from PIL import Image
import cv2 as cv
import re


def detect_img(yolo, img_path):
    detected_robots = []

    try:
        image = Image.open(img_path)
    except:
        print('Image open Error! Try again!')
        return None
    else:
        r_image, pred = yolo.detect_image(image)
        processed_image = cv.imread(img_path)
        # r_image.show()
        # r_image.save(img_path)

        if not pred:
            return None
        ## FIXME : better list mapping
        for prediction in pred:
            is_robot_detected = re.search("robot", prediction[0])
            if is_robot_detected:
                x1 = prediction[1][0]
                x2 = prediction[2][0]
                y1 = prediction[1][1]
                y2 = prediction[2][1]
                w = abs(x1 - x2)
                h = abs(y1 - y2)
                # print(pred)
                # print(f'x1: {x1}, x2: {x2}, y1: {y1}, y2: {y2}, w: {w}, h: {h}')

                robot_img = processed_image[y1:y1 + h, x1:x1 + w]
                robot_img = cv.resize(robot_img, (128, 128), interpolation=cv.INTER_AREA)
                robot_pos = w, x1
                detected_robots.append((robot_img, robot_pos))

    return detected_robots


def get_turn_value(cords):
    img_width = 1920
    w, x = cords
    center_of_object = (x + w) / 2
    object_position = center_of_object / img_width
    return round(object_position * 100, 2)



def detect_robot(model, img_path):
    detected_robots = detect_img(model, img_path)
    return detected_robots
    # if not detected_robots:
    #     return None
    # robot_position = detected_robots[0][1]
    # turn_val = get_turn_value(robot_position)
    # return turn_val