onnx_test_model.py File Reference

Namespaces
namespace	onnx_test_model

Variables
	onnx_test_model._
	onnx_test_model.bbox = draw.textbbox((x1, y1), label, font=font)
	onnx_test_model.best_class_indices = np.argmax(confs, axis=1)
dict	onnx_test_model.best_class_scores = confs[np.arange(N), best_class_indices]
dict	onnx_test_model.boxes = output_dict["boxes"]
list	onnx_test_model.class_names = [f"class_{i}" for i in range(num_classes)]
	onnx_test_model.cls = best_class_indices[idx]
dict	onnx_test_model.confs = output_dict["confs"]
list	onnx_test_model.detections = []
	onnx_test_model.draw = ImageDraw.Draw(img)
	onnx_test_model.fill
	onnx_test_model.font = ImageFont.load_default()
	onnx_test_model.H
	onnx_test_model.H_img
	onnx_test_model.img = Image.open(sys.argv[2]).convert('RGB')
float	onnx_test_model.img_np = np.array(img_resized).astype("float32") / 255.0
	onnx_test_model.img_resized = img.resize((W, H))
	onnx_test_model.input_name = session.get_inputs()[0].name
	onnx_test_model.input_shape = session.get_inputs()[0].shape
int	onnx_test_model.k = 10
	onnx_test_model.key
str	onnx_test_model.label = f"{class_names[cls]} {score:.2f}"
	onnx_test_model.N
	onnx_test_model.num_classes
	onnx_test_model.outline
dict	onnx_test_model.output_dict = {name: value for name, value in zip(output_names, outputs)}
list	onnx_test_model.output_names = [o.name for o in session.get_outputs()]
	onnx_test_model.outputs = session.run(output_names, {input_name: img_np})
dict	onnx_test_model.score = best_class_scores[idx]
	onnx_test_model.session = ort.InferenceSession(sys.argv[1], providers=["CPUExecutionProvider"])
list	onnx_test_model.text_bg = [text_x, text_y, text_x + text_w, text_y + text_h]
	onnx_test_model.text_h = bbox[3] - bbox[1]
	onnx_test_model.text_w = bbox[2] - bbox[0]
	onnx_test_model.text_x = x1
	onnx_test_model.text_y = max(0, y1 - text_h)
float	onnx_test_model.threshold = 0.75
	onnx_test_model.topk_indices = np.argsort(-best_class_scores)[:k]
	onnx_test_model.W
	onnx_test_model.W_img
	onnx_test_model.width
	onnx_test_model.x1
	onnx_test_model.x1_pix = x1 * W_img
	onnx_test_model.x2
	onnx_test_model.x2_pix = x2 * W_img
	onnx_test_model.y1
	onnx_test_model.y1_pix = y1 * H_img
	onnx_test_model.y2
	onnx_test_model.y2_pix = y2 * H_img