Object-Detection-with-CNN-Using-YOLOv3-

Object Detection with CNN (Using YOLOv3)" teaches the implementation of YOLOv3 for real-time object detection. Learn CNN fundamentals, YOLO architecture, and practical techniques for training models to identify objects in images and videos efficiently. Ideal for advancing skills in computer vision and deep learning.

import cv2 import numpy as np import tensorflow as tf

Load YOLOv3 model and weights

yolo_model = tf.keras.models.load_model('path/to/yolov3_model.h5')

Load the COCO class labels

labels_path = 'path/to/coco.names' with open(labels_path) as f: labels = f.read().strip().split("\n")

Load the image

image = cv2.imread('path/to/image.jpg') (H, W) = image.shape[:2]

Prepare the image for YOLOv3 model

blob = cv2.dnn.blobFromImage(image, 1/255.0, (416, 416), swapRB=True, crop=False) yolo_model.setInput(blob)

Get the bounding boxes and probabilities

layer_names = yolo_model.getLayerNames() output_layers = [layer_names[i[0] - 1] for i in yolo_model.getUnconnectedOutLayers()] layer_outputs = yolo_model.forward(output_layers)

boxes = [] confidences = [] class_ids = []

for output in layer_outputs: for detection in output: scores = detection[5:] class_id = np.argmax(scores) confidence = scores[class_id] if confidence > 0.5: box = detection[0:4] * np.array([W, H, W, H]) (centerX, centerY, width, height) = box.astype("int") x = int(centerX - (width / 2)) y = int(centerY - (height / 2)) boxes.append([x, y, int(width), int(height)]) confidences.append(float(confidence)) class_ids.append(class_id)

Apply non-maxima suppression to suppress weak, overlapping bounding boxes

idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.3)

Ensure at least one detection exists

if len(idxs) > 0: for i in idxs.flatten(): (x, y) = (boxes[i][0], boxes[i][1]) (w, h) = (boxes[i][2], boxes[i][3]) color = [int(c) for c in np.random.randint(0, 255, size=(3,))] cv2.rectangle(image, (x, y), (x + w, y + h), color, 2) text = "{}: {:.4f}".format(labels[class_ids[i]], confidences[i]) cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)

Display the output image

cv2.imshow("Image", image) cv2.waitKey(0) cv2.destroyAllWindows()