add README for evalu toolkit

evaluation_toolkit/README.md

+# Evaluation Toolkit
+
+Set of tools to run a particular algorithm on a dataset constructed with the validation set constructor, and evaluate it, along with advanced statistics regarding depth value, dans pixel position in image with repsect to flight path vector.
+
+## Inference Example
+
+Get the last frame and a previous frame such that the displacement magnitude is as close to 30cm as possible, with the condition of having a rotation of less that 1 radian. Each frame is preprocessed so that it is of shape `[C, H, W]` and with a range `[0, 1]` instead of `[0, 255]`.
+
+```python
+from evaluation_toolkit import inferenceFramework
+
+engine = inferenceFramework(dataset_root, evaluation_list, lambda x: x.transpose(2, 0, 1).astype(np.float32)[None]/255)
+
+for sample in tqdm(engine):
+    latest_frame, latest_intrinsics, _ = sample.get_frame()
+    previous_frame, previous_intrinsics, previous_pose = sample.get_previous_frame(displacement=0.3)
+    estimated_depth_map = my_model(latest_frame, previous_frame, previous_pose)
+    engine.finish_frame(estimated_depth_map)
+mean_inference_time, output_depth_maps = engine.finalize(output_file='output.npz')
+```
+
+You can find an example usage of this Inference Framework for SfmLearner [here](https://github.com/ClementPinard/SfmLearner-Pytorch/tree/inference-framework)
+
+## Evaluation
+
+The evaluation step is a simple script that takes into input the computed depth maps (here in the file `output.npz`)
+
+```
+depth_evaluation --dataset_root /path/to/dataset/root --est_depth output.npz --evaluation_list_path /path/to/evaluation_list.txt --flight_path_vector_list /path/to/fligt_path_vector_list.txt <--scale_invariant> <--mask_path /path/to/mask.npy>
+```
+
+It will output typical metrics and plot advanced statistics regarding the dataset and the depth estimations.
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