Visualize heatmap ================= Heatmap is an optional [ArFrame](../../argaze.md/#argaze.ArFeatures.ArFrame) pipeline step. It is executed at each new gaze position to update heatmap image. ![Heatmap](../../img/ar_frame_heatmap.png) ## Enable ArFrame heatmap [ArFrame](../../argaze.md/#argaze.ArFeatures.ArFrame) heatmap visualization can be enabled thanks to a dedicated JSON entry. Here is the JSON ArFrame configuration file example where heatmap visualization is enabled: ```json { "name": "My FullHD screen", "size": [1920, 1080], ... "heatmap": { "size": [320, 180], "sigma": 0.025, "buffer": 0 } } ``` Then, here is how to access to heatmap object: ```python # Assuming an ArFrame is loaded ... print("heatmap:", ar_frame.heatmap) ``` Finally, here is what the program writes in console: ```txt heatmap: Heatmap(size=[320, 180], buffer=0, sigma=0.025) ``` Now, let's understand the meaning of each JSON entry. ### Size The heatmap image size in pixel. Higher size implies higher CPU load. ### Sigma The gaussian point spreading to draw at each gaze position. ![Point spread](../../img/point_spread.png) ### Buffer The size of point spread images buffer (0 means no buffering) to visualize only last N gaze positions. ## Export heatmap to PNG file Once timestamped gaze positions have been processed by [ArFrame.look](../../argaze.md/#argaze.ArFeatures.ArFrame.look) method, it is possible to write heatmap image thanks to OpenCV package. ```python import cv2 # Assuming that timestamped gaze positions have been processed by ArFrame.look method ... # Export heatmap image cv2.imwrite('./heatmap.png', ar_frame.heatmap.image) ```