410 lines
16 KiB
Python
410 lines
16 KiB
Python
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import ctypes
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import os
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import random
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import sys
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import time
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import cv2
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import numpy as np
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import pycuda.autoinit # noqa: F401
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import pycuda.driver as cuda
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import tensorrt as trt
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CONF_THRESH = 0.5
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IOU_THRESHOLD = 0.4
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POSE_NUM = 17 * 3
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DET_NUM = 6
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SEG_NUM = 32
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OBB_NUM = 1
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# 全局变量（你原来代码里用到了 categories，但没有定义，这里补上）
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categories = ["NoHat","Hat"] # 根据你的实际类别修改
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def get_img_path_batches(batch_size, img_dir):
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ret = []
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batch = []
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for root, dirs, files in os.walk(img_dir):
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for name in files:
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if len(batch) == batch_size:
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ret.append(batch)
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batch = []
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batch.append(os.path.join(root, name))
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if len(batch) > 0:
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ret.append(batch)
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return ret
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def plot_one_box(x, img, color=None, label=None, line_thickness=None):
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tl = (
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line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1
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)
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color = color or [random.randint(0, 255) for _ in range(3)]
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c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
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cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
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if label:
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tf = max(tl - 1, 1)
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t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
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c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
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cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA)
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cv2.putText(
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img,
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label,
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(c1[0], c1[1] - 2),
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0,
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tl / 3,
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[225, 255, 255],
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thickness=tf,
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lineType=cv2.LINE_AA,
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)
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class YoLo11TRT(object):
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def __init__(self, engine_file_path):
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# 只在这里 push 一次 CUDA 上下文
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self.ctx = cuda.Device(0).make_context()
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stream = cuda.Stream()
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TRT_LOGGER = trt.Logger(trt.Logger.INFO)
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runtime = trt.Runtime(TRT_LOGGER)
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with open(engine_file_path, "rb") as f:
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engine = runtime.deserialize_cuda_engine(f.read())
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context = engine.create_execution_context()
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host_inputs = []
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cuda_inputs = []
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host_outputs = []
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cuda_outputs = []
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bindings = []
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for binding in engine:
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print('binding:', binding, engine.get_binding_shape(binding))
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self.batch_size = engine.get_binding_shape(binding)[0]
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size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size
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dtype = trt.nptype(engine.get_binding_dtype(binding))
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host_mem = cuda.pagelocked_empty(size, dtype)
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cuda_mem = cuda.mem_alloc(host_mem.nbytes)
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bindings.append(int(cuda_mem))
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if engine.binding_is_input(binding):
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self.input_w = engine.get_binding_shape(binding)[-1]
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self.input_h = engine.get_binding_shape(binding)[-2]
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host_inputs.append(host_mem)
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cuda_inputs.append(cuda_mem)
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else:
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host_outputs.append(host_mem)
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cuda_outputs.append(cuda_mem)
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self.stream = stream
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self.context = context
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self.engine = engine
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self.host_inputs = host_inputs
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self.cuda_inputs = cuda_inputs
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self.host_outputs = host_outputs
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self.cuda_outputs = cuda_outputs
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self.bindings = bindings
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self.det_output_length = host_outputs[0].shape[0]
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def infer(self, raw_image_generator,conf=None,cate=None):
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global CONF_THRESH,categories
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if conf is not None:
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CONF_THRESH=conf
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if categories is not None:
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categories=cate
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# 移除了 self.ctx.push() 和 self.ctx.pop()，因为已在 __init__ 和 destroy 中处理
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print(f"infer 1")
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start = time.time()
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stream = self.stream
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context = self.context
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host_inputs = self.host_inputs
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cuda_inputs = self.cuda_inputs
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host_outputs = self.host_outputs
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cuda_outputs = self.cuda_outputs
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bindings = self.bindings
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print(f"infer 2")
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batch_image_raw = []
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batch_origin_h = []
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batch_origin_w = []
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batch_input_image = np.empty(shape=[self.batch_size, 3, self.input_h, self.input_w])
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print(f"infer 3")
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for i, image_raw in enumerate(raw_image_generator):
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input_image, image_raw, origin_h, origin_w = self.preprocess_image(image_raw)
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batch_image_raw.append(image_raw)
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batch_origin_h.append(origin_h)
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batch_origin_w.append(origin_w)
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np.copyto(batch_input_image[i], input_image)
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batch_input_image = np.ascontiguousarray(batch_input_image)
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print(f"infer 4")
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np.copyto(host_inputs[0], batch_input_image.ravel())
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cuda.memcpy_htod_async(cuda_inputs[0], host_inputs[0], stream)
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context.execute_async_v2(bindings=bindings, stream_handle=stream.handle)
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cuda.memcpy_dtoh_async(host_outputs[0], cuda_outputs[0], stream)
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stream.synchronize()
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print(f"infer 5")
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end = time.time()
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output = host_outputs[0]
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result_box_list=[]
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result_scores_list=[]
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result_classid_list=[]
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print(f"infer 6")
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for i in range(self.batch_size):
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result_boxes, result_scores, result_classid = self.post_process(
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output[i * self.det_output_length: (i + 1) * self.det_output_length],
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batch_origin_h[i], batch_origin_w[i]
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)
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print(f"infer 7")
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result_box_list.append(result_boxes.tolist())
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result_scores_list.append(result_scores.tolist())
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result_classid_list.append(result_classid.tolist())
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print(f"infer 8")
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for j in range(len(result_boxes)):
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box = result_boxes[j]
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if int(result_classid[j])<=len(categories):
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plot_one_box(
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box,
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batch_image_raw[i],
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label="{}:{:.2f}".format(categories[int(result_classid[j])], result_scores[j])
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)
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print(f"infer 9")
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return batch_image_raw,result_box_list,result_scores_list,result_classid_list,end - start
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def destroy(self):
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self.ctx.pop() # 只在这里 pop 一次
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def get_raw_image(self, image_path_batch):
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for img_path in image_path_batch:
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yield cv2.imread(img_path)
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def get_raw_image_zeros(self, image_path_batch=None):
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for _ in range(self.batch_size):
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yield np.zeros([self.input_h, self.input_w, 3], dtype=np.uint8)
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def preprocess_image(self, raw_bgr_image):
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image_raw = raw_bgr_image
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h, w, c = image_raw.shape
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image = cv2.cvtColor(image_raw, cv2.COLOR_BGR2RGB)
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r_w = self.input_w / w
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r_h = self.input_h / h
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if r_h > r_w:
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tw = self.input_w
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th = int(r_w * h)
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tx1 = tx2 = 0
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ty1 = int((self.input_h - th) / 2)
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ty2 = self.input_h - th - ty1
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else:
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tw = int(r_h * w)
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th = self.input_h
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tx1 = int((self.input_w - tw) / 2)
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tx2 = self.input_w - tw - tx1
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ty1 = ty2 = 0
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image = cv2.resize(image, (tw, th))
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image = cv2.copyMakeBorder(image, ty1, ty2, tx1, tx2, cv2.BORDER_CONSTANT, None, (128, 128, 128))
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image = image.astype(np.float32)
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image /= 255.0
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image = np.transpose(image, [2, 0, 1])
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image = np.expand_dims(image, axis=0)
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image = np.ascontiguousarray(image)
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return image, image_raw, h, w
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def xywh2xyxy(self, origin_h, origin_w, x):
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y = np.zeros_like(x)
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r_w = self.input_w / origin_w
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r_h = self.input_h / origin_h
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if r_h > r_w:
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y[:, 0] = x[:, 0]
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y[:, 2] = x[:, 2]
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y[:, 1] = x[:, 1] - (self.input_h - r_w * origin_h) / 2
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y[:, 3] = x[:, 3] - (self.input_h - r_w * origin_h) / 2
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y /= r_w
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else:
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y[:, 0] = x[:, 0] - (self.input_w - r_h * origin_w) / 2
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y[:, 2] = x[:, 2] - (self.input_w - r_h * origin_w) / 2
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y[:, 1] = x[:, 1]
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y[:, 3] = x[:, 3]
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y /= r_h
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return y
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def post_process(self, output, origin_h, origin_w):
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num_values_per_detection = DET_NUM + SEG_NUM + POSE_NUM + OBB_NUM
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num = int(output[0])
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pred = np.reshape(output[1:], (-1, num_values_per_detection))[:num, :]
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boxes = self.non_max_suppression(pred, origin_h, origin_w, conf_thres=CONF_THRESH, nms_thres=IOU_THRESHOLD)
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result_boxes = boxes[:, :4] if len(boxes) else np.array([])
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result_scores = boxes[:, 4] if len(boxes) else np.array([])
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result_classid = boxes[:, 5] if len(boxes) else np.array([])
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return result_boxes, result_scores, result_classid
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def bbox_iou(self, box1, box2, x1y1x2y2=True):
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if not x1y1x2y2:
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b1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2
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b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2
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b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2
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b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2
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else:
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b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3]
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b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3]
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inter_rect_x1 = np.maximum(b1_x1, b2_x1)
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inter_rect_y1 = np.maximum(b1_y1, b2_y1)
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inter_rect_x2 = np.minimum(b1_x2, b2_x2)
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inter_rect_y2 = np.minimum(b1_y2, b2_y2)
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inter_area = (np.clip(inter_rect_x2 - inter_rect_x1 + 1, 0, None) *
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np.clip(inter_rect_y2 - inter_rect_y1 + 1, 0, None))
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b1_area = (b1_x2 - b1_x1 + 1) * (b1_y2 - b1_y1 + 1)
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b2_area = (b2_x2 - b2_x1 + 1) * (b2_y2 - b2_y1 + 1)
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iou = inter_area / (b1_area + b2_area - inter_area + 1e-16)
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return iou
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def non_max_suppression(self, prediction, origin_h, origin_w, conf_thres=0.5, nms_thres=0.4):
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boxes = prediction[prediction[:, 4] >= conf_thres]
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boxes[:, :4] = self.xywh2xyxy(origin_h, origin_w, boxes[:, :4])
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boxes[:, 0] = np.clip(boxes[:, 0], 0, origin_w - 1)
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boxes[:, 2] = np.clip(boxes[:, 2], 0, origin_w - 1)
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boxes[:, 1] = np.clip(boxes[:, 1], 0, origin_h - 1)
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boxes[:, 3] = np.clip(boxes[:, 3], 0, origin_h - 1)
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confs = boxes[:, 4]
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boxes = boxes[np.argsort(-confs)]
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keep_boxes = []
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while boxes.shape[0]:
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large_overlap = self.bbox_iou(np.expand_dims(boxes[0, :4], 0), boxes[:, :4]) > nms_thres
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label_match = boxes[0, -1] == boxes[:, -1]
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invalid = large_overlap & label_match
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keep_boxes += [boxes[0]]
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boxes = boxes[~invalid]
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boxes = np.stack(keep_boxes, 0) if len(keep_boxes) else np.array([])
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return boxes
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# ====== 视频推理 + 显示 + FPS统计（主程序，不变，直接使用） ======
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# if __name__ == "__main__":
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# # PLUGIN_LIBRARY = r"D:\project\AI-PYTHON\tensorrtx-master\yolo11\build-b\Release\myplugins.dll"
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# # engine_file_path = r"D:\project\AI-PYTHON\tensorrtx-master\yolo11\build-b\Release\build.engine"
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# engine_file_path = r"D:\project\AI-PYTHON\Ai_tottle\engine\gdaq_hat_0926\gdaq_hat_0926.engine"
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# PLUGIN_LIBRARY = r"D:\project\AI-PYTHON\Ai_tottle\engine\gdaq_hat_0926\myplugins.dll"
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# if len(sys.argv) > 1:
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# engine_file_path = sys.argv[1]
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# if len(sys.argv) > 2:
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# PLUGIN_LIBRARY = sys.argv[2]
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#
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# ctypes.CDLL(PLUGIN_LIBRARY)
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#
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# yolo11_wrapper = YoLo11TRT(engine_file_path)
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#
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# try:
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# video_path = r"E:\yolo-dataset\test_video\shimian_invade\DJI_20250910142510_0001_V.mp4"
|
|||
|
|
# cap = cv2.VideoCapture(video_path)
|
|||
|
|
#
|
|||
|
|
# if not cap.isOpened():
|
|||
|
|
# print("[ERROR] 无法打开视频文件:", video_path)
|
|||
|
|
# exit(-1)
|
|||
|
|
#
|
|||
|
|
# frame_count = 0
|
|||
|
|
# fps_smoothed = 0
|
|||
|
|
# last_time = time.time()
|
|||
|
|
# frame_times = []
|
|||
|
|
#
|
|||
|
|
# print("[INFO] 开始处理视频，按 'q' 键退出 ...")
|
|||
|
|
# cost_time=0
|
|||
|
|
# count=0
|
|||
|
|
# while True:
|
|||
|
|
# ret, frame = cap.read()
|
|||
|
|
# if not ret:
|
|||
|
|
# print("[INFO] 视频播放完毕或读取失败")
|
|||
|
|
# break
|
|||
|
|
#
|
|||
|
|
# frame_count += 1
|
|||
|
|
# start_time = time.time()
|
|||
|
|
#
|
|||
|
|
# time_start=time.time_ns()
|
|||
|
|
#
|
|||
|
|
# image_path = r"C:\Users\14867\Downloads\1758382029741-frame.jpg"
|
|||
|
|
#
|
|||
|
|
# batch_image_raw, infer_time = yolo11_wrapper.infer([frame])
|
|||
|
|
# time_end = time.time_ns()
|
|||
|
|
# single_frame_time = infer_time
|
|||
|
|
# frame_times.append(single_frame_time * 1000)
|
|||
|
|
#
|
|||
|
|
# cost_time=cost_time+(time_end-time_start)/1000000
|
|||
|
|
# count=count+1
|
|||
|
|
# if count>100:
|
|||
|
|
# print(f"single_frame_time: {cost_time/count} ms")
|
|||
|
|
# count=0
|
|||
|
|
# cost_time=0
|
|||
|
|
#
|
|||
|
|
#
|
|||
|
|
#
|
|||
|
|
#
|
|||
|
|
# detected_frame = batch_image_raw[0]
|
|||
|
|
#
|
|||
|
|
# current_time = time.time()
|
|||
|
|
# elapsed = current_time - last_time
|
|||
|
|
#
|
|||
|
|
# if elapsed >= 1.0:
|
|||
|
|
# fps = frame_count / elapsed
|
|||
|
|
# fps_smoothed = fps
|
|||
|
|
# frame_count = 0
|
|||
|
|
# last_time = current_time
|
|||
|
|
#
|
|||
|
|
# fps_text = f"FPS: {fps_smoothed:.2f}"
|
|||
|
|
# time_text = f"Frame Time: {single_frame_time * 1000:.2f}ms"
|
|||
|
|
# cv2.putText(detected_frame, fps_text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
|
|||
|
|
# cv2.putText(detected_frame, time_text, (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
|
|||
|
|
#
|
|||
|
|
# cv2.imshow("YOLO11 Detection", detected_frame)
|
|||
|
|
#
|
|||
|
|
# if cv2.waitKey(1) & 0xFF == ord('q'):
|
|||
|
|
# print("[INFO] 用户退出")
|
|||
|
|
# break
|
|||
|
|
#
|
|||
|
|
# cap.release()
|
|||
|
|
# cv2.destroyAllWindows()
|
|||
|
|
#
|
|||
|
|
# finally:
|
|||
|
|
# yolo11_wrapper.destroy()
|
|||
|
|
|
|||
|
|
# 单张图片测试代码
|
|||
|
|
if __name__ == "__main__":
|
|||
|
|
# 设置引擎和插件路径
|
|||
|
|
engine_file_path = r"D:\project\AI-PYTHON\Ai_tottle\engine\gdaq_hat_0926\gdaq_hat_0926.engine"
|
|||
|
|
PLUGIN_LIBRARY = r"D:\project\AI-PYTHON\Ai_tottle\engine\gdaq_hat_0926\myplugins.dll"
|
|||
|
|
# PLUGIN_LIBRARY = r"D:\project\AI-PYTHON\tensorrtx-master\yolo11\build-b\Release\myplugins.dll"
|
|||
|
|
# engine_file_path = r"D:\project\AI-PYTHON\tensorrtx-master\yolo11\build-b\Release\build.engine"
|
|||
|
|
if len(sys.argv) > 1:
|
|||
|
|
engine_file_path = sys.argv[1]
|
|||
|
|
if len(sys.argv) > 2:
|
|||
|
|
PLUGIN_LIBRARY = sys.argv[2]
|
|||
|
|
|
|||
|
|
# 加载插件
|
|||
|
|
ctypes.CDLL(PLUGIN_LIBRARY)
|
|||
|
|
|
|||
|
|
# 初始化推理器
|
|||
|
|
yolo11_wrapper = YoLo11TRT(engine_file_path)
|
|||
|
|
|
|||
|
|
try:
|
|||
|
|
# 读取测试图片
|
|||
|
|
img_path = r"C:\Users\14867\Downloads\1758382029741-frame.jpg" # 替换为你的测试图片路径
|
|||
|
|
frame = cv2.imread(img_path)
|
|||
|
|
|
|||
|
|
if frame is None:
|
|||
|
|
print("[ERROR] 无法读取图片文件:", img_path)
|
|||
|
|
exit(-1)
|
|||
|
|
|
|||
|
|
# 执行推理
|
|||
|
|
start_time = time.time()
|
|||
|
|
batch_image_raw,result_box_list,result_scores_list,result_classid_list, infer_time = yolo11_wrapper.infer([frame])
|
|||
|
|
detected_frame = batch_image_raw[0]
|
|||
|
|
|
|||
|
|
# 显示结果
|
|||
|
|
cv2.putText(detected_frame, f"Infer Time: {infer_time * 1000:.2f}ms",
|
|||
|
|
(10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
|
|||
|
|
cv2.imshow("YOLO11 Detection", detected_frame)
|
|||
|
|
|
|||
|
|
# 保存结果
|
|||
|
|
output_path = img_path.replace(".jpg", "_detected.jpg")
|
|||
|
|
cv2.imwrite(output_path, detected_frame)
|
|||
|
|
print(f"结果已保存到: {output_path}")
|
|||
|
|
|
|||
|
|
cv2.waitKey(0)
|
|||
|
|
cv2.destroyAllWindows()
|
|||
|
|
|
|||
|
|
finally:
|
|||
|
|
yolo11_wrapper.destroy()
|