import gradio as gr
import cv2
import numpy as np
import random
from PIL import Image

def resize_with_fixed_aspect_ratio(image, new_metrics, size_metrics="height"):
    height, width = image.shape[:2]
    # print("new_metrics in functions", new_metrics)
    # print("before:", image.shape)
    if size_metrics=="height":
        aspect_ratio = width / height
        # print("aspect_ratio: ", aspect_ratio)
        new_height = new_metrics
        new_width = int(new_height * aspect_ratio)

    elif size_metrics=="width":
        aspect_ratio = height / width
        new_width = new_metrics
        new_height = int(new_width * aspect_ratio)

    new_size = (new_height, new_width)
    new_size = (new_width, new_height)
    # print("new_size: ", new_size)

    resized_image = cv2.resize(image, dsize=new_size, interpolation=cv2.INTER_CUBIC)
    # print("after:", resized_image.shape)

    return resized_image


def show_image(obj_image, bg_image):


    bg_height, bg_width = bg_image.shape[:2]    
    obj_height, obj_width = obj_image.shape[:2]

    if obj_height>obj_width:
        size_metrics="height"
        if bg_height>obj_height:
            resize_min_rate = bg_height/obj_height
            new_metrics = bg_height
        else:
            resize_min_rate = obj_height/bg_height
            new_metrics = obj_height
        

    elif obj_height<obj_width:
        size_metrics="width"
        if bg_height>obj_height:
            resize_min_rate = bg_width/obj_width
            new_metrics = bg_width
        else:
            resize_min_rate = obj_width/bg_width
            new_metrics = obj_width
    


    obj_vs_bg = random.uniform(resize_min_rate, resize_min_rate+5)
    
    obj_image = resize_with_fixed_aspect_ratio(obj_image, int(new_metrics/obj_vs_bg), size_metrics)


    height, width = obj_image.shape[:2]

    result_image = bg_image.copy()

    max_x = bg_width-width
    max_y = bg_height-height

    try:
        move_x = random.randint(0, max_x)
        move_y = random.randint(0, max_y)
        result_image[move_y:move_y+height, move_x:move_x+width, :] = obj_image
    except:
        print("bg_width.shape: ", bg_image.shape)
        print("obj_image.shape: ", obj_image.shape)
        print("max_y: ", max_y)
        print("max_x: ", max_x)
        print("obj_vs_bg", obj_vs_bg)
        print("resize_min_rate", resize_min_rate)


    result_image = np.where(result_image>0, result_image, bg_image)


    return result_image



image_input_obj = gr.Image(type="numpy")
image_input_bg = gr.Image(type="numpy")
image_output = gr.Image(type="numpy")

iface = gr.Interface(
    fn=show_image, 
    inputs=[
        image_input_obj,
        image_input_bg
        ],
    outputs=image_output
    )

iface.launch()