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E2E Tests: ImageManip node and better checks. #140

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merged 2 commits into from
Nov 27, 2024
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E2E Tests: ImageManip node and better checks. #140

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e9c1e21
Add some checks to E2E testing.
kkeroo Nov 26, 2024
0849253
Use ImageManip to handle small inputs.
kkeroo Nov 26, 2024
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31 changes: 22 additions & 9 deletions tests/end_to_end/manual.py
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Original file line number Diff line number Diff line change
Expand Up @@ -81,19 +81,32 @@
device.close()
exit(8)

image_type = (
dai.ImgFrame.Type.BGR888i
if device.getPlatform().name == "RVC4"
else dai.ImgFrame.Type.BGR888p
)
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Can we set the type based on ˙dai_type˙ field in the NNArchive if present otherwise use this as default?

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Will be addressed in the future because dai has not yet implemented it.

manip = None

if input_size[0] % 2 != 0 or input_size[1] % 2 != 0:
print(
f"We only support even numbers for resolution sizes. Got {input_size[0]}x{input_size[1]}."
)
device.close()
exit(8)
manip = pipeline.create(dai.node.ImageManipV2)
manip.initialConfig.addResize(input_size[0], input_size[1])
large_input_shape = (input_size[0] * 2, input_size[1] * 2)

if input_size[0] < 128 and input_size[1] < 128:
print("Input size is too small for the device.")
device.close()
exit(8)
manip = pipeline.create(dai.node.ImageManipV2)
manip.initialConfig.addResize(input_size[0], input_size[1])
large_input_shape = (input_size[0] * 4, input_size[1] * 4)

nn_w_parser = pipeline.create(ParsingNeuralNetwork).build(camera_node, nn_archive)
if manip:
camera_node.requestOutput(large_input_shape, type=image_type).link(
manip.inputImage
)
nn_w_parser = pipeline.create(ParsingNeuralNetwork).build(manip.out, nn_archive)
else:
nn_w_parser = pipeline.create(ParsingNeuralNetwork).build(
camera_node, nn_archive
)

head_indices = nn_w_parser._parsers.keys()

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