generated from zhangwei/Matrixai
451 lines
18 KiB
Python
451 lines
18 KiB
Python
|
"""
|
||
|
|
An example that uses TensorRT's Python api to make inferences.
|
||
|
|
"""
|
||
|
|
import ctypes
|
||
|
|
import os
|
||
|
|
import shutil
|
||
|
|
import random
|
||
|
|
import sys
|
||
|
|
import threading
|
||
|
|
import time
|
||
|
|
import cv2
|
||
|
|
import numpy as np
|
||
|
|
import pycuda.autoinit
|
||
|
|
import pycuda.driver as cuda
|
||
|
|
import tensorrt as trt
|
||
|
|
|
||
|
|
CONF_THRESH = 0.5
|
||
|
|
IOU_THRESHOLD = 0.4
|
||
|
|
|
||
|
|
|
||
|
|
def get_img_path_batches(batch_size, img_dir):
|
||
|
|
ret = []
|
||
|
|
batch = []
|
||
|
|
for root, dirs, files in os.walk(img_dir):
|
||
|
|
for name in files:
|
||
|
|
if len(batch) == batch_size:
|
||
|
|
ret.append(batch)
|
||
|
|
batch = []
|
||
|
|
batch.append(os.path.join(root, name))
|
||
|
|
if len(batch) > 0:
|
||
|
|
ret.append(batch)
|
||
|
|
return ret
|
||
|
|
|
||
|
|
def plot_one_box(x, img, color=None, label=None, line_thickness=None):
|
||
|
|
"""
|
||
|
|
description: Plots one bounding box on image img,
|
||
|
|
this function comes from YoLov5 project.
|
||
|
|
param:
|
||
|
|
x: a box likes [x1,y1,x2,y2]
|
||
|
|
img: a opencv image object
|
||
|
|
color: color to draw rectangle, such as (0,255,0)
|
||
|
|
label: str
|
||
|
|
line_thickness: int
|
||
|
|
return:
|
||
|
|
no return
|
||
|
|
|
||
|
|
"""
|
||
|
|
tl = (
|
||
|
|
line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1
|
||
|
|
) # line/font thickness
|
||
|
|
color = color or [random.randint(0, 255) for _ in range(3)]
|
||
|
|
c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
|
||
|
|
cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
|
||
|
|
if label:
|
||
|
|
tf = max(tl - 1, 1) # font thickness
|
||
|
|
t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
|
||
|
|
c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
|
||
|
|
cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled
|
||
|
|
cv2.putText(
|
||
|
|
img,
|
||
|
|
label,
|
||
|
|
(c1[0], c1[1] - 2),
|
||
|
|
0,
|
||
|
|
tl / 3,
|
||
|
|
[225, 255, 255],
|
||
|
|
thickness=tf,
|
||
|
|
lineType=cv2.LINE_AA,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
class YoLov5TRT(object):
|
||
|
|
"""
|
||
|
|
description: A YOLOv5 class that warps TensorRT ops, preprocess and postprocess ops.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, engine_file_path):
|
||
|
|
# Create a Context on this device,
|
||
|
|
self.ctx = cuda.Device(0).make_context()
|
||
|
|
stream = cuda.Stream()
|
||
|
|
TRT_LOGGER = trt.Logger(trt.Logger.INFO)
|
||
|
|
runtime = trt.Runtime(TRT_LOGGER)
|
||
|
|
|
||
|
|
# Deserialize the engine from file
|
||
|
|
with open(engine_file_path, "rb") as f:
|
||
|
|
engine = runtime.deserialize_cuda_engine(f.read())
|
||
|
|
context = engine.create_execution_context()
|
||
|
|
|
||
|
|
host_inputs = []
|
||
|
|
cuda_inputs = []
|
||
|
|
host_outputs = []
|
||
|
|
cuda_outputs = []
|
||
|
|
bindings = []
|
||
|
|
|
||
|
|
for binding in engine:
|
||
|
|
print('bingding:', binding, engine.get_binding_shape(binding))
|
||
|
|
size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size
|
||
|
|
dtype = trt.nptype(engine.get_binding_dtype(binding))
|
||
|
|
# Allocate host and device buffers
|
||
|
|
host_mem = cuda.pagelocked_empty(size, dtype)
|
||
|
|
cuda_mem = cuda.mem_alloc(host_mem.nbytes)
|
||
|
|
# Append the device buffer to device bindings.
|
||
|
|
bindings.append(int(cuda_mem))
|
||
|
|
# Append to the appropriate list.
|
||
|
|
if engine.binding_is_input(binding):
|
||
|
|
self.input_w = engine.get_binding_shape(binding)[-1]
|
||
|
|
self.input_h = engine.get_binding_shape(binding)[-2]
|
||
|
|
host_inputs.append(host_mem)
|
||
|
|
cuda_inputs.append(cuda_mem)
|
||
|
|
else:
|
||
|
|
host_outputs.append(host_mem)
|
||
|
|
cuda_outputs.append(cuda_mem)
|
||
|
|
|
||
|
|
# Store
|
||
|
|
self.stream = stream
|
||
|
|
self.context = context
|
||
|
|
self.engine = engine
|
||
|
|
self.host_inputs = host_inputs
|
||
|
|
self.cuda_inputs = cuda_inputs
|
||
|
|
self.host_outputs = host_outputs
|
||
|
|
self.cuda_outputs = cuda_outputs
|
||
|
|
self.bindings = bindings
|
||
|
|
self.batch_size = engine.max_batch_size
|
||
|
|
|
||
|
|
def infer(self, raw_image_generator):
|
||
|
|
threading.Thread.__init__(self)
|
||
|
|
# Make self the active context, pushing it on top of the context stack.
|
||
|
|
self.ctx.push()
|
||
|
|
# Restore
|
||
|
|
stream = self.stream
|
||
|
|
context = self.context
|
||
|
|
engine = self.engine
|
||
|
|
host_inputs = self.host_inputs
|
||
|
|
cuda_inputs = self.cuda_inputs
|
||
|
|
host_outputs = self.host_outputs
|
||
|
|
cuda_outputs = self.cuda_outputs
|
||
|
|
bindings = self.bindings
|
||
|
|
# Do image preprocess
|
||
|
|
batch_image_raw = []
|
||
|
|
batch_origin_h = []
|
||
|
|
batch_origin_w = []
|
||
|
|
batch_input_image = np.empty(shape=[self.batch_size, 3, self.input_h, self.input_w])
|
||
|
|
for i, image_raw in enumerate(raw_image_generator):
|
||
|
|
input_image, image_raw, origin_h, origin_w = self.preprocess_image(image_raw)
|
||
|
|
batch_image_raw.append(image_raw)
|
||
|
|
batch_origin_h.append(origin_h)
|
||
|
|
batch_origin_w.append(origin_w)
|
||
|
|
np.copyto(batch_input_image[i], input_image)
|
||
|
|
batch_input_image = np.ascontiguousarray(batch_input_image)
|
||
|
|
|
||
|
|
# Copy input image to host buffer
|
||
|
|
np.copyto(host_inputs[0], batch_input_image.ravel())
|
||
|
|
start = time.time()
|
||
|
|
# Transfer input data to the GPU.
|
||
|
|
cuda.memcpy_htod_async(cuda_inputs[0], host_inputs[0], stream)
|
||
|
|
# Run inference.
|
||
|
|
context.execute_async(batch_size=self.batch_size, bindings=bindings, stream_handle=stream.handle)
|
||
|
|
# Transfer predictions back from the GPU.
|
||
|
|
cuda.memcpy_dtoh_async(host_outputs[0], cuda_outputs[0], stream)
|
||
|
|
# Synchronize the stream
|
||
|
|
stream.synchronize()
|
||
|
|
end = time.time()
|
||
|
|
# Remove any context from the top of the context stack, deactivating it.
|
||
|
|
self.ctx.pop()
|
||
|
|
# Here we use the first row of output in that batch_size = 1
|
||
|
|
output = host_outputs[0]
|
||
|
|
# Do postprocess
|
||
|
|
for i in range(self.batch_size):
|
||
|
|
result_boxes, result_scores, result_classid = self.post_process(
|
||
|
|
output[i * 6001: (i + 1) * 6001], batch_origin_h[i], batch_origin_w[i]
|
||
|
|
)
|
||
|
|
# Draw rectangles and labels on the original image
|
||
|
|
for j in range(len(result_boxes)):
|
||
|
|
box = result_boxes[j]
|
||
|
|
plot_one_box(
|
||
|
|
box,
|
||
|
|
batch_image_raw[i],
|
||
|
|
label="{}:{:.2f}".format(
|
||
|
|
categories[int(result_classid[j])], result_scores[j]
|
||
|
|
),
|
||
|
|
)
|
||
|
|
return batch_image_raw, end - start
|
||
|
|
|
||
|
|
def destroy(self):
|
||
|
|
# Remove any context from the top of the context stack, deactivating it.
|
||
|
|
self.ctx.pop()
|
||
|
|
|
||
|
|
def get_raw_image(self, image_path_batch):
|
||
|
|
"""
|
||
|
|
description: Read an image from image path
|
||
|
|
"""
|
||
|
|
for img_path in image_path_batch:
|
||
|
|
yield cv2.imread(img_path)
|
||
|
|
|
||
|
|
def get_raw_image_zeros(self, image_path_batch=None):
|
||
|
|
"""
|
||
|
|
description: Ready data for warmup
|
||
|
|
"""
|
||
|
|
for _ in range(self.batch_size):
|
||
|
|
yield np.zeros([self.input_h, self.input_w, 3], dtype=np.uint8)
|
||
|
|
|
||
|
|
def preprocess_image(self, raw_bgr_image):
|
||
|
|
"""
|
||
|
|
description: Convert BGR image to RGB,
|
||
|
|
resize and pad it to target size, normalize to [0,1],
|
||
|
|
transform to NCHW format.
|
||
|
|
param:
|
||
|
|
input_image_path: str, image path
|
||
|
|
return:
|
||
|
|
image: the processed image
|
||
|
|
image_raw: the original image
|
||
|
|
h: original height
|
||
|
|
w: original width
|
||
|
|
"""
|
||
|
|
image_raw = raw_bgr_image
|
||
|
|
h, w, c = image_raw.shape
|
||
|
|
image = cv2.cvtColor(image_raw, cv2.COLOR_BGR2RGB)
|
||
|
|
# Calculate widht and height and paddings
|
||
|
|
r_w = self.input_w / w
|
||
|
|
r_h = self.input_h / h
|
||
|
|
if r_h > r_w:
|
||
|
|
tw = self.input_w
|
||
|
|
th = int(r_w * h)
|
||
|
|
tx1 = tx2 = 0
|
||
|
|
ty1 = int((self.input_h - th) / 2)
|
||
|
|
ty2 = self.input_h - th - ty1
|
||
|
|
else:
|
||
|
|
tw = int(r_h * w)
|
||
|
|
th = self.input_h
|
||
|
|
tx1 = int((self.input_w - tw) / 2)
|
||
|
|
tx2 = self.input_w - tw - tx1
|
||
|
|
ty1 = ty2 = 0
|
||
|
|
# Resize the image with long side while maintaining ratio
|
||
|
|
image = cv2.resize(image, (tw, th))
|
||
|
|
# Pad the short side with (128,128,128)
|
||
|
|
image = cv2.copyMakeBorder(
|
||
|
|
image, ty1, ty2, tx1, tx2, cv2.BORDER_CONSTANT, None, (128, 128, 128)
|
||
|
|
)
|
||
|
|
image = image.astype(np.float32)
|
||
|
|
# Normalize to [0,1]
|
||
|
|
image /= 255.0
|
||
|
|
# HWC to CHW format:
|
||
|
|
image = np.transpose(image, [2, 0, 1])
|
||
|
|
# CHW to NCHW format
|
||
|
|
image = np.expand_dims(image, axis=0)
|
||
|
|
# Convert the image to row-major order, also known as "C order":
|
||
|
|
image = np.ascontiguousarray(image)
|
||
|
|
return image, image_raw, h, w
|
||
|
|
|
||
|
|
def xywh2xyxy(self, origin_h, origin_w, x):
|
||
|
|
"""
|
||
|
|
description: Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
|
||
|
|
param:
|
||
|
|
origin_h: height of original image
|
||
|
|
origin_w: width of original image
|
||
|
|
x: A boxes numpy, each row is a box [center_x, center_y, w, h]
|
||
|
|
return:
|
||
|
|
y: A boxes numpy, each row is a box [x1, y1, x2, y2]
|
||
|
|
"""
|
||
|
|
y = np.zeros_like(x)
|
||
|
|
r_w = self.input_w / origin_w
|
||
|
|
r_h = self.input_h / origin_h
|
||
|
|
if r_h > r_w:
|
||
|
|
y[:, 0] = x[:, 0] - x[:, 2] / 2
|
||
|
|
y[:, 2] = x[:, 0] + x[:, 2] / 2
|
||
|
|
y[:, 1] = x[:, 1] - x[:, 3] / 2 - (self.input_h - r_w * origin_h) / 2
|
||
|
|
y[:, 3] = x[:, 1] + x[:, 3] / 2 - (self.input_h - r_w * origin_h) / 2
|
||
|
|
y /= r_w
|
||
|
|
else:
|
||
|
|
y[:, 0] = x[:, 0] - x[:, 2] / 2 - (self.input_w - r_h * origin_w) / 2
|
||
|
|
y[:, 2] = x[:, 0] + x[:, 2] / 2 - (self.input_w - r_h * origin_w) / 2
|
||
|
|
y[:, 1] = x[:, 1] - x[:, 3] / 2
|
||
|
|
y[:, 3] = x[:, 1] + x[:, 3] / 2
|
||
|
|
y /= r_h
|
||
|
|
|
||
|
|
return y
|
||
|
|
|
||
|
|
def post_process(self, output, origin_h, origin_w):
|
||
|
|
"""
|
||
|
|
description: postprocess the prediction
|
||
|
|
param:
|
||
|
|
output: A numpy likes [num_boxes,cx,cy,w,h,conf,cls_id, cx,cy,w,h,conf,cls_id, ...]
|
||
|
|
origin_h: height of original image
|
||
|
|
origin_w: width of original image
|
||
|
|
return:
|
||
|
|
result_boxes: finally boxes, a boxes numpy, each row is a box [x1, y1, x2, y2]
|
||
|
|
result_scores: finally scores, a numpy, each element is the score correspoing to box
|
||
|
|
result_classid: finally classid, a numpy, each element is the classid correspoing to box
|
||
|
|
"""
|
||
|
|
# Get the num of boxes detected
|
||
|
|
num = int(output[0])
|
||
|
|
# Reshape to a two dimentional ndarray
|
||
|
|
pred = np.reshape(output[1:], (-1, 6))[:num, :]
|
||
|
|
# Do nms
|
||
|
|
boxes = self.non_max_suppression(pred, origin_h, origin_w, conf_thres=CONF_THRESH, nms_thres=IOU_THRESHOLD)
|
||
|
|
result_boxes = boxes[:, :4] if len(boxes) else np.array([])
|
||
|
|
result_scores = boxes[:, 4] if len(boxes) else np.array([])
|
||
|
|
result_classid = boxes[:, 5] if len(boxes) else np.array([])
|
||
|
|
return result_boxes, result_scores, result_classid
|
||
|
|
|
||
|
|
def bbox_iou(self, box1, box2, x1y1x2y2=True):
|
||
|
|
"""
|
||
|
|
description: compute the IoU of two bounding boxes
|
||
|
|
param:
|
||
|
|
box1: A box coordinate (can be (x1, y1, x2, y2) or (x, y, w, h))
|
||
|
|
box2: A box coordinate (can be (x1, y1, x2, y2) or (x, y, w, h))
|
||
|
|
x1y1x2y2: select the coordinate format
|
||
|
|
return:
|
||
|
|
iou: computed iou
|
||
|
|
"""
|
||
|
|
if not x1y1x2y2:
|
||
|
|
# Transform from center and width to exact coordinates
|
||
|
|
b1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2
|
||
|
|
b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2
|
||
|
|
b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2
|
||
|
|
b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2
|
||
|
|
else:
|
||
|
|
# Get the coordinates of bounding boxes
|
||
|
|
b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3]
|
||
|
|
b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3]
|
||
|
|
|
||
|
|
# Get the coordinates of the intersection rectangle
|
||
|
|
inter_rect_x1 = np.maximum(b1_x1, b2_x1)
|
||
|
|
inter_rect_y1 = np.maximum(b1_y1, b2_y1)
|
||
|
|
inter_rect_x2 = np.minimum(b1_x2, b2_x2)
|
||
|
|
inter_rect_y2 = np.minimum(b1_y2, b2_y2)
|
||
|
|
# Intersection area
|
||
|
|
inter_area = np.clip(inter_rect_x2 - inter_rect_x1 + 1, 0, None) * \
|
||
|
|
np.clip(inter_rect_y2 - inter_rect_y1 + 1, 0, None)
|
||
|
|
# Union Area
|
||
|
|
b1_area = (b1_x2 - b1_x1 + 1) * (b1_y2 - b1_y1 + 1)
|
||
|
|
b2_area = (b2_x2 - b2_x1 + 1) * (b2_y2 - b2_y1 + 1)
|
||
|
|
|
||
|
|
iou = inter_area / (b1_area + b2_area - inter_area + 1e-16)
|
||
|
|
|
||
|
|
return iou
|
||
|
|
|
||
|
|
def non_max_suppression(self, prediction, origin_h, origin_w, conf_thres=0.5, nms_thres=0.4):
|
||
|
|
"""
|
||
|
|
description: Removes detections with lower object confidence score than 'conf_thres' and performs
|
||
|
|
Non-Maximum Suppression to further filter detections.
|
||
|
|
param:
|
||
|
|
prediction: detections, (x1, y1, x2, y2, conf, cls_id)
|
||
|
|
origin_h: original image height
|
||
|
|
origin_w: original image width
|
||
|
|
conf_thres: a confidence threshold to filter detections
|
||
|
|
nms_thres: a iou threshold to filter detections
|
||
|
|
return:
|
||
|
|
boxes: output after nms with the shape (x1, y1, x2, y2, conf, cls_id)
|
||
|
|
"""
|
||
|
|
# Get the boxes that score > CONF_THRESH
|
||
|
|
boxes = prediction[prediction[:, 4] >= conf_thres]
|
||
|
|
# Trandform bbox from [center_x, center_y, w, h] to [x1, y1, x2, y2]
|
||
|
|
boxes[:, :4] = self.xywh2xyxy(origin_h, origin_w, boxes[:, :4])
|
||
|
|
# clip the coordinates
|
||
|
|
boxes[:, 0] = np.clip(boxes[:, 0], 0, origin_w -1)
|
||
|
|
boxes[:, 2] = np.clip(boxes[:, 2], 0, origin_w -1)
|
||
|
|
boxes[:, 1] = np.clip(boxes[:, 1], 0, origin_h -1)
|
||
|
|
boxes[:, 3] = np.clip(boxes[:, 3], 0, origin_h -1)
|
||
|
|
# Object confidence
|
||
|
|
confs = boxes[:, 4]
|
||
|
|
# Sort by the confs
|
||
|
|
boxes = boxes[np.argsort(-confs)]
|
||
|
|
# Perform non-maximum suppression
|
||
|
|
keep_boxes = []
|
||
|
|
while boxes.shape[0]:
|
||
|
|
large_overlap = self.bbox_iou(np.expand_dims(boxes[0, :4], 0), boxes[:, :4]) > nms_thres
|
||
|
|
label_match = boxes[0, -1] == boxes[:, -1]
|
||
|
|
# Indices of boxes with lower confidence scores, large IOUs and matching labels
|
||
|
|
invalid = large_overlap & label_match
|
||
|
|
keep_boxes += [boxes[0]]
|
||
|
|
boxes = boxes[~invalid]
|
||
|
|
boxes = np.stack(keep_boxes, 0) if len(keep_boxes) else np.array([])
|
||
|
|
return boxes
|
||
|
|
|
||
|
|
|
||
|
|
class inferThread(threading.Thread):
|
||
|
|
def __init__(self, yolov5_wrapper, image_path_batch):
|
||
|
|
threading.Thread.__init__(self)
|
||
|
|
self.yolov5_wrapper = yolov5_wrapper
|
||
|
|
self.image_path_batch = image_path_batch
|
||
|
|
|
||
|
|
def run(self):
|
||
|
|
batch_image_raw, use_time = self.yolov5_wrapper.infer(self.yolov5_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, yolov5_wrapper):
|
||
|
|
threading.Thread.__init__(self)
|
||
|
|
self.yolov5_wrapper = yolov5_wrapper
|
||
|
|
|
||
|
|
def run(self):
|
||
|
|
batch_image_raw, use_time = self.yolov5_wrapper.infer(self.yolov5_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 = "build/yolov5s.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", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
|
||
|
|
"fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
|
||
|
|
"elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
|
||
|
|
"skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
|
||
|
|
"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
|
||
|
|
"sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
|
||
|
|
"potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
|
||
|
|
"microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear",
|
||
|
|
"hair drier", "toothbrush"]
|
||
|
|
|
||
|
|
if os.path.exists('output/'):
|
||
|
|
shutil.rmtree('output/')
|
||
|
|
os.makedirs('output/')
|
||
|
|
# a YoLov5TRT instance
|
||
|
|
yolov5_wrapper = YoLov5TRT(engine_file_path)
|
||
|
|
try:
|
||
|
|
print('batch size is', yolov5_wrapper.batch_size)
|
||
|
|
|
||
|
|
image_dir = "samples/"
|
||
|
|
image_path_batches = get_img_path_batches(yolov5_wrapper.batch_size, image_dir)
|
||
|
|
|
||
|
|
for i in range(10):
|
||
|
|
# create a new thread to do warm_up
|
||
|
|
thread1 = warmUpThread(yolov5_wrapper)
|
||
|
|
thread1.start()
|
||
|
|
thread1.join()
|
||
|
|
for batch in image_path_batches:
|
||
|
|
# create a new thread to do inference
|
||
|
|
thread1 = inferThread(yolov5_wrapper, batch)
|
||
|
|
thread1.start()
|
||
|
|
thread1.join()
|
||
|
|
finally:
|
||
|
|
# destroy the instance
|
||
|
|
yolov5_wrapper.destroy()
|