import cv2
import numpy as np


def non_max_suppression(boxes, overlapThresh):
    if len(boxes) == 0:
        return []

    # Convert to float
    boxes = np.array(boxes, dtype="float")

    # Initialize the list of picked indexes
    pick = []

    # Grab the coordinates of the bounding boxes
    x1 = boxes[:, 0]
    y1 = boxes[:, 1]
    x2 = boxes[:, 2]
    y2 = boxes[:, 3]

    # Compute the area of the bounding boxes and sort by bottom-right y-coordinate
    area = (x2 - x1 + 1) * (y2 - y1 + 1)
    idxs = np.argsort(y2)

    # Keep looping while some indexes still remain in the indexes list
    while len(idxs) > 0:
        # Grab the last index in the indexes list and add the index value to the list of picked indexes
        last = len(idxs) - 1
        i = idxs[last]
        pick.append(i)

        # Find the largest (x, y) coordinates for the start of the bounding box and the smallest (x, y)
        # coordinates for the end of the bounding box
        xx1 = np.maximum(x1[i], x1[idxs[:last]])
        yy1 = np.maximum(y1[i], y1[idxs[:last]])
        xx2 = np.minimum(x2[i], x2[idxs[:last]])
        yy2 = np.minimum(y2[i], y2[idxs[:last]])

        # Compute the width and height of the bounding box
        w = np.maximum(0, xx2 - xx1 + 1)
        h = np.maximum(0, yy2 - yy1 + 1)

        # Compute the ratio of overlap
        overlap = (w * h) / area[idxs[:last]]

        # Delete all indexes from the index list that have overlap greater than the threshold
        idxs = np.delete(
            idxs, np.concatenate(([last], np.where(overlap > overlapThresh)[0]))
        )

    # Return only the bounding boxes that were picked
    return boxes[pick].astype("int")


# Load images
target_image = cv2.imread("test/20240108_224218.jpg")
template_image = cv2.imread("templates/dungeon/attack.png")
h, w = template_image.shape[:-1]

# Template matching
result = cv2.matchTemplate(target_image, template_image, cv2.TM_CCOEFF_NORMED)

# Define a threshold
threshold = 0.9  # Adjust this threshold based on your requirements

# Finding all locations where match exceeds threshold
locations = np.where(result >= threshold)
locations = list(zip(*locations[::-1]))

# Create list of rectangles
rectangles = [(*loc, loc[0] + w, loc[1] + h) for loc in locations]

# Apply non-maximum suppression to remove overlaps
rectangles = non_max_suppression(rectangles, 0.3)

# Draw rectangles around matches
for startX, startY, endX, endY in rectangles:
    cv2.rectangle(target_image, (startX, startY), (endX, endY), (0, 255, 0), 2)

    # Print the coordinates of the rectangle
    centerX = round(startX + (endX - startX) / 2)
    centerY = round(startY + (endY - startY) / 2)
    print(f"center coordinates: {centerX}/{centerY}")

# Display the result
cv2.imshow("Matched Results", target_image)
cv2.waitKey(0)
cv2.destroyAllWindows()