Upload 5 files

.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
+input_video.mp4 filter=lfs diff=lfs merge=lfs -text

best.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4945128a2f1a133d1429a3e04861177cb8f95ba9445d27becf6cf832fdd731e5
+size 6254425

input_video.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e4ea71aa6ffc460c430debe677e9cecea6e2a1712d45ce26d48ace0a1aa76a83
+size 4143534

n.py

@@ -0,0 +1,122 @@
+import PIL
+import streamlit as st
+from ultralytics import YOLO
+import cv2
+import os
+
+# Give the path of the best.pt (best weights)
+model_dir="model"
+model_file="best.pt"
+model_path = os.path.join(model_dir, model_file)
+
+# Setting page layout
+st.set_page_config(
+    page_title="PPE(Private Protective Equipment)",  # Setting page title
+    #page_icon="NK logo.jpeg",     # Setting page icon
+    layout="wide",      # Setting layout to wide
+    initial_sidebar_state="expanded",    # Expanding sidebar by default
+    
+)
+
+try:
+    model = YOLO(model_path)
+    names=model.names
+except Exception as ex:
+    st.error(
+        f"Unable to load model. Check the specified path: {model_path}")
+    st.error(ex)
+
+
+# Creating sidebar
+with st.sidebar:
+    st.header("Upload The Image")     # Adding header to sidebar
+    # Adding file uploader to sidebar for selecting images
+    source = st.file_uploader(
+        "Upload an image or video...", type=("jpg", "jpeg", "png", 'bmp', 'webp','mp4'))
+    
+    #st.sidebar("Upload the video")   #adding header to sidebar
+    # Adding file uploader to sidebar for selecting videos
+    #source_video=st.file_uploader("Upload a video...", type=("mp4"))
+
+    # Model Options
+    confidence = float(st.slider(
+        "Select Model Confidence", 25, 100, 40)) / 100
+
+# Creating main page heading
+st.title("PPE(Private Protective Equipment) Detection")
+st.caption('Updload a photo or video by selecting :blue[Browse files]')
+st.caption('Then click the :blue[Detect Objects] button and check the result.')
+# Creating two columns on the main page
+col1, col2 = st.columns(2)
+
+# Adding image to the first column if image is uploaded
+with col1:
+     #checking if the source is not empty
+    if source is not None:
+
+        #getting the file extentions from the uploaded file using name.split()
+        file_extension = source.name.split(".")[-1]
+
+        #checking if the file extention is image or not
+        if file_extension in ["jpg", "jpeg", "png"]:
+
+            # Opening the uploaded image
+            uploaded_image = PIL.Image.open(source)
+
+            # Getting the image size
+            image_width, image_height = uploaded_image.size
+
+            # Adding the uploaded image to the page with a caption
+            st.image(uploaded_image,
+                    caption="Uploaded Image",
+                    width=image_width
+                        )
+            #checking if the the button is clicked
+            if st.sidebar.button('Detect Objects'):
+                #prediction based on the image with conf=confidence from the slider
+                res = model.predict(uploaded_image,
+                                    conf=confidence,
+                                    line_width=2, 
+                                    show_labels=False, 
+                                    show_conf=False
+                                    )
+                #extracting information about the bounding box from res 
+                boxes = res[0].boxes
+                #plotting the bounding box with confidence and labels
+                res_plotted = res[0].plot(labels=True, line_width=1)[:, :, ::-1]
+                with col2:
+                    st.image(res_plotted,
+                            caption='Detected Image',
+                            width=image_width                 
+                            )
+                    try:
+                        st.write(f'Number of detected: {len(boxes)}')
+                        with st.expander("class detected:"):                
+                            for c in boxes.cls:
+                                st.write(names[int(c)])
+                                #print(names[int(c)])
+                    except Exception as ex:
+                        st.write("No image is uploaded yet!")
+            
+        elif file_extension == "mp4":
+
+            video_bytes = source.read()
+            st.video(video_bytes)
+            # Save video locally
+            with open("input_video.mp4", "wb") as f:
+                f.write(video_bytes)
+            if st.sidebar.button("Detect Objects"):
+                with col2:
+                    vid_cap = cv2.VideoCapture("input_video.mp4")
+                    st_frame = st.empty()
+                    while vid_cap.isOpened():
+                        success, image = vid_cap.read()
+                        if success:
+                            image = cv2.resize(image, (720, int(720 * (9 / 16))))
+                            res = model.predict(image, conf=confidence)
+                            result_tensor = res[0].boxes
+                            res_plotted = res[0].plot()
+                            st_frame.image(res_plotted, caption="Detected Video", channels="BGR", use_column_width=True)
+                        else:
+                            vid_cap.release()
+                            break

requirements.txt

@@ -0,0 +1,3 @@
+ultralytics==8.0.196
+jupyter
+ipykernel