import cv2
import numpy as np
# from shared_queue import image_queue


def read_image(file_bytes):
    np_img = np.frombuffer(file_bytes, np.uint8)
    img = cv2.imdecode(np_img, cv2.IMREAD_COLOR)
    return img

def combine_mask(image, mask, label2color={1: (255, 255, 0), 2: (0, 255, 255)}, alpha=0.1):
    image = to_color(image)
    image = cv2.resize(image, mask.shape)
    mask_image = np.zeros_like(image)
    for label, color in label2color.items():
        mask_image[mask == label] = color

    mask_image = cv2.addWeighted(image, 1 - alpha, mask_image, alpha, 0)
    return mask_image


def to_color(image):
    if len(image.shape) == 3 and image.shape[-1] == 3:
        return image
    return cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)


def to_gray(image):
    if len(image.shape) == 3 and image.shape[-1] == 3:
        return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    return image

def prob_process(prob_list):
    prob = prob_list[0]
    max_idx = np.argmax(prob)
    min_idx = np.argmin(prob)
    prob[max_idx] += prob[min_idx]
    prob[min_idx] = 0
    
    if max_idx == 0:
        return 1 - prob[max_idx]
    elif max_idx ==1:
        return prob[max_idx]
    else:
        return 1 + prob[max_idx]

def combine_prob_jsw(prob, jsw_m, jsw_mm):
    processed_prob = prob_process(prob)
    temp = np.array([processed_prob, jsw_m, jsw_mm])
    if temp.any():
        temp = np.hstack(temp)
    else:
        temp = temp.flatten()
    
    return temp

def scale_coordinates(links, original_size, mask_size):
    scale_factor = original_size / mask_size
    scaled_links = [(int(x * scale_factor), int(y * scale_factor)) for x, y in links]
    return scaled_links

def get_annotations(probabilites):
    OSTEOPYTE_LEVELS = {
        0: "Definite osteophytes",
        1: "No osteophytes",
        2: "Possible osteophytes",
    }

    JNS_LEVELS = {
        0: "Definite JSN",
        1: "Mild JSN",
        2: "No JSN",
        3: "Severe JSN",
    }

    return {
        "osteophyte": OSTEOPYTE_LEVELS[np.argmax(probabilites[5:8])],
        "jsn": JNS_LEVELS[np.argmax(probabilites[8:12])],
    }