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import os
import gradio as gr
import cv2
import torch
import urllib.request
import matplotlib.pyplot as plt
from PIL import Image
def update(slider, img):
if not os.path.exists('temp'):
os.system('mkdir temp')
filename = "temp/image.jpg"
img.save(filename, "JPEG")
model_type = "DPT_Hybrid"
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(output)
#out_im.convert('RGB').save("temp/image_depth.jpeg", "JPEG")
#cv2.imwrite("temp/image_depth.jpeg", output)
plt.imsave('test.png', output)
return f'temp/image_depth.jpeg'
with gr.Blocks() as demo:
gr.Markdown("Start typing below and then click **Run** to see the output.")
inp = [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="file", label="Output")
btn = gr.Button("Run")
btn.click(fn=update, inputs=inp, outputs=out)
demo.launch() |