E2E Tests: ImageManip node and better checks. (#140)

* Add some checks to E2E testing.

* Use ImageManip to handle small inputs.

tests/end_to_end/manual.py

@@ -1,7 +1,7 @@
 import argparse
 
 import depthai as dai
-from utils import get_input_shape, parse_model_slug
+from utils import get_input_shape, get_num_inputs, parse_model_slug
 
 from depthai_nodes.parsing_neural_network import ParsingNeuralNetwork
 
@@ -25,7 +25,8 @@
 
 try:
     device = dai.Device(dai.DeviceInfo(args.ip))
-except Exception:
+except Exception as e:
+    print(e)
     print("Can't connect to the device with IP/mxid: ", args.ip)
     exit(6)
 
@@ -41,13 +42,20 @@
         )
         try:
             nn_archive_path = dai.getModelFromZoo(model_desc, useCached=False)
-            nn_archive = dai.NNArchive(nn_archive_path)
-        except Exception:
+        except Exception as e:
+            print(e)
             print(
                 f"Couldn't find model {args.model_slug} for {device.getPlatform().name} in the ZOO"
             )
             device.close()
             exit(7)
+        try:
+            nn_archive = dai.NNArchive(nn_archive_path)
+        except Exception as e:
+            print(e)
+            print(f"Couldn't load the model {args.model_slug} from NN archive.")
+            device.close()
+            exit(9)
 
     else:
         nn_archive = dai.NNArchive(args.nn_archive)
@@ -59,13 +67,46 @@
         device.close()
         exit(5)
 
-    input_size = get_input_shape(nn_archive)
-    if input_size[0] < 128 and input_size[1] < 128:
-        print("Input size is too small for the device.")
+    if get_num_inputs(nn_archive) > 1:
+        print(
+            "This model has more than one input. Currently, only models with one input are supported."
+        )
+        device.close()
+        exit(8)
+
+    try:
+        input_size = get_input_shape(nn_archive)
+    except Exception as e:
+        print(e)
         device.close()
         exit(8)
 
-    nn_w_parser = pipeline.create(ParsingNeuralNetwork).build(camera_node, nn_archive)
+    image_type = (
+        dai.ImgFrame.Type.BGR888i
+        if device.getPlatform().name == "RVC4"
+        else dai.ImgFrame.Type.BGR888p
+    )
+    manip = None
+
+    if input_size[0] % 2 != 0 or input_size[1] % 2 != 0:
+        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:
+        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)
+
+    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()
 

tests/end_to_end/test_e2e.py

@@ -84,15 +84,17 @@ def test_pipelines(IP: str, ip_platform: str, nn_archive_path, slug):
             )
     except subprocess.CalledProcessError as e:
         if e.returncode == 5:
-            pytest.skip(f"Model not supported on {ip_platform}.")
+            pytest.skip(f"Model {slug} not supported on {ip_platform}.")
         elif e.returncode == 6:
             pytest.skip(f"Can't connect to the device with IP/mxid: {IP}")
         elif e.returncode == 7:
             pytest.skip(f"Couldn't find model {slug} in the ZOO")
         elif e.returncode == 8:
             pytest.skip(
-                "The model is not supported in this test. (small input size, grayscale image, etc.)"
+                f"The model {slug} is not supported in this test. (small input size, grayscale image, etc.)"
             )
+        elif e.returncode == 9:
+            pytest.skip(f"Couldn't load the model {slug} from NN archive.")
         else:
             raise RuntimeError("Pipeline crashed.") from e
     except subprocess.TimeoutExpired:

tests/end_to_end/utils.py

@@ -61,6 +61,13 @@ def get_input_shape(nn_archive: dai.NNArchive) -> Tuple[int, int]:
     return input_shape
 
 
+def get_num_inputs(nn_archive: dai.NNArchive) -> int:
+    """Get the number of inputs of the model from the NN archive."""
+    inputs = get_inputs_from_archive(nn_archive)
+
+    return len(inputs)
+
+
 def parse_model_slug(full_slug) -> Tuple[str, str]:
     """Parse the model slug into model_slug and model_version_slug."""
     if ":" not in full_slug: