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# MIT License
# Copyright (c) 2022 Intelligent Systems Lab Org
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# File author: Shariq Farooq Bhat
import numpy as np
from torchvision.transforms import ToTensor
from PIL import Image
from zoedepth.utils.misc import get_image_from_url, colorize
import torch
from zoedepth.models.builder import build_model
from zoedepth.utils.config import get_config
from pprint import pprint
torch.hub.help("intel-isl/MiDaS", "DPT_BEiT_L_384", force_reload=True)
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
if DEVICE == "cpu":
print("WARNING: Running on CPU. This will be slow. Check your CUDA installation.")
print("*" * 20 + " Testing zoedepth " + "*" * 20)
conf = get_config("zoedepth", "infer")
print("Config:")
pprint(conf)
model = build_model(conf).to(DEVICE)
model.eval()
x = torch.rand(1, 3, 384, 512).to(DEVICE)
print("-"*20 + "Testing on a random input" + "-"*20)
with torch.no_grad():
out = model(x)
if isinstance(out, dict):
# print shapes of all outputs
for k, v in out.items():
if v is not None:
print(k, v.shape)
else:
print([o.shape for o in out if o is not None])
print("\n\n")
print("-"*20 + " Testing on an indoor scene from url " + "-"*20)
# Test img
url = "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS4W8H_Nxk_rs3Vje_zj6mglPOH7bnPhQitBH8WkqjlqQVotdtDEG37BsnGofME3_u6lDk&usqp=CAU"
img = get_image_from_url(url)
orig_size = img.size
X = ToTensor()(img)
X = X.unsqueeze(0).to(DEVICE)
print("X.shape", X.shape)
print("predicting")
with torch.no_grad():
out = model.infer(X).cpu()
# or just,
# out = model.infer_pil(img)
print("output.shape", out.shape)
pred = Image.fromarray(colorize(out))
# Stack img and pred side by side for comparison and save
pred = pred.resize(orig_size, Image.ANTIALIAS)
stacked = Image.new("RGB", (orig_size[0]*2, orig_size[1]))
stacked.paste(img, (0, 0))
stacked.paste(pred, (orig_size[0], 0))
stacked.save("pred.png")
print("saved pred.png")
model.infer_pil(img, output_type="pil").save("pred_raw.png") |