503 lines
19 KiB
Python
503 lines
19 KiB
Python
"""
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An example that uses TensorRT's Python api to make inferences.
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"""
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import ctypes
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import os
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import shutil
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import random
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import sys
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import threading
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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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keypoint_pairs = [
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(0, 1), (0, 2), (0, 5), (0, 6), (1, 2),
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(1, 3), (2, 4), (5, 6), (5, 7), (5, 11),
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(6, 8), (6, 12), (7, 9), (8, 10), (11, 12),
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(11, 13), (12, 14), (13, 15), (14, 16)
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]
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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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"""
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description: Plots one bounding box on image img,
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this function comes from YoLov8 project.
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param:
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x: a box likes [x1,y1,x2,y2]
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img: a opencv image object
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color: color to draw rectangle, such as (0,255,0)
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label: str
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line_thickness: int
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return:
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no return
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"""
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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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) # line/font thickness
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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) # font thickness
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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) # filled
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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 YoLov8TRT(object):
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"""
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description: A YOLOv8 class that warps TensorRT ops, preprocess and postprocess ops.
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"""
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def __init__(self, engine_file_path):
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# Create a Context on this device,
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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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# Deserialize the engine from file
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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('bingding:', binding, engine.get_binding_shape(binding))
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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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# Allocate host and device buffers
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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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# Append the device buffer to device bindings.
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bindings.append(int(cuda_mem))
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# Append to the appropriate list.
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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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# Store
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self.stream = stream
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self.context = context
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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.batch_size = engine.max_batch_size
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self.det_output_size = host_outputs[0].shape[0]
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def infer(self, raw_image_generator):
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threading.Thread.__init__(self)
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# Make self the active context, pushing it on top of the context stack.
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self.ctx.push()
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# Restore
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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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# Do image preprocess
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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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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],
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input_image)
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batch_input_image = np.ascontiguousarray(batch_input_image)
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# Copy input image to host buffer
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np.copyto(host_inputs[0], batch_input_image.ravel())
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start = time.time()
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# Transfer input data to the GPU.
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cuda.memcpy_htod_async(cuda_inputs[0], host_inputs[0], stream)
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# Run inference.
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context.execute_async(batch_size=self.batch_size, bindings=bindings, stream_handle=stream.handle)
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# Transfer predictions back from the GPU.
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cuda.memcpy_dtoh_async(host_outputs[0], cuda_outputs[0], stream)
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# Synchronize the stream
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stream.synchronize()
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end = time.time()
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# Remove any context from the top of the context stack, deactivating it.
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self.ctx.pop()
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# Here we use the first row of output in that batch_size = 1
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output = host_outputs[0]
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# Do postprocess
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for i in range(self.batch_size):
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result_boxes, result_scores, result_classid, keypoints = self.post_process(
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output[i * (self.det_output_size): (i + 1) * (self.det_output_size)],
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batch_origin_h[i], batch_origin_w[i]
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)
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# Draw rectangles and labels on the original image
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for j in range(len(result_boxes)):
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box = result_boxes[j]
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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(
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categories[int(result_classid[j])], result_scores[j]
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),
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)
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num_keypoints = len(keypoints[j]) // 3
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points = []
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for k in range(num_keypoints):
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x = keypoints[j][k * 3]
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y = keypoints[j][k * 3 + 1]
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confidence = keypoints[j][k * 3 + 2]
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if confidence > 0:
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points.append((int(x), int(y)))
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else:
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points.append(None)
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# 根据关键点索引对绘制线条
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for pair in keypoint_pairs:
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partA, partB = pair
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if points[partA] and points[partB]:
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cv2.line(batch_image_raw[i], points[partA], points[partB], (0, 255, 0), 2)
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return batch_image_raw, end - start
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def destroy(self):
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# Remove any context from the top of the context stack, deactivating it.
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self.ctx.pop()
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def get_raw_image(self, image_path_batch):
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"""
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description: Read an image from image path
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"""
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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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"""
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description: Ready data for warmup
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"""
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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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"""
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description: Convert BGR image to RGB,
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resize and pad it to target size, normalize to [0,1],
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transform to NCHW format.
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param:
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input_image_path: str, image path
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return:
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image: the processed image
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image_raw: the original image
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h: original height
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w: original width
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"""
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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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# Calculate widht and height and paddings
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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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# Resize the image with long side while maintaining ratio
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image = cv2.resize(image, (tw, th))
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# Pad the short side with (128,128,128)
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image = cv2.copyMakeBorder(
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image, ty1, ty2, tx1, tx2, cv2.BORDER_CONSTANT, None, (128, 128, 128)
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)
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image = image.astype(np.float32)
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# Normalize to [0,1]
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image /= 255.0
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# HWC to CHW format:
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image = np.transpose(image, [2, 0, 1])
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# CHW to NCHW format
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image = np.expand_dims(image, axis=0)
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# Convert the image to row-major order, also known as "C order":
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image = np.ascontiguousarray(image)
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return image, image_raw, h, w
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def xywh2xyxy_with_keypoints(self, origin_h, origin_w, boxes, keypoints):
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n = len(boxes)
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box_array = np.zeros_like(boxes)
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keypoint_array = np.zeros_like(keypoints)
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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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for i in range(n):
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if r_h > r_w:
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box = boxes[i]
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lmk = keypoints[i]
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box_array[i, 0] = box[0] / r_w
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box_array[i, 2] = box[2] / r_w
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box_array[i, 1] = (box[1] - (self.input_h - r_w * origin_h) / 2) / r_w
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box_array[i, 3] = (box[3] - (self.input_h - r_w * origin_h) / 2) / r_w
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for j in range(0, len(lmk), 3):
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keypoint_array[i, j] = lmk[j] / r_w
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keypoint_array[i, j + 1] = (lmk[j + 1] - (self.input_h - r_w * origin_h) / 2) / r_w
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keypoint_array[i, j + 2] = lmk[j + 2]
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else:
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box = boxes[i]
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lmk = keypoints[i]
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box_array[i, 0] = (box[0] - (self.input_w - r_h * origin_w) / 2) / r_h
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box_array[i, 2] = (box[2] - (self.input_w - r_h * origin_w) / 2) / r_h
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box_array[i, 1] = box[1] / r_h
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box_array[i, 3] = box[3] / r_h
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for j in range(0, len(lmk), 3):
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keypoint_array[i, j] = (lmk[j] - (self.input_w - r_h * origin_w) / 2) / r_h
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keypoint_array[i, j + 1] = lmk[j + 1] / r_h
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keypoint_array[i, j + 2] = lmk[j + 2]
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return box_array, keypoint_array
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def post_process(self, output, origin_h, origin_w):
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"""
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description: Post-process the prediction to include pose keypoints
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param:
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output: A numpy array like [num_boxes, cx, cy, w, h, conf,
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cls_id, px1, py1, pconf1,...px17, py17, pconf17] where p denotes pose keypoint
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origin_h: Height of original image
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origin_w: Width of original image
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return:
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result_boxes: Final boxes, a numpy array, each row is a box [x1, y1, x2, y2]
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result_scores: Final scores, a numpy array, each element is the score corresponding to box
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result_classid: Final classID, a numpy array, each element is the classid corresponding to box
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result_keypoints: Final keypoints, a list of numpy arrays,
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each element represents keypoints for a box, shaped as (#keypoints, 3)
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"""
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# Number of values per detection: 38 base values + 17 keypoints * 3 values each + angle
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num_values_per_detection = DET_NUM + SEG_NUM + POSE_NUM + OBB_NUM
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# Get the number of boxes detected
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num = int(output[0])
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# Reshape to a two-dimensional ndarray with the full detection shape
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pred = np.reshape(output[1:], (-1, num_values_per_detection))[:num, :]
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# Perform non-maximum suppression to filter the detections
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boxes = self.non_max_suppression(
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pred[:, :num_values_per_detection], origin_h, origin_w,
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conf_thres=CONF_THRESH, nms_thres=IOU_THRESHOLD)
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# Extract the bounding boxes, confidence scores, and class IDs
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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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result_keypoints = boxes[:, -POSE_NUM-1:-1] if len(boxes) else np.array([])
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# Return the post-processed results including keypoints
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return result_boxes, result_scores, result_classid, result_keypoints
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def bbox_iou(self, box1, box2, x1y1x2y2=True):
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"""
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description: compute the IoU of two bounding boxes
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param:
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box1: A box coordinate (can be (x1, y1, x2, y2) or (x, y, w, h))
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box2: A box coordinate (can be (x1, y1, x2, y2) or (x, y, w, h))
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x1y1x2y2: select the coordinate format
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return:
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iou: computed iou
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"""
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if not x1y1x2y2:
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# Transform from center and width to exact coordinates
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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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# Get the coordinates of bounding boxes
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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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# Get the coordinates of the intersection rectangle
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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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# Intersection area
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inter_area = np.clip(
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inter_rect_x2 - inter_rect_x1 + 1, 0, None) * np.clip(inter_rect_y2 - inter_rect_y1 + 1, 0, None)
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# Union Area
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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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"""
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description: Removes detections with lower object confidence score than 'conf_thres' and performs
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Non-Maximum Suppression to further filter detections.
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param:
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prediction: detections, (x1, y1, x2, y2, conf, cls_id)
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origin_h: original image height
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origin_w: original image width
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conf_thres: a confidence threshold to filter detections
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nms_thres: a iou threshold to filter detections
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return:
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boxes: output after nms with the shape (x1, y1, x2, y2, conf, cls_id)
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"""
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# Get the boxes that score > CONF_THRESH
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boxes = prediction[prediction[:, 4] >= conf_thres]
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# Trandform bbox from [center_x, center_y, w, h] to [x1, y1, x2, y2]
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res_array = np.copy(boxes)
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box_pred_deep_copy = np.copy(boxes[:, :4])
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keypoints_pred_deep_copy = np.copy(boxes[:, -POSE_NUM-1:-1])
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res_box, res_keypoints = self.xywh2xyxy_with_keypoints(
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origin_h, origin_w, box_pred_deep_copy, keypoints_pred_deep_copy)
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res_array[:, :4] = res_box
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res_array[:, -POSE_NUM-1:-1] = res_keypoints
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# clip the coordinates
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res_array[:, 0] = np.clip(res_array[:, 0], 0, origin_w - 1)
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res_array[:, 2] = np.clip(res_array[:, 2], 0, origin_w - 1)
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res_array[:, 1] = np.clip(res_array[:, 1], 0, origin_h - 1)
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res_array[:, 3] = np.clip(res_array[:, 3], 0, origin_h - 1)
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# Object confidence
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confs = res_array[:, 4]
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# Sort by the confs
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res_array = res_array[np.argsort(-confs)]
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# Perform non-maximum suppression
|
|
keep_res_array = []
|
|
while res_array.shape[0]:
|
|
large_overlap = self.bbox_iou(np.expand_dims(res_array[0, :4], 0), res_array[:, :4]) > nms_thres
|
|
label_match = res_array[0, 5] == res_array[:, 5]
|
|
invalid = large_overlap & label_match
|
|
keep_res_array.append(res_array[0])
|
|
res_array = res_array[~invalid]
|
|
|
|
res_array = np.stack(keep_res_array, 0) if len(keep_res_array) else np.array([])
|
|
return res_array
|
|
|
|
|
|
class inferThread(threading.Thread):
|
|
def __init__(self, yolov8_wrapper, image_path_batch):
|
|
threading.Thread.__init__(self)
|
|
self.yolov8_wrapper = yolov8_wrapper
|
|
self.image_path_batch = image_path_batch
|
|
|
|
def run(self):
|
|
batch_image_raw, use_time = self.yolov8_wrapper.infer(self.yolov8_wrapper.get_raw_image(self.image_path_batch))
|
|
for i, img_path in enumerate(self.image_path_batch):
|
|
parent, filename = os.path.split(img_path)
|
|
save_name = os.path.join('output', filename)
|
|
# Save image
|
|
|
|
cv2.imwrite(save_name, batch_image_raw[i])
|
|
print('input->{}, time->{:.2f}ms, saving into output/'.format(self.image_path_batch, use_time * 1000))
|
|
|
|
|
|
class warmUpThread(threading.Thread):
|
|
def __init__(self, yolov8_wrapper):
|
|
threading.Thread.__init__(self)
|
|
self.yolov8_wrapper = yolov8_wrapper
|
|
|
|
def run(self):
|
|
batch_image_raw, use_time = self.yolov8_wrapper.infer(self.yolov8_wrapper.get_raw_image_zeros())
|
|
print('warm_up->{}, time->{:.2f}ms'.format(batch_image_raw[0].shape, use_time * 1000))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
# load custom plugin and engine
|
|
PLUGIN_LIBRARY = "./build/libmyplugins.so"
|
|
engine_file_path = "yolov8n-pose.engine"
|
|
|
|
if len(sys.argv) > 1:
|
|
engine_file_path = sys.argv[1]
|
|
if len(sys.argv) > 2:
|
|
PLUGIN_LIBRARY = sys.argv[2]
|
|
|
|
ctypes.CDLL(PLUGIN_LIBRARY)
|
|
|
|
# load coco labels
|
|
|
|
categories = ["person"]
|
|
|
|
if os.path.exists('output/'):
|
|
shutil.rmtree('output/')
|
|
os.makedirs('output/')
|
|
# a YoLov8TRT instance
|
|
yolov8_wrapper = YoLov8TRT(engine_file_path)
|
|
try:
|
|
print('batch size is', yolov8_wrapper.batch_size)
|
|
|
|
image_dir = "images/"
|
|
image_path_batches = get_img_path_batches(yolov8_wrapper.batch_size, image_dir)
|
|
|
|
for i in range(10):
|
|
# create a new thread to do warm_up
|
|
thread1 = warmUpThread(yolov8_wrapper)
|
|
thread1.start()
|
|
thread1.join()
|
|
for batch in image_path_batches:
|
|
# create a new thread to do inference
|
|
thread1 = inferThread(yolov8_wrapper, batch)
|
|
thread1.start()
|
|
thread1.join()
|
|
finally:
|
|
# destroy the instance
|
|
yolov8_wrapper.destroy()
|