6. Advanced Deep Learning Architectures

AIM:  Implement Object Detection Using YOLO.


PROGRAM:


import cv2
import numpy as np

# Load YOLO model
net = cv2.dnn.readNet("/content/drive/MyDrive/6-3/yolov3.weights", "/content/drive/MyDrive/6-3/yolov3.cfg")
classes = []
with open("/content/drive/MyDrive/6-3/classes.txt", "r") as f:
    classes = [line.strip() for line in f.readlines()]

#layer_names = net.getLayerNames()
#output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

layer_names = net.getLayerNames()

# Get the names of the output layers (unconnected layers)
output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]

# Load image
image = cv2.imread("/content/drive/MyDrive/6/test/road1.jpg")
height, width, channels = image.shape

# Preprocess image for YOLO model
blob = cv2.dnn.blobFromImage(image, scalefactor=0.00392, size=(416, 416), mean=(0, 0, 0), swapRB=True, crop=False)

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

# Get output layer predictions
outs = net.forward(output_layers)

# Initialize lists to store detected objects and their details
class_ids = []
confidences = []
boxes = []

# Process each output layer
for out in outs:
    for detection in out:
        scores = detection[5:]
        class_id = np.argmax(scores)
        confidence = scores[class_id]

        if confidence > 0.5:  # You can adjust this threshold as needed
            # 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)

# Non-maximum suppression to eliminate redundant overlapping boxes
indexes = cv2.dnn.NMSBoxes(boxes, confidences, score_threshold=0.5, nms_threshold=0.4)

# Draw bounding boxes on the image
for i in range(len(boxes)):
    if i in indexes:
        x, y, w, h = boxes[i]
        label = classes[class_ids[i]]
        confidence = confidences[i]

        # Draw rectangle and label
        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
        cv2.putText(image, f"{label} {confidence:.2f}", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

# Display the result
'''cv2.imshow("Object Detection", image)
cv2.waitKey(0)
cv2.destroyAllWindows()
display(image)'''
import matplotlib.pyplot as plt

plt.imshow(image)
plt.axis('off')  # Turn off axis labels
plt.show()


link to model, classes

https://drive.google.com/drive/folders/18KGfkGRmmznAjTBBRSrew6WfEUYdiuF6?usp=sharing

link to test images

https://drive.google.com/drive/folders/1ZwjomDmyZtsKD8Wi3IlwTUl7EkmOTF-M?usp=sharing

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