import cv2
import os
import numpy as np
from segment_anything import sam_model_registry, SamPredictor

input_dir = './input'
output_dir = './output'
crop_mode = True  # 是否裁剪到最小范围
# alpha_channel 是否保留透明通道
os.makedirs(output_dir, exist_ok=True)
image_files = [f for f in os.listdir(input_dir) if f.lower().endswith(
    ('.png', '.jpg', '.jpeg', '.JPG', '.JPEG', '.PNG'))]

sam = sam_model_registry["default"](checkpoint="models/sam_vit_h_4b8939.pth")
_ = sam.to(device="mps")
predictor = SamPredictor(sam)


def mouse_click(event, x, y, flags, param):
    global input_point, input_label, input_stop
    if not input_stop:
        if event == cv2.EVENT_LBUTTONDOWN:
            input_point.append([x, y])
            input_label.append(1)
        elif event == cv2.EVENT_RBUTTONDOWN:
            input_point.append([x, y])
            input_label.append(0)
    else:
        if event == cv2.EVENT_LBUTTONDOWN or event == cv2.EVENT_RBUTTONDOWN:
            print('此时不能添加点,按w退出mask选择模式')


def apply_mask(image, mask, alpha_channel=True):
    if alpha_channel:
        alpha = np.zeros_like(image[..., 0])
        alpha[mask == 1] = 255
        image = cv2.merge((image[..., 0], image[..., 1], image[..., 2], alpha))
    else:
        image = np.where(mask[..., None] == 1, image, 0)
    return image


def apply_color_mask(image, mask, color, color_dark=0.5):
    for c in range(3):
        image[:, :, c] = np.where(
            mask == 1, image[:, :, c] * (1 - color_dark) + color_dark * color[c], image[:, :, c])
    return image


def get_next_filename(base_path, filename):
    name, ext = os.path.splitext(filename)
    for i in range(1, 101):
        new_name = f"{name}_{i}{ext}"
        if not os.path.exists(os.path.join(base_path, new_name)):
            return new_name
    return None


def save_masked_image(image, mask, output_dir, filename, crop_mode_):
    if crop_mode_:
        y, x = np.where(mask)
        y_min, y_max, x_min, x_max = y.min(), y.max(), x.min(), x.max()
        cropped_mask = mask[y_min:y_max+1, x_min:x_max+1]
        cropped_image = image[y_min:y_max+1, x_min:x_max+1]
        masked_image = apply_mask(cropped_image, cropped_mask)
    else:
        masked_image = apply_mask(image, mask)
    filename = filename[:filename.rfind('.')]+'.png'
    new_filename = get_next_filename(output_dir, filename)

    if new_filename:
        if masked_image.shape[-1] == 4:
            cv2.imwrite(os.path.join(output_dir, new_filename),
                        masked_image, [cv2.IMWRITE_PNG_COMPRESSION, 9])
        else:
            cv2.imwrite(os.path.join(output_dir, new_filename), masked_image)
        print(f"Saved as {new_filename}")
    else:
        print("Could not save the image. Too many variations exist.")


current_index = 0

cv2.namedWindow("image")
cv2.setMouseCallback("image", mouse_click)
input_point = []
input_label = []
input_stop = False
while True:
    filename = image_files[current_index]
    image_orign = cv2.imread(os.path.join(input_dir, filename))
    image_crop = image_orign.copy()
    image = cv2.cvtColor(image_orign.copy(), cv2.COLOR_BGR2RGB)
    selected_mask = None
    logit_input = None
    while True:
        # print(input_point)
        input_stop = False
        image_display = image_orign.copy()
        display_info = f'{filename} | click | w(predict) | s(save) | d(next img) | a(pre img) | space(clear all) | q(clear pre) '
        cv2.putText(image_display, display_info, (10, 30),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2, cv2.LINE_AA)
        for point, label in zip(input_point, input_label):
            color = (0, 255, 0) if label == 1 else (0, 0, 255)
            cv2.circle(image_display, tuple(point), 5, color, -1)
        if selected_mask is not None:
            color = tuple(np.random.randint(0, 256, 3).tolist())
            selected_image = apply_color_mask(
                image_display, selected_mask, color)

        cv2.imshow("image", image_display)
        key = cv2.waitKey(1)

        if key == ord(" "):
            input_point = []
            input_label = []
            selected_mask = None
            logit_input = None
        elif key == ord("w"):
            input_stop = True
            if len(input_point) > 0 and len(input_label) > 0:

                predictor.set_image(image)
                input_point_np = np.array(input_point)
                input_label_np = np.array(input_label)

                masks, scores, logits = predictor.predict(
                    point_coords=input_point_np,
                    point_labels=input_label_np,
                    mask_input=logit_input[None, :,
                                           :] if logit_input is not None else None,
                    multimask_output=True,
                )

                mask_idx = 0
                num_masks = len(masks)
                while (1):
                    color = tuple(np.random.randint(0, 256, 3).tolist())
                    image_select = image_orign.copy()
                    selected_mask = masks[mask_idx]
                    selected_image = apply_color_mask(
                        image_select, selected_mask, color)
                    # mask_info = f'Total: {num_masks} | Current: {mask_idx} | Score: {scores[mask_idx]:.2f} {display_info}'
                    mask_info = f'{display_info}'
                    cv2.putText(selected_image, mask_info, (10, 30),
                                cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2, cv2.LINE_AA)

                    cv2.imshow("image", selected_image)

                    key = cv2.waitKey(10)
                    if key == ord('q') and len(input_point) > 0:
                        input_point.pop(-1)
                        input_label.pop(-1)
                    elif key == ord('s'):
                        save_masked_image(
                            image_crop, selected_mask, output_dir, filename, crop_mode_=crop_mode)
                    elif key == ord('a'):
                        if mask_idx > 0:
                            mask_idx -= 1
                        else:
                            mask_idx = num_masks-1
                    elif key == ord('d'):
                        if mask_idx < num_masks-1:
                            mask_idx += 1
                        else:
                            mask_idx = 0
                    elif key == ord('w'):
                        break
                    elif key == ord(" "):
                        input_point = []
                        input_label = []
                        selected_mask = None
                        logit_input = None
                        break
                logit_input = logits[mask_idx, :, :]
                print('max score:', np.argmax(scores), ' select:', mask_idx)

        elif key == ord('a'):
            current_index = max(0, current_index - 1)
            input_point = []
            input_label = []
            break
        elif key == ord('d'):
            current_index = min(len(image_files) - 1, current_index + 1)
            input_point = []
            input_label = []
            break
        elif key == 27:
            break
        elif key == ord('q') and len(input_point) > 0:
            input_point.pop(-1)
            input_label.pop(-1)
        elif key == ord('s') and selected_mask is not None:
            save_masked_image(image_crop, selected_mask,
                              output_dir, filename, crop_mode_=crop_mode)

    if key == 27:
        break
