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, gan_type, dim, slider, 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") c_images = '1' name_output = 'out' dict_saved_gans = {'Cycle': '74962_110', 'Cycle(half)': '66942','noCycle': '31219_110', 'noCycle-noCr': '92332_110', 'noCycle-noCr-noL1': '82122_110', 'OnlyGen': '70944_110' } 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", dict_saved_gans[gan_type], "--gpu_mode", "false", "--imageDim", dim, "--name_wiggle", name_output ]) subprocess.run(["python", "WiggleResults/split.py", "--dim", dim]) path_video = os.path.join(os.path.dirname(__file__), 'WiggleResults' , name_output + '_0.mp4') print(path_video) return [img,f'WiggleResults/' + name_output + '_0.gif', path_video, f'WiggleResults/'+ name_output + '.jpg'] 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"] gan_models = ["Cycle","Cycle(half)","noCycle","noCycle-noCr","noCycle-noCr-noL1","OnlyGen"] dim = ['256','512','1024'] with gr.Row(): inp = [gr.inputs.Dropdown(midas_models, default="MiDaS_small", label="Depth estimation model type")] inp.append(gr.inputs.Dropdown(gan_models, default="Cycle", label="Different GAN trainings")) inp.append(gr.inputs.Dropdown(dim, default="256", label="Wiggle dimension result")) inp.append(gr.Slider(1,15, default = 2, label='StepCycles',step= 1)) with gr.Row(): inp.append(gr.Image(type="pil", label="Input")) out = [gr.Image(type="pil", label="depth_estimation")] with gr.Row(): out.append(gr.Image(type="file", label="Output_wiggle_gif")) out.append(gr.Video(label="Output_wiggle_video")) out.append(gr.Image(type="file", label="Output_images")) btn = gr.Button("Calculate depth + Wiggle") btn.click(fn=calculate_depth, inputs=inp, outputs=out) demo.launch()