app.py

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

def calculate_depth(model_type, img):

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

    filename = "Images/Input-Test/0.jpg"

    img.save(filename, "JPEG")

    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()

    out_im = Image.fromarray(np.uint8(output*255))
    #formatted = (output * 255 / np.max(output)).astype('uint8')
    #out_im = Image.fromarray(formatted)
    out_im.save("Images/Input-Test/0_d.jpg", "JPEG")

    return f'Images/Input-Test/0_d.jpg'

def wiggle_effect(slider):

    return [f'temp/image_depth.jpeg',f'temp/image_depth.jpeg']


#pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116

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="file", 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()