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import impact.additional_dependencies | |
from impact.utils import * | |
impact.additional_dependencies.ensure_onnx_package() | |
try: | |
import onnxruntime | |
def onnx_inference(image, onnx_model): | |
# prepare image | |
pil = tensor2pil(image) | |
image = np.ascontiguousarray(pil) | |
image = image[:, :, ::-1] # to BGR image | |
image = image.astype(np.float32) | |
image -= [103.939, 116.779, 123.68] # 'caffe' mode image preprocessing | |
# do detection | |
onnx_model = onnxruntime.InferenceSession(onnx_model, providers=["CPUExecutionProvider"]) | |
outputs = onnx_model.run( | |
[s_i.name for s_i in onnx_model.get_outputs()], | |
{onnx_model.get_inputs()[0].name: np.expand_dims(image, axis=0)}, | |
) | |
labels = [op for op in outputs if op.dtype == "int32"][0] | |
scores = [op for op in outputs if isinstance(op[0][0], np.float32)][0] | |
boxes = [op for op in outputs if isinstance(op[0][0], np.ndarray)][0] | |
# filter-out useless item | |
idx = np.where(labels[0] == -1)[0][0] | |
labels = labels[0][:idx] | |
scores = scores[0][:idx] | |
boxes = boxes[0][:idx].astype(np.uint32) | |
return labels, scores, boxes | |
except Exception as e: | |
print("[ERROR] ComfyUI-Impact-Pack: 'onnxruntime' package doesn't support 'python 3.11', yet.") | |
print(f"\t{e}") | |