Update app.py

app.py

@@ -1,29 +1,54 @@
-import tensorflow as tf
-from tensorflow import keras
-from tensorflow.keras.preprocessing import image_dataset_from_directory
-from tensorflow.keras import models, layers
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForImageClassification
-from PIL import Image
-import requests
-import torch
-
-# Load model from Hugging Face model hub
-model_name = "NKASG/GNN"  # Replace with your model's name on Hugging Face
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForImageClassification.from_pretrained(model_name)
-
-# Define function for image preprocessing and prediction
-def process_image(image):
-    
-    img_4d=img.reshape(-1,256,256,3)
-    prediction=model.predict(img_4d)[0]
-  # return {class_names[i]: float(prediction[i]) for i in range(3)}
-
-    image = gr.inputs.Image(shape=(256,256))
-    label = gr.outputs.Label(num_top_classes=1)
-
-    gr.Interface(fn=predict_image, inputs=image, outputs=label,interpretation='default').launch()
+from ultralytics import YOLO
+import cv2
+import cvzone
+import math
+import imutils
+from imutils.video import VideoStream
+
+cap = cv2.VideoCapture(0)#'rtsp://admin:passw0rd@192.168.1.64:554/Streaming/Channels/1')
+cap.set(3, 640)
+cap.set(4, 128)
+
+#cap = cv2.VideoCapture("C:/Users/Moh/PycharmProjects/pythonProject/video/GVD.mp4")
+
+model = YOLO("best20.pt")
+
+classNames = ['person', 'Gun' ]
+
+while True:
+    success, frame = cap.read()
+
+    result = model(frame, stream=True)
+
+    for r in result:
+        boxes = r.boxes
+        for box in boxes:
+            # bounding box
+            x1, y1, x2, y2 = box.xyxy[0]
+            x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+            w, h = x2 - x1, y2 - y1
+
+            #confidence
+
+            conf = math.ceil((box.conf[0] * 100)) / 100
+
+            #className
+
+            cls = int(box.cls[0])
+            currentClass = classNames[cls]
+
+            if currentClass == "Gun"  and conf > 0.4:
+                print(conf)
+
+                cvzone.cornerRect(frame, (x1, y1, w, h), l=9, rt=5, colorR=(255, 0, 0))
+                cvzone.putTextRect(frame, f'{conf}{currentClass}', (max(0, x1), max(0, y1)))
+                print(conf)
+
+    frame = imutils.resize(frame, width=1200)
+    cv2.imshow('AsimCodeCam', frame)
+    key = cv2.waitKey(1) & 0xFF
+    if key == ord('q'):
+        break
+
+cv2.destroyAllWindows()
+video_stream.stop()