Closed alter-sachin closed 4 years ago
Hi @alter-sachin We will probably port this example in future to python. For now, you can skip parts you don't need (like SDK integration). Overall, this is just one of possible heuristics to improve depth for collision avoidance, but it proven very useful in real-life applications on drones. Let me know if you have questions about the code
@dorodnic Thankyou for the prompt reply. I am not sure about the SDK integration part. I have applied the mask generated using the main filter(edge and corner combined) and applied it to the original decimated depth.
Is that all I need to do ?
If yes, my decimated frame needs to use the mask , how do I apply it properly is where I am worried.
Thanks :)
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Hi, I hope this finds you well . I am using the realsense D435 to do some obstacle avoidance. I am referring to the depth filter code and read here: https://github.com/IntelRealSense/librealsense/blob/7a3ffaf412401849894a4d69791b98559f624d47/wrappers/opencv/depth-filter/readme.md
I have managed to create equivalent main filters using python opencv:
`def main_filter(self, frame): source = frame
self.filter_edge(source)
I then access the same in another function using: ` filtered_ir_image = self.main_filter(ir1_image)
scale_percent = 60 # percent of original size
I was hoping this would be enough to get a new distance array from my obstacles. I would like to understand how the SDK portion of the guide be implemented in python ? Is that even necessary ? Any pointers would be great.
https://github.com/IntelRealSense/librealsense/blob/7a3ffaf412401849894a4d69791b98559f624d47/wrappers/opencv/depth-filter/readme.md#sdk-integration