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import os |
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import gradio as gr |
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import cv2 |
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import torch |
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import urllib.request |
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import numpy as np |
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import matplotlib.pyplot as plt |
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from PIL import Image |
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import subprocess |
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def calculate_depth(model_type, gan_type, dim, slider, img): |
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if not os.path.exists('temp'): |
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os.system('mkdir temp') |
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filename = "Images/Input-Test/1.png" |
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img.save(filename, "PNG") |
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midas = torch.hub.load("intel-isl/MiDaS", model_type) |
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device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") |
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midas.to(device) |
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midas.eval() |
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midas_transforms = torch.hub.load("intel-isl/MiDaS", "transforms") |
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if model_type == "DPT_Large" or model_type == "DPT_Hybrid": |
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transform = midas_transforms.dpt_transform |
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else: |
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transform = midas_transforms.small_transform |
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img = cv2.imread(filename) |
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) |
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input_batch = transform(img).to(device) |
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with torch.no_grad(): |
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prediction = midas(input_batch) |
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prediction = torch.nn.functional.interpolate( |
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prediction.unsqueeze(1), |
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size=img.shape[:2], |
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mode="bicubic", |
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align_corners=False, |
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).squeeze() |
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output = prediction.cpu().numpy() |
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formatted = (output * 255.0 / np.max(output)).astype('uint8') |
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out_im = Image.fromarray(formatted) |
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out_im.save("Images/Input-Test/1_d.png", "PNG") |
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c_images = '1' |
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name_output = 'out' |
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dict_saved_gans = {'Cycle': '74962_110', 'Cycle(half)': '66942_110','noCycle': '31219_110', 'noCycle-noCr': '92332_110', 'noCycle-noCr-noL1': '82122_110', 'OnlyGen': '70944_110' } |
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subprocess.run(["python", "main.py", "--gan_type", 'WiggleGAN', "--expandGen", "4", "--expandDis", "4", "--batch_size", c_images, "--cIm", c_images, |
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"--visdom", "false", "--wiggleDepth", str(slider), "--seedLoad", dict_saved_gans[gan_type], "--gpu_mode", "false", "--imageDim", dim, "--name_wiggle", name_output |
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]) |
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subprocess.run(["python", "WiggleResults/split.py", "--dim", dim]) |
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path_video = os.path.join(os.path.dirname(__file__), 'WiggleResults' , name_output + '_0.mp4') |
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print(path_video) |
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return [out_im,f'WiggleResults/' + name_output + '_0.gif', path_video, f'WiggleResults/'+ name_output + '.jpg'] |
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with gr.Blocks() as demo: |
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gr.Markdown("Start typing below and then click **Run** to see the output.") |
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midas_models = ["DPT_Large","DPT_Hybrid","MiDaS_small"] |
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gan_models = ["Cycle","Cycle(half)","noCycle","noCycle-noCr","noCycle-noCr-noL1","OnlyGen"] |
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dim = ['256','512','1024'] |
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with gr.Row(): |
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inp = [gr.inputs.Dropdown(midas_models, default="MiDaS_small", label="Depth estimation model type")] |
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inp.append(gr.inputs.Dropdown(gan_models, default="Cycle", label="Different GAN trainings")) |
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inp.append(gr.inputs.Dropdown(dim, default="256", label="Wiggle dimension result")) |
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inp.append(gr.Slider(1,15, default = 2, label='StepCycles',step= 1)) |
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with gr.Row(): |
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inp.append(gr.Image(type="pil", label="Input")) |
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out = [gr.Image(type="pil", label="depth_estimation")] |
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with gr.Row(): |
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out.append(gr.Image(type="file", label="Output_wiggle_gif")) |
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out.append(gr.Video(label="Output_wiggle_video")) |
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out.append(gr.Image(type="file", label="Output_images")) |
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btn = gr.Button("Calculate depth + Wiggle") |
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btn.click(fn=calculate_depth, inputs=inp, outputs=out) |
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demo.launch() |
