Muti-template Matching

การพัฒนาโปรแกรมตรวจจับวัตถุในภาพด้วย Muti-template Matching

👉ดาวน์โหลดภาพ

import cv2

import numpy as np

def non_maximum_suppression(boxes, overlapThresh=0.5):

    if len(boxes) == 0:

        return []

    boxes = np.array(boxes)

    x1, y1, x2, y2 = boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:, 3]

    areas = (x2 - x1 + 1) * (y2 - y1 + 1)

    indices = np.argsort(y2)  # Sort by bottom-right y-coordinate

    pick = []

    while len(indices) > 0:

        last = len(indices) - 1

        i = indices[last]

        pick.append(i)

        xx1 = np.maximum(x1[i], x1[indices[:last]])

        yy1 = np.maximum(y1[i], y1[indices[:last]])

        xx2 = np.minimum(x2[i], x2[indices[:last]])

        yy2 = np.minimum(y2[i], y2[indices[:last]])

        w = np.maximum(0, xx2 - xx1 + 1)

        h = np.maximum(0, yy2 - yy1 + 1)

        overlap = (w * h) / areas[indices[:last]]

        indices = indices[np.where(overlap <= overlapThresh)[0]]

    return boxes[pick]

def multi_template_matching(image_path, template_paths, threshold=0.4):

    # Load the main image

    image = cv2.imread(image_path, cv2.IMREAD_COLOR)

    if image is None:

        print("Error: Could not load image.")

        return

    class_counts = {}

    for template_path in template_paths:

        # Load template

        template = cv2.imread(template_path, cv2.IMREAD_COLOR)

        if template is None:

            print(f"Error: Could not load template {template_path}.")

            continue

        # Get template dimensions

        w, h = template.shape[1], template.shape[0]

        # Perform template matching

        result = cv2.matchTemplate(image, template, cv2.TM_CCOEFF_NORMED)

        # Find locations where matching score is above the threshold

        locations = np.where(result >= threshold)

        boxes = [(pt[0], pt[1], pt[0] + w, pt[1] + h) for pt in zip(*locations[::-1])]

        # Apply Non-Maximum Suppression (NMS) to remove duplicates

        filtered_boxes = non_maximum_suppression(boxes)

        count = len(filtered_boxes)  # Corrected count

        class_counts[template_path] = count

        # Draw rectangles around detected objects

        for (x1, y1, x2, y2) in filtered_boxes:

            cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)

    # Display counts on image

    y_offset = 20

    for class_name, count in class_counts.items():

        text = f"{class_name}: {count}"

        cv2.putText(image, text, (10, y_offset), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 255), 2)

        y_offset += 30

        # Print counts

    print("Object Counts:")

    for class_name, count in class_counts.items():

        print(f"{class_name}: {count}")

    # Show the result

    cv2.imshow("Detected Objects", image)

    cv2.waitKey(0)

    cv2.destroyAllWindows()

# Example usage

multi_template_matching(r"C:\Users\user\Desktop\p0.jpg", [r"C:\Users\user\Desktop\p2.jpg", r"C:\Users\user\Desktop\p3.jpg"])

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