- This is the "final" version to do a diff between 2 depth map images:
#! /usr/bin/env python3
from pathlib import Path
import torch
from torch import nn
import blobconverter
import onnx
from onnxsim import simplify
import sys
# Define the model
class DiffImgs(nn.Module):
def forward(self, img1, img2):
# We will be inputting UINT16 but interprets as UINT8
# So we need to adjust to account of the 8 bit shift
img1DepthFP16 = 256.0 * img1[:,:,:,1::2] + img1[:,:,:,::2]
img2DepthFP16 = 256.0 * img2[:,:,:,1::2] + img2[:,:,:,::2]
# Create binary masks for each image
# A pixel in the mask is 1 if the corresponding pixel in the image is 0, otherwise it's 0
img1Mask = (img1DepthFP16 == 0)
img2Mask = (img2DepthFP16 == 0)
# If a pixel is 0 in either image, set the corresponding pixel in both images to 0
img1DepthFP16 = img1DepthFP16 * (~img1Mask & ~img2Mask)
img2DepthFP16 = img2DepthFP16 * (~img1Mask & ~img2Mask)
# Compute the difference between the two images
diff = torch.sub(img1DepthFP16, img2DepthFP16)
# Square the difference
# square_diff = torch.square(diff)
# # Compute the square root of the square difference
# sqrt_diff = torch.sqrt(square_diff)
# sqrt_diff[sqrt_diff < 1500] = 0
return diff
# Instantiate the model
model = DiffImgs()
# Create dummy input for the ONNX export
input1 = torch.randn(1, 1, 320, 544 * 2, dtype=torch.float16)
input2 = torch.randn(1, 1, 320, 544 * 2, dtype=torch.float16)
onnx_file = "diff_images.onnx"
# Export the model
torch.onnx.export(model, # model being run
(input1, input2), # model input (or a tuple for multiple inputs)
onnx_file, # where to save the model (can be a file or file-like object)
opset_version=12, # the ONNX version to export the model to
do_constant_folding=True, # whether to execute constant folding for optimization
input_names = ['input1', 'input2'], # the model's input names
output_names = ['output'])
# Simplify the model
onnx_model = onnx.load(onnx_file)
onnx_simplified, check = simplify(onnx_file)
onnx.save(onnx_simplified, "diff_images_simplified.onnx")
# Use blobconverter to convert onnx->IR->blob
blobconverter.from_onnx(
model="diff_images_simplified.onnx",
data_type="FP16",
shaves=4,
use_cache=False,
output_dir="../",
optimizer_params=[],
compile_params=['-ip U8'],
)Important to note! This does not take in dynamic image sizes. It must be a certain size. For some reason dynamic dimensions are not supported. So these 2 lines:
# Create dummy input for the ONNX export
input1 = torch.randn(1, 1, 320, 544 * 2, dtype=torch.float16)
input2 = torch.randn(1, 1, 320, 544 * 2, dtype=torch.float16)
Define what size of depth images are coming in. change 320 (height) and 544 (width) to your actual depth image size.
- These lines are what changes the depth input from U8 (1 byte) to U16 (2 bytes):
depthFP16 = 256.0 * depth[:,:,:,1::2] + depth[:,:,:,::2]
The reason is because the depth image comes into the model at U16. We then convert it to U8 when it enters the model. We tell the nn to do that by this compile command: compile_params=['-ip U8']
So the data comes in twice as big because it changes from U16 to U8. It needs twice as many bytes to represent the image. What that operation does is a little trick to turn the U8 data into FP16 data (which is required by the NN). So what that does is it unconverts the input data back from U8 to U16 (in this case FP16).
What is your use case, do you also want to diff a "control depth" from new depth or something else.
