1#include "blace_ai.h"
 2#include "videoflow_v1_default_v1_ALL_export_version_v25.h"
 3#include <opencv2/opencv.hpp>
 4
 5using namespace blace;
 6
 7int main() {
 8  ::workload_management::BlaceWorld blace;
 9  auto exe_path = util::getPathToExe();
10  auto frame_0_op = CONSTRUCT_OP(
11      ops::FromImageFileOp((exe_path / "videoflow_frame_0.png").string()));
12  auto frame_1_op = CONSTRUCT_OP(
13      ops::FromImageFileOp((exe_path / "videoflow_frame_1.png").string()));
14  auto frame_2_op = CONSTRUCT_OP(
15      ops::FromImageFileOp((exe_path / "videoflow_frame_2.png").string()));
16
17  // interpolate to half size to save memory
18  frame_0_op = CONSTRUCT_OP(ops::Interpolate2DOp(
19      frame_0_op, 540, 960, ml_core::BILINEAR, false, true));
20  frame_1_op = CONSTRUCT_OP(ops::Interpolate2DOp(
21      frame_1_op, 540, 960, ml_core::BILINEAR, false, true));
22  frame_2_op = CONSTRUCT_OP(ops::Interpolate2DOp(
23      frame_2_op, 540, 960, ml_core::BILINEAR, false, true));
24
25  // construct model inference arguments
26  ml_core::InferenceArgsCollection infer_args;
27  infer_args.inference_args.backends = {
28      ml_core::TORCHSCRIPT_CUDA_FP16, ml_core::TORCHSCRIPT_MPS_FP16,
29      ml_core::TORCHSCRIPT_CUDA_FP32, ml_core::TORCHSCRIPT_MPS_FP32,
30      ml_core::ONNX_DML_FP32,         ml_core::TORCHSCRIPT_CPU_FP32};
31
32  // construct inference operation. the model returns two values, flow 1->0 and
33  // flow 1->2
34  auto flow_1_to_0 = videoflow_v1_default_v1_ALL_export_version_v25_run(
35      frame_0_op, frame_1_op, frame_2_op, 0, infer_args,
36      util::getPathToExe().string());
37  auto flow_1_to_2 = videoflow_v1_default_v1_ALL_export_version_v25_run(
38      frame_0_op, frame_1_op, frame_2_op, 1, infer_args,
39      util::getPathToExe().string());
40
41  // normalize optical flow to zero-one range for plotting. The model returns
42  // relative offsets in -1 to 1 pixel space, so the raw values are to small to
43  // plot
44  flow_1_to_0 = CONSTRUCT_OP(ops::NormalizeToZeroOneOP(flow_1_to_0));
45  flow_1_to_0 = CONSTRUCT_OP(ops::ToColorOp(flow_1_to_0, ml_core::RGB));
46
47  flow_1_to_2 = CONSTRUCT_OP(ops::NormalizeToZeroOneOP(flow_1_to_2));
48  flow_1_to_2 = CONSTRUCT_OP(ops::ToColorOp(flow_1_to_2, ml_core::RGB));
49
50  // construct evaluator and evaluate to opencv mat
51  computation_graph::GraphEvaluator evaluator_0(flow_1_to_0);
52  auto [return_code_0, flow_1_to_0_cv] = evaluator_0.evaluateToCVMat();
53  computation_graph::GraphEvaluator evaluator_1(flow_1_to_2);
54  auto [return_code_1, flow_1_to_2_cv] = evaluator_1.evaluateToCVMat();
55
56  // multipy for for plotting
57  flow_1_to_0_cv *= 255;
58  flow_1_to_2_cv *= 255;
59
60  cv::imwrite((exe_path / "optical_flow_1_to_0.png").string(), flow_1_to_0_cv);
61  cv::imwrite((exe_path / "optical_flow_1_to_2.png").string(), flow_1_to_2_cv);
62
63  return 0;
64}