Deploying in web

22AIE114_Project_codes_A12/YOLO(Python)/main.py

@@ -1,111 +1,68 @@
-import io
+import streamlit as st
 import cv2
 import telebot
 import requests
+import numpy as np
+import io
 
-# TODO: 1.1 Get your environment variables
-chat_id=''
-bot_id = ''
-channel_id="@amrita_eco"
+# Initialize Telegram bot
+bot_id = st.secrets["bot_id"]
+chat_id = st.secrets["chat_id"]
+channel_id = st.secrets["channel_id"]
 url = f"https://api.telegram.org/bot{bot_id}/sendPhoto"
 bot = telebot.TeleBot(bot_id)
-start_message = "Hello, I'm your bot and I'm starting up!"
-bot.send_message(chat_id, start_message)
-
-# Custom categories
-category_map = {
-    'beasts': ['bear', 'elephant', 'zebra', 'giraffe','dog', 'cat','cow','sheep'],
-    'birds': ['bird'],
-    'person': ['person'],
-    'vehicle': ['bicycle','car','motorcycle','bus','truck','train','boat'],
-    'signs':['street sign','stop sign'],
-    'cutting_objects':['knife','fork'],
-    'danger': ['snake', 'spider']
-}
 
-# Function to map the COCO class to its category
-def map_to_category(class_name):
-    for category, classes in category_map.items():
-        if class_name in classes:
-            return category
-    return 'other'  # Return 'other' if the class doesn't belong to any custom category
+# Helper function to send photos to Telegram
+def sendbot(img, message):
+    image_bytes = cv2.imencode(".jpg", img)[1].tobytes()
+    with io.BytesIO(image_bytes) as f:
+        f.name = "image.jpg"
+        media_photo = telebot.types.InputFile(f)
+        bot.send_photo(chat_id, media_photo)
+        data = {"chat_id": channel_id, "text": message}
+        requests.post(url, data=data, files={"photo": f})
 
 # Load the COCO class names
 classNames = []
 classFile = 'coco.names'
 with open(classFile, 'rt') as f:
     classNames = f.read().rstrip('\n').split('\n')
 
+# Load model configuration and weights
 configPath = 'ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt'
 weightsPath = 'frozen_inference_graph.pb'
-
 net = cv2.dnn_DetectionModel(weightsPath, configPath)
 net.setInputSize(320, 320)
-net.setInputScale(1.0/127.5)
+net.setInputScale(1.0 / 127.5)
 net.setInputMean((127.5, 127.5, 127.5))
 net.setInputSwapRB(True)
-print("Press backspace or ESC to close the cam")
-# Open the camera
-cap = cv2.VideoCapture(0)  # Change the argument to the camera index if you have multiple cameras
 
-while True:
-    ret, img = cap.read()  # Read a frame from the camera
+# Streamlit UI and camera handling
+st.title("Real-time Object Detection and Alerts")
+st.write("Capture an image using the camera below:")
 
+# Capture image from the browser
+image_data = st.camera_input("Camera")
 
+if image_data is not None:
+    # Convert the image from Streamlit format to OpenCV format
+    file_bytes = np.asarray(bytearray(image_data.read()), dtype=np.uint8)
+    img = cv2.imdecode(file_bytes, 1)
+
+    # Perform object detection
     classIds, confs, bbox = net.detect(img, confThreshold=0.5)
-
-    if len(classIds) != 0:
-        for classId, confidence, box in zip(classIds.flatten(), confs.flatten(), bbox):
-            class_name = classNames[classId-1]
-            category = map_to_category(class_name)
-            # Set color based on category
-            if (category == 'beasts')or(category=='danger')or(category=='cutting_objects'):
-                color = (0,0 ,255)  # red (for beasts & dangerous things)
-                if(category=='cutting_objects'):
-                    message="Alert! Sharp objects detected Detected"
-                    bot.send_message(chat_id, message)
-                else:
-                    message="Alert! Beast Detected"
-                    bot.send_message(chat_id, message)
-                image_bytes = cv2.imencode(".jpg", img)[1].tobytes()
-
-                # Wrap the byte array in a file-like object
-                with io.BytesIO(image_bytes) as f:
-                    # Set the name attribute of the file-like object
-                    f.name = "image.jpg"
-
-                    # Create an InputFile object
-                    media_photo = telebot.types.InputFile(f)
-
-                    # Send the photo to the user
-                    bot.send_photo(chat_id, media_photo)
-                    data = {
-                        "chat_id": channel_id,
-                        "text": message
-                    }
-                    cv2.imwrite("image.jpg", img)
-                    media_photo = {"photo": ("image.jpg", open("image.jpg", "rb"))}
-                    response = requests.post(url, data=data,files=media_photo)
-            elif category == 'birds':
-                color = (0, 255, 0)    #  (for birds)
-            elif category == 'person':
-                color = (0, 255, 255)    # Yellow (for persons)
-            elif (category == 'vehicle') or (category=="signs"):
-                color = (255, 0, 0)      # Blue (for vehicle & signs)
-            else:
-                color = (255, 255,255)    # White (for other objects)
-
-            cv2.rectangle(img, box, color=color, thickness=3)
-            cv2.putText(img, category, (box[0]+10, box[1]+30), cv2.FONT_HERSHEY_COMPLEX, 1, color, 2)
-
-    cv2.imshow('Object Detection', img)  # Show the image
-
-    # Wait for 'ESC' or 'Backspace" key to be pressed to end the program
-    tecla = cv2.waitKey(5) & 0xFF
-    if ((tecla == 27) or (tecla==8)):
-        break
-
-cap.release()  # Release the camera
-cv2.destroyAllWindows()
-bot.send_message(chat_id, "Bye Bye Shutting down")
-bot.polling()
+
+    # Detect and categorize objects
+    for classId, confidence, box in zip(classIds.flatten(), confs.flatten(), bbox):
+        class_name = classNames[classId - 1]
+        # Customize colors and labels based on the category
+        color = (0, 255, 0) if class_name == "person" else (0, 0, 255)  # Green for persons, red for others
+        cv2.rectangle(img, box, color=color, thickness=3)
+        cv2.putText(img, class_name, (box[0] + 10, box[1] + 30), cv2.FONT_HERSHEY_SIMPLEX, 1, color, 2)
+
+    # Display detection results in Streamlit
+    st.image(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), caption="Detected Objects", use_column_width=True)
+
+    # Send alert to Telegram if specific objects are detected
+    if "person" in [classNames[id - 1] for id in classIds.flatten()]:
+        sendbot(img, "Alert! Person Detected")