.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/plot_optical_flow.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_plot_optical_flow.py: ===================================================== Optical Flow: Predicting movement with the RAFT model ===================================================== Optical flow is the task of predicting movement between two images, usually two consecutive frames of a video. Optical flow models take two images as input, and predict a flow: the flow indicates the displacement of every single pixel in the first image, and maps it to its corresponding pixel in the second image. Flows are (2, H, W)-dimensional tensors, where the first axis corresponds to the predicted horizontal and vertical displacements. The following example illustrates how torchvision can be used to predict flows using our implementation of the RAFT model. We will also see how to convert the predicted flows to RGB images for visualization. .. GENERATED FROM PYTHON SOURCE LINES 17-46 .. code-block:: default import numpy as np import torch import matplotlib.pyplot as plt import torchvision.transforms.functional as F import torchvision.transforms as T plt.rcParams["savefig.bbox"] = "tight" # sphinx_gallery_thumbnail_number = 2 def plot(imgs, **imshow_kwargs): if not isinstance(imgs[0], list): # Make a 2d grid even if there's just 1 row imgs = [imgs] num_rows = len(imgs) num_cols = len(imgs[0]) _, axs = plt.subplots(nrows=num_rows, ncols=num_cols, squeeze=False) for row_idx, row in enumerate(imgs): for col_idx, img in enumerate(row): ax = axs[row_idx, col_idx] img = F.to_pil_image(img.to("cpu")) ax.imshow(np.asarray(img), **imshow_kwargs) ax.set(xticklabels=[], yticklabels=[], xticks=[], yticks=[]) plt.tight_layout() .. GENERATED FROM PYTHON SOURCE LINES 47-55 Reading Videos Using Torchvision -------------------------------- We will first read a video using :func:`~torchvision.io.read_video`. Alternatively one can use the new :class:`~torchvision.io.VideoReader` API (if torchvision is built from source). The video we will use here is free of use from `pexels.com `_, credits go to `Pavel Danilyuk `_. .. GENERATED FROM PYTHON SOURCE LINES 55-66 .. code-block:: default import tempfile from pathlib import Path from urllib.request import urlretrieve video_url = "https://download.pytorch.org/tutorial/pexelscom_pavel_danilyuk_basketball_hd.mp4" video_path = Path(tempfile.mkdtemp()) / "basketball.mp4" _ = urlretrieve(video_url, video_path) .. GENERATED FROM PYTHON SOURCE LINES 67-74 :func:`~torchvision.io.read_video` returns the video frames, audio frames and the metadata associated with the video. In our case, we only need the video frames. Here we will just make 2 predictions between 2 pre-selected pairs of frames, namely frames (100, 101) and (150, 151). Each of these pairs corresponds to a single model input. .. GENERATED FROM PYTHON SOURCE LINES 74-84 .. code-block:: default from torchvision.io import read_video frames, _, _ = read_video(str(video_path)) frames = frames.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W) img1_batch = torch.stack([frames[100], frames[150]]) img2_batch = torch.stack([frames[101], frames[151]]) plot(img1_batch) .. image-sg:: /auto_examples/images/sphx_glr_plot_optical_flow_001.png :alt: plot optical flow :srcset: /auto_examples/images/sphx_glr_plot_optical_flow_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 85-90 The RAFT model that we will use accepts RGB float images with pixel values in [-1, 1]. The frames we got from :func:`~torchvision.io.read_video` are int images with values in [0, 255], so we will have to pre-process them. We also reduce the image sizes for the example to run faster. Image dimension must be divisible by 8. .. GENERATED FROM PYTHON SOURCE LINES 90-113 .. code-block:: default def preprocess(batch): transforms = T.Compose( [ T.ConvertImageDtype(torch.float32), T.Normalize(mean=0.5, std=0.5), # map [0, 1] into [-1, 1] T.Resize(size=(520, 960)), ] ) batch = transforms(batch) return batch # If you can, run this example on a GPU, it will be a lot faster. device = "cuda" if torch.cuda.is_available() else "cpu" img1_batch = preprocess(img1_batch).to(device) img2_batch = preprocess(img2_batch).to(device) print(f"shape = {img1_batch.shape}, dtype = {img1_batch.dtype}") .. rst-class:: sphx-glr-script-out Out: .. code-block:: none shape = torch.Size([2, 3, 520, 960]), dtype = torch.float32 .. GENERATED FROM PYTHON SOURCE LINES 114-121 Estimating Optical flow using RAFT ---------------------------------- We will use our RAFT implementation from :func:`~torchvision.models.optical_flow.raft_large`, which follows the same architecture as the one described in the `original paper `_. We also provide the :func:`~torchvision.models.optical_flow.raft_small` model builder, which is smaller and faster to run, sacrificing a bit of accuracy. .. GENERATED FROM PYTHON SOURCE LINES 121-131 .. code-block:: default from torchvision.models.optical_flow import raft_large model = raft_large(pretrained=True, progress=False).to(device) model = model.eval() list_of_flows = model(img1_batch.to(device), img2_batch.to(device)) print(f"type = {type(list_of_flows)}") print(f"length = {len(list_of_flows)} = number of iterations of the model") .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Downloading: "https://download.pytorch.org/models/raft_large_C_T_SKHT_V2-ff5fadd5.pth" to /root/.cache/torch/hub/checkpoints/raft_large_C_T_SKHT_V2-ff5fadd5.pth type = length = 12 = number of iterations of the model .. GENERATED FROM PYTHON SOURCE LINES 132-144 The RAFT model outputs lists of predicted flows where each entry is a (N, 2, H, W) batch of predicted flows that corresponds to a given "iteration" in the model. For more details on the iterative nature of the model, please refer to the `original paper `_. Here, we are only interested in the final predicted flows (they are the most acccurate ones), so we will just retrieve the last item in the list. As described above, a flow is a tensor with dimensions (2, H, W) (or (N, 2, H, W) for batches of flows) where each entry corresponds to the horizontal and vertical displacement of each pixel from the first image to the second image. Note that the predicted flows are in "pixel" unit, they are not normalized w.r.t. the dimensions of the images. .. GENERATED FROM PYTHON SOURCE LINES 144-150 .. code-block:: default predicted_flows = list_of_flows[-1] print(f"dtype = {predicted_flows.dtype}") print(f"shape = {predicted_flows.shape} = (N, 2, H, W)") print(f"min = {predicted_flows.min()}, max = {predicted_flows.max()}") .. rst-class:: sphx-glr-script-out Out: .. code-block:: none dtype = torch.float32 shape = torch.Size([2, 2, 520, 960]) = (N, 2, H, W) min = -3.862499713897705, max = 6.464077949523926 .. GENERATED FROM PYTHON SOURCE LINES 151-161 Visualizing predicted flows --------------------------- Torchvision provides the :func:`~torchvision.utils.flow_to_image` utlity to convert a flow into an RGB image. It also supports batches of flows. each "direction" in the flow will be mapped to a given RGB color. In the images below, pixels with similar colors are assumed by the model to be moving in similar directions. The model is properly able to predict the movement of the ball and the player. Note in particular the different predicted direction of the ball in the first image (going to the left) and in the second image (going up). .. GENERATED FROM PYTHON SOURCE LINES 161-172 .. code-block:: default from torchvision.utils import flow_to_image flow_imgs = flow_to_image(predicted_flows) # The images have been mapped into [-1, 1] but for plotting we want them in [0, 1] img1_batch = [(img1 + 1) / 2 for img1 in img1_batch] grid = [[img1, flow_img] for (img1, flow_img) in zip(img1_batch, flow_imgs)] plot(grid) .. image-sg:: /auto_examples/images/sphx_glr_plot_optical_flow_002.png :alt: plot optical flow :srcset: /auto_examples/images/sphx_glr_plot_optical_flow_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 173-181 Bonus: Creating GIFs of predicted flows --------------------------------------- In the example above we have only shown the predicted flows of 2 pairs of frames. A fun way to apply the Optical Flow models is to run the model on an entire video, and create a new video from all the predicted flows. Below is a snippet that can get you started with this. We comment out the code, because this example is being rendered on a machine without a GPU, and it would take too long to run it. .. GENERATED FROM PYTHON SOURCE LINES 181-194 .. code-block:: default # from torchvision.io import write_jpeg # for i, (img1, img2) in enumerate(zip(frames, frames[1:])): # # Note: it would be faster to predict batches of flows instead of individual flows # img1 = preprocess(img1[None]).to(device) # img2 = preprocess(img2[None]).to(device) # list_of_flows = model(img1_batch, img2_batch) # predicted_flow = list_of_flows[-1][0] # flow_img = flow_to_image(predicted_flow).to("cpu") # output_folder = "/tmp/" # Update this to the folder of your choice # write_jpeg(flow_img, output_folder + f"predicted_flow_{i}.jpg") .. GENERATED FROM PYTHON SOURCE LINES 195-199 Once the .jpg flow images are saved, you can convert them into a video or a GIF using ffmpeg with e.g.: ffmpeg -f image2 -framerate 30 -i predicted_flow_%d.jpg -loop -1 flow.gif .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 11.274 seconds) .. _sphx_glr_download_auto_examples_plot_optical_flow.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_optical_flow.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_optical_flow.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_