initial commit

yolo_server/infer_new.py

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+import paddle.inference as paddle_infer
+import numpy as np
+import cv2
+class Yolo_model_infer:
+    def __init__(self, model_dir="./ppyoloe_plus_crn_t_auxhead_320_300e_coco", target_size=[320, 320]):
+        # 初始化 paddle 推理
+        self.model_dir = model_dir
+        self.config = paddle_infer.Config(model_dir + "/model.pdmodel", model_dir + "/model.pdiparams")
+        self.config.disable_glog_info()
+        self.config.enable_use_gpu(500, 0)
+        self.config.enable_memory_optim()
+        self.config.switch_ir_optim()
+        self.config.switch_use_feed_fetch_ops(False)
+        self.predictor = paddle_infer.create_predictor(self.config)
+        self.input_names = self.predictor.get_input_names()
+        self.input_handle = self.predictor.get_input_handle(self.input_names[0])
+        self.input_handle1 = self.predictor.get_input_handle(self.input_names[1])
+        self.output_names = self.predictor.get_output_names()
+        self.output_handle = self.predictor.get_output_handle(self.output_names[0])
+
+        self.target_size = target_size
+        origin_shape = (240, 320)
+        resize_h, resize_w = self.target_size
+        self.im_scale_y = resize_h / float(origin_shape[0])
+        self.im_scale_x = resize_w / float(origin_shape[1])
+        self.scale_info = np.array([[self.im_scale_y, self.im_scale_x]]).astype('float32')
+    def infer(self,src) -> np.ndarray:
+        image = self.preprocess(src)
+        self.input_handle.copy_from_cpu(image)
+        self.input_handle1.copy_from_cpu(self.scale_info)
+        self.predictor.run()
+        results = self.output_handle.copy_to_cpu()
+        return results
+    def preprocess(self,src):
+        # resize
+        # keep_ratio=0
+        img = cv2.resize(
+            src,
+            None,
+            None,
+            fx=self.im_scale_x,
+            fy=self.im_scale_y,
+            interpolation=2)
+        # NormalizeImage
+        img = img.astype(np.float32, copy=False)
+        scale = 1.0 / 255.0
+        img *= scale
+        # Permute
+        img = img.transpose((2, 0, 1))
+        img = np.array((img, ))
+        # .astype('float32')
+        return img
+