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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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def calculate_depth(model_type, 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/0.jpg" |
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img.save(filename, "JPEG") |
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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/0_d.jpg", "JPEG") |
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return f'Images/Input-Test/0_d.jpg' |
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def wiggle_effect(slider): |
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return [f'Images/Input-Test/0_d.jpg',f'Images/Input-Test/0_d.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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inp = [gr.inputs.Dropdown(midas_models, default="MiDaS_small", label="Depth estimation model type")] |
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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="file", label="depth_estimation") |
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btn = gr.Button("Calculate depth") |
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btn.click(fn=calculate_depth, inputs=inp, outputs=out) |
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inp = [gr.Slider(1,15, default = 2, label='StepCycles',step= 1)] |
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with gr.Row(): |
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out = [ gr.Image(type="file", label="Output_images"), |
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gr.Image(type="file", label="Output_wiggle")] |
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btn = gr.Button("Generate Wigglegram") |
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btn.click(fn=wiggle_effect, inputs=inp, outputs=out) |
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demo.launch() |
