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Create app.py
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app.py
ADDED
@@ -0,0 +1,261 @@
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1 |
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#import semua library yang dibutuhkan
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import os
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import io
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import cv2
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import numpy as np
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from PIL import Image, ImageDraw
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from transformers import AutoImageProcessor, AutoModelForObjectDetection
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import streamlit as st
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import torch
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import time
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import pandas as pd
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# Setting page layout
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st.set_page_config(
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page_title="YoloS Helmet Detection",
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page_icon="🤗",
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layout="wide",
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initial_sidebar_state="expanded"
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)
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def input_image_setup(uploaded_file):
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if uploaded_file is not None:
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bytes_data = uploaded_file.getvalue()
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image = Image.open(io.BytesIO(bytes_data)) # Convert bytes data to PIL image
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return image
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else:
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raise FileNotFoundError("No file uploaded")
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# Function to convert OpenCV image to PIL image
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def cv2_to_pil(image):
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return Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
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def draw_bounding_boxes(image, results, model, confidence):
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draw = ImageDraw.Draw(image)
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for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
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if score.item() >= confidence:
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box = [int(i) for i in box.tolist()]
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draw.rectangle(box, outline="purple", width=2)
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label_text = f"{model.config.id2label[label.item()]} ({round(score.item(), 2)})"
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draw.text((box[0], box[1]), label_text, fill="white")
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return image
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def process_image(image, model, processor, confidence):
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inputs = processor(images=image, return_tensors="pt")
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outputs = model(**inputs)
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target_sizes = torch.tensor([image.size[::-1]])
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results = processor.post_process_object_detection(outputs, threshold=confidence, target_sizes=target_sizes)[0]
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return results
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def detection_results_to_dict(results, model):
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detection_dict = {
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"objects": []
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}
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for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
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box = [round(i, 2) for i in box.tolist()]
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detection_dict["objects"].append({
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"label": model.config.id2label[label.item()],
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"confidence": round(score.item(), 3),
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"box": box
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})
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return detection_dict
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def convert_dict_to_csv(detection_dict_list):
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combined_results = []
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for detection_dict in detection_dict_list:
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combined_results.extend(detection_dict["objects"])
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df = pd.DataFrame(combined_results)
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return df.to_csv(index=False).encode('utf-8')
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def clear_detection_results():
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st.session_state.detection_dict_list = []
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# Initialize session state to store detection results
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if 'detection_dict_list' not in st.session_state:
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st.session_state.detection_dict_list = []
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# Streamlit App Configuration
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st.header("Helmet Rider Detection")
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# Sidebar for Model Selection and Confidence Slider
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st.sidebar.header("ML Model Config")
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models = ["gghsgn/final200" ,"gghsgn/final100", "/gghsgn/final50"]
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model_name = st.sidebar.selectbox("Select model", models)
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confidence = st.sidebar.slider("Select Model Confidence", 25, 100, 40, step=5) / 100
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# Load Model and Processor
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processor = AutoImageProcessor.from_pretrained(model_name)
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model = AutoModelForObjectDetection.from_pretrained(model_name)
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# Option to select Real-Time or Upload Image
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mode = st.sidebar.selectbox("Select Input Mode", ["Upload Image", "Real-Time Webcam", "RTSP Video"])
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# Option if select Upload Image
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if mode == "Upload Image":
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uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
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if uploaded_file is not None:
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image = Image.open(uploaded_file)
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st.image(image, caption="Uploaded Image.", use_column_width=True)
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else:
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image = None
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submit = st.button("Detect Objects")
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if submit and image is not None:
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try:
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image_data = input_image_setup(uploaded_file)
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st.subheader("The response is..")
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results = process_image(image, model, processor, confidence)
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drawn_image = draw_bounding_boxes(image.copy(), results, model, confidence)
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st.image(drawn_image, caption="Detected Objects", use_column_width=True)
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st.subheader("List of Objects:")
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for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
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box = [round(i, 2) for i in box.tolist()]
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st.write(
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f"Detected :orange[{model.config.id2label[label.item()]}] with confidence "
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f":green[{round(score.item(), 3)}] at location :violet[{box}]"
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)
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detected_objects = {model.config.id2label[label.item()]: 0 for label in results["labels"]}
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for label in results["labels"]:
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detected_objects[model.config.id2label[label.item()]] += 1
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for obj, count in detected_objects.items():
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st.write(f"Class :orange[{obj}] detected {count} time(s)")
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detection_dict = detection_results_to_dict(results, model)
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#st.write(detection_dict)
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csv_data = convert_dict_to_csv([detection_dict])
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st.download_button(
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label="Download Results as CSV",
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data=csv_data,
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file_name="detection_results.csv",
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mime="text/csv"
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)
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except Exception as e:
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st.error(f"Error: {e}")
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elif submit and image is None:
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st.error("Please upload an image before trying to detect objects.")
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# Option if select Realtime Webcam
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elif mode == "Real-Time Webcam":
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run = st.checkbox("Run Webcam")
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FRAME_WINDOW = st.image([])
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if run:
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cap = cv2.VideoCapture(0)
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while run:
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ret, frame = cap.read()
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153 |
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if not ret:
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st.error("Failed to capture image from webcam")
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break
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frame_pil = cv2_to_pil(frame)
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try:
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results = process_image(frame_pil, model, processor, confidence)
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drawn_image = draw_bounding_boxes(frame_pil.copy(), results, model, confidence)
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FRAME_WINDOW.image(drawn_image, caption="Detected Objects", use_column_width=True)
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+
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st.subheader("List of Objects:")
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for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
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box = [round(i, 2) for i in box.tolist()]
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167 |
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st.write(
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f"Detected :orange[{model.config.id2label[label.item()]}] with confidence "
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f":green[{round(score.item(), 3)}] at location :violet[{box}]"
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)
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detected_objects = {model.config.id2label[label.item()]: 0 for label in results["labels"]}
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173 |
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for label in results["labels"]:
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detected_objects[model.config.id2label[label.item()]] += 1
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175 |
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for obj, count in detected_objects.items():
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st.write(f"Class :orange[{obj}] detected {count} time(s)")
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177 |
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detection_dict = detection_results_to_dict(results, model)
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179 |
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st.session_state.detection_dict_list.append(detection_dict)
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180 |
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#st.write(detection_dict)
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181 |
+
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182 |
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183 |
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except Exception as e:
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184 |
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st.error(f"Error: {e}")
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185 |
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186 |
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time.sleep(0.1) # Delay for the next frame capture to create an illusion of real-time
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cap.release()
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+
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189 |
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if not run and st.session_state.detection_dict_list:
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st.write("Detection stopped.")
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csv_data = convert_dict_to_csv(st.session_state.detection_dict_list)
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st.download_button(
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label="Download All Results as CSV",
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194 |
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data=csv_data,
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file_name="all_detection_results.csv",
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mime="text/csv"
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)
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198 |
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st.button("Clear Results", on_click=clear_detection_results)
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# Option if select Realtime RTSP Video
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elif mode == "RTSP Video":
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rtsp_url = st.text_input("RTSP URL")
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run = st.checkbox("Run RTSP Video")
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FRAME_WINDOW = st.image([])
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if rtsp_url and run:
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cap = cv2.VideoCapture(rtsp_url)
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st.subheader("List of Objects:")
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while run:
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ret, frame = cap.read()
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if not ret:
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st.error("Failed to capture image from RTSP stream")
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break
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frame_pil = cv2_to_pil(frame)
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try:
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results = process_image(frame_pil, model, processor, confidence)
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drawn_image = draw_bounding_boxes(frame_pil.copy(), results, model, confidence)
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FRAME_WINDOW.image(drawn_image, caption="Detected Objects", use_column_width=True)
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st.subheader("List of Objects:")
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for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
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box = [round(i, 2) for i in box.tolist()]
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st.write(
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f"Detected :orange[{model.config.id2label[label.item()]}] with confidence "
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f":green[{round(score.item(), 3)}] at location :violet[{box}]"
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)
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detected_objects = {model.config.id2label[label.item()]: 0 for label in results["labels"]}
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for label in results["labels"]:
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detected_objects[model.config.id2label[label.item()]] += 1
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for obj, count in detected_objects.items():
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st.write(f"Class :orange[{obj}] detected {count} time(s)")
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+
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detection_dict = detection_results_to_dict(results, model)
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st.session_state.detection_dict_list.append(detection_dict)
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#st.write(detection_dict)
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+
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except Exception as e:
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st.error(f"Error: {e}")
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+
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time.sleep(0.1) # Delay for the next frame capture to create an illusion of real-time
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+
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cap.release()
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if not run and st.session_state.detection_dict_list:
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st.write("Detection stopped.")
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csv_data = convert_dict_to_csv(st.session_state.detection_dict_list)
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st.download_button(
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label="Download All Results as CSV",
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data=csv_data,
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file_name="all_detection_results.csv",
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mime="text/csv"
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)
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st.button("Clear Results", on_click=clear_detection_results)
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elif run:
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st.error("Please provide a valid RTSP URL before running the stream.")
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+
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# Ensure the video capture object is released if the checkbox is unchecked
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if 'cap' in locals():
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cap.release()
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