mirror of
https://github.com/azaion/detections.git
synced 2026-04-22 06:56:31 +00:00
Oleksandr Bezdieniezhnykh
fc57d677b4
- Updated various Cython files to explicitly cast types, enhancing type safety and readability. - Adjusted the `engine_name` property in `InferenceEngine` and its subclasses to be set directly in the constructor. - Modified the `request` method in `_SessionWithBase` to accept `*args` for better flexibility. - Ensured proper type casting for return values in methods across multiple classes, including `Inference`, `CoreMLEngine`, and `TensorRTEngine`. These changes aim to streamline the codebase and improve maintainability by enforcing consistent type usage.
86 lines
2.7 KiB
Cython
86 lines
2.7 KiB
Cython
from engines.inference_engine cimport InferenceEngine
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cimport constants_inf
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import numpy as np
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from PIL import Image
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import io
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import os
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import tempfile
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import zipfile
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cdef class CoreMLEngine(InferenceEngine):
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def __init__(self, model_bytes: bytes, batch_size: int = 1, **kwargs):
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super().__init__(model_bytes, batch_size)
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import coremltools as ct
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model_path = kwargs.get('model_path')
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if model_path is None:
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model_path = self._extract_from_zip(model_bytes)
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self.model = ct.models.MLModel(
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model_path, compute_units=ct.ComputeUnit.ALL)
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spec = self.model.get_spec()
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img_input = spec.description.input[0]
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self.img_width = int(img_input.type.imageType.width)
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self.img_height = int(img_input.type.imageType.height)
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self.batch_size = 1
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constants_inf.log(<str>f'CoreML model: {self.img_width}x{self.img_height}')
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self.engine_name = <str>"coreml"
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@staticmethod
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def get_engine_filename():
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return "azaion_coreml.zip"
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@staticmethod
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def _extract_from_zip(model_bytes):
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tmpdir = tempfile.mkdtemp()
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buf = io.BytesIO(model_bytes)
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with zipfile.ZipFile(buf, 'r') as zf:
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zf.extractall(tmpdir)
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for item in os.listdir(tmpdir):
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if item.endswith('.mlpackage') or item.endswith('.mlmodel'):
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return os.path.join(tmpdir, item)
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raise ValueError("No .mlpackage or .mlmodel found in zip")
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cdef tuple get_input_shape(self):
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return <tuple>(self.img_height, self.img_width)
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cdef int get_batch_size(self):
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return <int>1
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cdef run(self, input_data):
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cdef int w = self.img_width
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cdef int h = self.img_height
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blob = input_data[0]
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img_array = np.clip(blob * 255.0, 0, 255).astype(np.uint8)
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img_array = np.transpose(img_array, (1, 2, 0))
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pil_img = Image.fromarray(img_array, 'RGB')
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pred = self.model.predict({
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'image': pil_img,
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'iouThreshold': 0.45,
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'confidenceThreshold': 0.25,
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})
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coords = pred.get('coordinates', np.empty((0, 4), dtype=np.float32))
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confs = pred.get('confidence', np.empty((0, 80), dtype=np.float32))
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if coords.size == 0:
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return [np.zeros((1, 0, 6), dtype=np.float32)]
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cx, cy, bw, bh = coords[:, 0], coords[:, 1], coords[:, 2], coords[:, 3]
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x1 = (cx - bw / 2) * w
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y1 = (cy - bh / 2) * h
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x2 = (cx + bw / 2) * w
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y2 = (cy + bh / 2) * h
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class_ids = np.argmax(confs, axis=1).astype(np.float32)
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conf_values = np.max(confs, axis=1)
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dets = np.stack([x1, y1, x2, y2, conf_values, class_ids], axis=1)
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return [dets[np.newaxis, :, :]]
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