yolov3 python opencv

YoloV3 is an object detection algorithm that can be implemented using Python and OpenCV. Here are the steps to implement YoloV3 using Python and OpenCV:

1. Download the YoloV3 weights and configuration files from the official website.

2. Install OpenCV and other necessary libraries.

3. Load the YoloV3 network using OpenCV's DNN module.

4. Read the input image or video frame.

5. Preprocess the input image by resizing it to the required size and normalizing the pixel values.

6. Pass the preprocessed image through the YoloV3 network to obtain the predicted bounding boxes, class labels, and confidence scores.

7. Filter the predicted bounding boxes based on their confidence scores and non-maximum suppression.

8. Draw the filtered bounding boxes and class labels on the input image.

9. Display the output image or save it to a file.

Here's a sample code snippet for implementing YoloV3 using Python and OpenCV:

import cv2

# Load YoloV3 network
net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")

# Load classes
classes = []
with open("coco.names", "r") as f:
    classes = [line.strip() for line in f.readlines()]

# Set input and output layers
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# Read input image
img = cv2.imread("input.jpg")

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

# Pass image through YoloV3 network
net.setInput(blob)
outs = net.forward(output_layers)

# Get predicted bounding boxes, class labels, and confidence scores
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:
            center_x = int(detection[0] * img.shape[1])
            center_y = int(detection[1] * img.shape[0])
            w = int(detection[2] * img.shape[1])
            h = int(detection[3] * img.shape[0])
            x = center_x - w // 2
            y = center_y - h // 2
            class_ids.append(class_id)
            confidences.append(float(confidence))
            boxes.append([x, y, w, h])

# Apply non-maximum suppression
indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

# Draw filtered bounding boxes and class labels on input image
for i in indices:
    i = i[0]
    x, y, w, h = boxes[i]
    label = classes[class_ids[i]]
    cv2.rectangle(img, (x, y), (x + w, y + h), (0,255,0), 2)
    cv2.putText(img, label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,255,0), 2)

# Display output image
cv2.imshow("Output", img)
cv2.waitKey(0)
cv2.destroyAllWindows()