You should now find the plugin under the sub-menu Plugins › UCB Vision Sciences › Quiver Plot. Now you should see additional jar files for download. This brings up a dialog where you can activate additional update sites.ģ) Activate the UCB Vision Sciences update site and close the dialog. To install it, you just need to add the UCB Vision Sciences update site:ġ) Select Help › Update… from the Fiji menu to start the updater.Ģ) Click on Manage update sites. The Quiver Plot plugin is part of the UCB Vision Sciences library. This plugin is also recordable within ImageJ, and can be implemented as part of a macro. This means that the vector lengths are relative, and therefore the vector lengths between any two plots are not cross-comparable. The plugin will then search for the largest magnitude vector in the magnitude image, and scale all of the vectors relative to the largest vector. the pixel dimensions of the final plot.). The third option allows you to set the resolution of plot (i.e. Make sure that there are no NaN values with your images before using the plugin. NOTE: pixels with a value of NaN will generate erratic vectors. Of these windows, select the images that correspond to the angle map and magnitude map (i.e. There are two drop-down menus that list all of the currently open windows. When you run the plugin, you will see the following window: The Quiver Plot plugin needs two 32-bit images, one representing the vector angles (direction) and one representing the vector magnitude. The Quiver Plot plugin was then used to make a quiver plot based off of the angle and speed arrays. (NOTE: The actual pixel values in these images needs to be the angle in degrees, and the magnitude of the vector). Vector analysis was performed on the wave to generate a corresponding array of vector angles and vector speeds for points in space. Kwargs = Īx.set_ylim(sorted(ax.get_ylim(), reverse=True))Įxample usage: flow = cv2.calcOpticalFlowFarneback(įrame_1, frame_2, None, 0.5, 3, 15, 3, 5, 1.Intro to generating a quiver plot From left to right: The original data showing a wave moving through time (blue = start time, red = end time). Y = np.linspace(margin, h - margin - 1, ny, dtype=np.int64) X = np.linspace(margin, w - margin - 1, nx, dtype=np.int64) Kwargs: quiver kwargs (default: angles="xy", scale_units="xy") Margin: width (px) of enclosing region without arrows Spacing: space (px) between each arrow in grid def plot_quiver(ax, flow, spacing, margin=0, **kwargs): Here is a general method for plotting a quiver field easily and accurately. In some occasions, you will have to change the step so the height and with are divisible by it. The x positions goes from 0 to image width, while the y positions are inversed (otherwise the optical flow will be up side down) from image height to 0. The step is to downsample the number of optical flow vectors picked. Plt.quiver(np.arange(0, flow.shape, step), np.arange(flow.shape, -1, -step),įlow, flow) Taxi2 = Image.open(taxis_fnames)įlow = cv.calcOpticalFlowFarneback(np.array(taxi1), Taxis_fnames = list(Path('taxi').iterdir()) Get all images and pick two random frames.I explain all my example here using the Hamburg taxi sequence:
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