import time
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import cv2
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import numpy as np
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import onnxruntime
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class IDENTIFIER:
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def __init__(self, path):
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# Initialize model
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self.initialize_model(path)
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def __call__(self, image):
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return self.idengify(image)
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def initialize_model(self, path):
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self.session = onnxruntime.InferenceSession(path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
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self.class_names = eval(self.session.get_modelmeta().custom_metadata_map['names'])
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# Get model info
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self.get_input_details()
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self.get_output_details()
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def idengify(self, image):
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input_tensor, ratio = self.prepare_input(image)
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# Perform inference on the image
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outputs = self.inference(input_tensor)
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self.herb_probabilities = outputs[0]
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return self.herb_probabilities
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def prepare_input(self, image):
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self.img_height, self.img_width = image.shape[:2]
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input_img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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# Resize图片不要直接使用resize，需要按比例缩放，空白区域填空纯色即可
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input_img, ratio = self.ratioresize(input_img)
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# Scale input pixel values to 0 to 1
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input_img = input_img / 255.0
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input_img = input_img.transpose(2, 0, 1)
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input_tensor = input_img[np.newaxis, :, :, :].astype(np.float32)
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return input_tensor, ratio
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def inference(self, input_tensor):
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start = time.perf_counter()
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outputs = self.session.run(self.output_names, {self.input_names[0]: input_tensor})
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# print(f"Inference time: {(time.perf_counter() - start)*1000:.2f} ms")
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return outputs
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def get_input_details(self):
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model_inputs = self.session.get_inputs()
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self.input_names = [model_inputs[i].name for i in range(len(model_inputs))]
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self.input_shape = model_inputs[0].shape
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self.input_height = self.input_shape[2]
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self.input_width = self.input_shape[3]
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def get_output_details(self):
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model_outputs = self.session.get_outputs()
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self.output_names = [model_outputs[i].name for i in range(len(model_outputs))]
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# 等比例缩放图片
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def ratioresize(self, im, color=114):
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shape = im.shape[:2]
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new_h, new_w = self.input_height, self.input_width
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padded_img = np.ones((new_h, new_w, 3), dtype=np.uint8) * color
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# Scale ratio (new / old)
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r = min(new_h / shape[0], new_w / shape[1])
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# Compute padding
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new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
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if shape[::-1] != new_unpad:
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im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
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padded_img[: new_unpad[1], : new_unpad[0]] = im
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padded_img = np.ascontiguousarray(padded_img)
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return padded_img, 1 / r
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