import os
import gradio as gr
import cv2
import torch
import urllib.request
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import subprocess

def calculate_depth(model_type, img):

    if not os.path.exists('temp'):
      os.system('mkdir temp')

    filename = "Images/Input-Test/1.png"

    img.save(filename, "PNG")

    midas = torch.hub.load("intel-isl/MiDaS", model_type)
    
    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
    midas.to(device)
    midas.eval()

    midas_transforms = torch.hub.load("intel-isl/MiDaS", "transforms")

    if model_type == "DPT_Large" or model_type == "DPT_Hybrid":
        transform = midas_transforms.dpt_transform
    else:
        transform = midas_transforms.small_transform

    img = cv2.imread(filename)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

    input_batch = transform(img).to(device)

    with torch.no_grad():
        prediction = midas(input_batch)

        prediction = torch.nn.functional.interpolate(
            prediction.unsqueeze(1),
            size=img.shape[:2],
            mode="bicubic",
            align_corners=False,
        ).squeeze()

    output = prediction.cpu().numpy()

    formatted = (output * 255.0 / np.max(output)).astype('uint8')
    out_im = Image.fromarray(formatted)
    out_im.save("Images/Input-Test/1_d.png", "PNG")

    return out_im

def wiggle_effect(slider):

    dim = '256' 
    c_images = '1'
    name_output = 'out'

    subprocess.run(["python", "main.py", "--gan_type", 'WiggleGAN', "--expandGen", "4", "--expandDis", "4", "--batch_size", c_images, "--cIm", c_images,
                    "--visdom", "false", "--wiggleDepth", str(slider), "--seedLoad", '31219_110', "--gpu_mode", "false", "--imageDim", dim, "--name_wiggle", name_output
                    ])
    subprocess.run(["python",  "WiggleResults/split.py", "--dim", dim])

    return [f'WiggleResults/'+ name_output + '.jpg',f'WiggleResults/' + name_output + '_0.gif']


with gr.Blocks() as demo:
    gr.Markdown("Start typing below and then click **Run** to see the output.")
    
    
    ## Depth Estimation
    midas_models = ["DPT_Large","DPT_Hybrid","MiDaS_small"]
    inp = [gr.inputs.Dropdown(midas_models, default="MiDaS_small", label="Depth estimation model type")]
    with gr.Row():
        inp.append(gr.Image(type="pil", label="Input"))
        out = gr.Image(type="pil", label="depth_estimation")
    btn = gr.Button("Calculate depth")
    btn.click(fn=calculate_depth, inputs=inp, outputs=out)


    ## Wigglegram
    inp = [gr.Slider(1,15, default = 2, label='StepCycles',step= 1)]
    with gr.Row():
        out = [ gr.Image(type="file", label="Output_images"), #TODO change to gallery 
                gr.Image(type="file", label="Output_wiggle")]
    btn = gr.Button("Generate Wigglegram")
    btn.click(fn=wiggle_effect, inputs=inp, outputs=out)

demo.launch()