object_detect.py

import cv2
import numpy as np


# Load YOLO
print("LOADING YOLO")
net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")

# Save all the names in the file to the list classes
classes = []
with open("coco.names", "r") as f:
    classes = [line.strip() for line in f.readlines()]

# Get layers of the network
layer_names = net.getLayerNames()

# Determine the output layer names from the YOLO model
output_layers = [i for i in range(len(layer_names)) if layer_names[i].endswith("yolo_")]

print("YOLO LOADED")

# Read and preprocess the input image
img = cv2.imread("Sample.jpg")
height, width, channels = img.shape

# Using the blob function of OpenCV to preprocess the image
blob = cv2.dnn.blobFromImage(img, 1 / 255.0, (416, 416), swapRB=True, crop=False)

# Detecting objects
net.setInput(blob)
outs = net.forward(output_layers)

# Showing information on the screen
class_ids = []
confidences = []
boxes = []

for out in outs:
    for detection in out:
        scores = detection[5:]
        class_id = np.argmax(scores)
        confidence = scores[class_id]

        if confidence > 0.5:
            # Object detected
            center_x = int(detection[0] * width)
            center_y = int(detection[1] * height)
            w = int(detection[2] * width)
            h = int(detection[3] * height)

            # Rectangle coordinates
            x = int(center_x - w / 2)
            y = int(center_y - h / 2)

            boxes.append([x, y, w, h])
            confidences.append(float(confidence))
            class_ids.append(class_id)

# Use NMS function in OpenCV to perform Non-maximum Suppression
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
colors = np.random.uniform(0, 255, size=(len(classes), 3))

for i in range(len(boxes)):
    if i in indexes:
        x, y, w, h = boxes[i]
        label = str(classes[class_ids[i]])
        color = colors[class_ids[i]]
        cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
        cv2.putText(img, label, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)

cv2.imshow("Image", img)
cv2.waitKey(0)