Projectt.jpynb

# Mount Google Drive
from google.colab import drive
drive.mount('/content/drive')

# Load the necessary libraries
import cv2
import numpy as np
import os

# Define paths to the model files
config_file = '/content/drive/My Drive/ObjectDetection/ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt'
frozen_model = '/content/drive/My Drive/ObjectDetection/frozen_inference_graph.pb'
class_labels_file = '/content/drive/My Drive/ObjectDetection/coco.names'

# Check if the files exist
if not os.path.isfile(frozen_model):
    print("Frozen model file does not exist. Please check the path.")
if not os.path.isfile(config_file):
    print("Config file does not exist. Please check the path.")
if not os.path.isfile(class_labels_file):
    print("Class labels file does not exist. Please check the path.")

# Load the class labels
with open(class_labels_file, 'r') as f:
    class_labels = f.read().strip().split('\n')

# Load the DNN model
net = cv2.dnn.readNetFromTensorflow(frozen_model, config_file)

# Function to perform object detection on an image
def detect_objects(image):
    height, width, _ = image.shape

    # Create a blob from the image
    blob = cv2.dnn.blobFromImage(image, 1.0 / 127.5, (320, 320), (127.5, 127.5, 127.5), swapRB=True, crop=False)

    # Set the input to the network
    net.setInput(blob)

    # Forward pass to get the detections
    detections = net.forward()

    for detection in detections[0, 0]:
        confidence = detection[2]
        if confidence > 0.5:  # Confidence threshold
            class_index = int(detection[1])
            box = detection[3:7] * np.array([width, height, width, height])
            (startX, startY, endX, endY) = box.astype("int")

            # Draw bounding box and label
            label = f"{class_labels[class_index]}: {confidence:.2f}"
            cv2.rectangle(image, (startX, startY), (endX, endY), (0, 255, 0), 2)
            cv2.putText(image, label, (startX, startY - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

    return image

# For image detection
image_path = '/content/drive/My Drive/ObjectDetection/kite.jpeg'  # Replace with your image path
image = cv2.imread(image_path)  # Load the image
detected_image = detect_objects(image)  # Perform detection

# Display the detected image
import matplotlib.pyplot as plt
plt.imshow(cv2.cvtColor(detected_image, cv2.COLOR_BGR2RGB))
plt.axis('off')
plt.show()