+"""
+
+st.markdown(video_html, unsafe_allow_html=True)
+
+# Define custom style for the glowing text
+glowing_text_style = '''
+    <style>
+        .glowing-text {
+            font-family: 'Arial Black', sans-serif;
+            font-size: 48px;
+            text-align: center;
+            animation: glowing 2s infinite;
+        }
+        
+        @keyframes glowing {
+            0% { color: #FF9933; } /* Saffron color */
+            25% { color: #FFFFFF; } /* White color */
+            50% { color: #128807; } /* Green color */
+            75% { color: #0000FF; } /* Blue color */
+            100% { color: #FF9933; } /* Saffron color */
+        }
+    </style>
+'''
+
+# Display the glowing text using st.markdown
+st.markdown(glowing_text_style, unsafe_allow_html=True)
+st.markdown(f'<p class="glowing-text">🕳️ PothHole Detector 🕳️</p>', unsafe_allow_html=True)
+
+def upload():
+    image=None
+    image_filename=None
+    initial_image = st.camera_input('Take a picture')
+    original_image = initial_image
+    temp_path = None
+    if initial_image is not None:
+        image_filename = f"{int(time.time())}.jpg"
+        bytes_data = initial_image.getvalue()
+        image = cv2.imdecode(np.frombuffer(bytes_data, np.uint8), cv2.IMREAD_COLOR)
+
+    return image, original_image,image_filename
+
+def process_line(line, image_np,counter):
+    # Process a single line from the labels.txt file
+    bresults = line.split()
+    if len(bresults) >=5:
+        names={0:'POTH_HOLE'}
+        xc, yc, nw, nh = map(float, bresults[1:5])
+        h, w = image_np.shape[0], image_np.shape[1]
+
+        xc *= w
+        yc *= h
+        nw *= w
+        nh *= h
+        top_left = (int(xc - nw / 2), int(yc - nh / 2))
+        bottom_right = (int(xc + nw / 2), int(yc + nh / 2))
+
+        # Draw bounding box
+        cv2.rectangle(image_np, top_left, bottom_right, (4, 29, 255), 3, cv2.LINE_4)
+
+        # Draw label text
+        #label = names[int(bresults[0])]
+        label = f'{names[int(bresults[0])]}-{counter}'
+        text_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 2, 3)[0]
+        text_width, text_height = text_size
+        text_x = (top_left[0] + bottom_right[0] - text_width) // 2 + 100
+        text_y = top_left[1] - 10
+        cv2.putText(image_np, label, (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)
+        
+
+ 
+sidebar_option = st.sidebar.radio("Select an option", ("Take picture for prediction", "Upload file"))
+
+def main():
+    
+    
+    
+    
+   
+    if sidebar_option == "Take picture for prediction":
+        if st.checkbox('Take a picture for prediction'):
+    
+        
+            image, original_image,image_filename= upload()
+            if original_image is not None and original_image is not None and len(image_filename)!=0 and st.button('Prediction'):  # Check if original_image is not None
+                st.info('Wait for the results...!')
+                #image1=cv2.imread(image)
+                
+                counter=1
+                names={0:'POTH_HOLE'
+                    }
+               
+                model=YOLO('best.pt')
+                result = model.predict(image,save=True,save_txt=True)
+                txt_files_exist = any(filename.endswith('.txt') for filename in os.listdir('runs/detect/predict/labels'))
+                if txt_files_exist:
+                    lis=open('runs/detect/predict/labels/image0.txt','r').readlines()
+                    for line in lis:
+                        process_line(line, image_np,counter)
+                        counter+=1
+                    with st.spinner('Wait for the results...!'):
+                        time.sleep(5)
+                    
+                    st.image(image,use_column_width=True)
+                    st.balloons()
+                    
+                    try:
+                        if os.path.exists('runs'):
+                            shutil.rmtree('runs')
+                            st.session_state.original_image = None  # Clear the original_image variable
+                           
+                    except Exception as e:
+                        st.error(f"An error occurred: {e}")
+                else:
+                    st.warning('⚠️Please check your image')
+                    try:
+                        if os.path.exists('runs'):
+                            shutil.rmtree('runs')
+                            st.session_state.original_image = None  # Clear the original_image variable
+                           
+                    except Exception as e:
+                        st.error(f"An error occurred: {e}")
+    elif sidebar_option == "Upload file":  
+                  
+        
+        fileimage=st.file_uploader('Upload the file for detection 📁',type=['jpg'])
+       
+        if st.button('Predict'):
+            if fileimage is None:
+                st.warning('🛎️We are using default image 📷')
+                counter=1
+                default_image_path='df.jpg'
+                pic = Image.open(default_image_path)
+                image_np = np.array(pic)
+                names={0:'POTH_HOLE'
+                        }
+                mod1=YOLO('best.pt')
+                mod1.predict(image_np,save=True,save_txt=True)
+                txt_files_exist = any(filename.endswith('.txt') for filename in os.listdir('runs/detect/predict/labels'))
+                if txt_files_exist:
+                    lis=open('runs/detect/predict/labels/image0.txt','r').readlines()
+                    with st.spinner('Wait for the results...!'):
+                        time.sleep(5)
+    
+                                    
+                    for line in lis:
+                        process_line(line, image_np,counter)
+                        counter+=1
+                    col1,col2=st.columns(2)
+                    with col1:
+                                
+                        st.info('Original Image!')
+                        st.image(default_image_path,use_column_width=True)
+                    with col2:
+                        st.info('Detected Image!')
+                        st.image(image_np,use_column_width=True)
+                    st.balloons()
+                        
+                    try:
+                        if os.path.exists('runs'):
+                            shutil.rmtree('runs')
+                            st.session_state.original_image = None  # Clear the original_image variable
+                                
+                    except Exception as e:
+                        st.error(f"An error occurred: {e}")
+                    
+            else:
+                    
+                if fileimage is not None and not fileimage.name.endswith('.jpg'):
+                    st.warning('⚠️ Please upload only JPG images.')
+                else:
+                    st.info('Wait for the results...!')
+                    counter=1
+                    pic=Image.open(fileimage)
+                    image_np = np.array(pic)
+                    names={0:'POTH_HOLE'
+                        }
+                    mod1=YOLO('best.pt')
+                    mod1.predict(image_np,save=True,save_txt=True)
+                    txt_files_exist = any(filename.endswith('.txt') for filename in os.listdir('runs/detect/predict/labels'))
+                    if txt_files_exist:
+                        lis=open('runs/detect/predict/labels/image0.txt','r').readlines()
+                        with st.spinner('Wait for the results...!'):
+                            time.sleep(5)
+    
+                                    
+                        for line in lis:
+                            process_line(line, image_np,counter)
+                            counter+=1
+                        col1,col2=st.columns(2)
+                        with col1:
+                                
+                            st.info('Original Image!')
+                            st.image(fileimage,use_column_width=True)
+                        with col2:
+                            st.info('Detected Image!')
+                            st.image(image_np,use_column_width=True)
+                        st.balloons()
+                        
+                        try:
+                            if os.path.exists('runs'):
+                                shutil.rmtree('runs')
+                                st.session_state.original_image = None  # Clear the original_image variable
+                                
+                        except Exception as e:
+                            st.error(f"An error occurred: {e}")
+                    else:
+                        st.warning('⚠️Please check your image')
+                        try:
+                            if os.path.exists('runs'):
+                                shutil.rmtree('runs')
+                                st.session_state.original_image = None  # Clear the original_image variable
+                            
+                        except Exception as e:
+                            st.error(f"An error occurred: {e}")
+                    
+
+        
+        
+            
+
+   
+if __name__ == '__main__':
+    
+   
+    main()
+    

best.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:155fb805da8c4c965f6f20a3c2e695ce8d692a5d1cd712e37e0aad2884a01490
+size 136668926

deep1.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:46c04a20444ea67fc7bce121bb50b11b6bee19e88268236f6abda947b741cd7d
+size 61427868

requirements.txt

@@ -0,0 +1,3 @@
+opecv-contrib-python-headless
+ultralytics
+streamlit