Object Detection with CNN (Using YOLOv3).txt

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()