我正在尝试从这里运行YOLO的darknet_video.py脚本
在Jetson(nano和xavier NX(中。代码在一个纳米级中运行良好，但在另一个纳米和NX中运行不好。该脚本在Ubuntu 18.04(Jetpack(中使用以下命令运行
python3 darknet_video.py --input test.mp4 --out_filename out1.txt --weights yolov3-tiny.weights --ext_output --config_file yolov3-tiny.cfg --data_file coco.data --thresh 0.2
我得到以下错误：
JPEG parameter struct mismatch: library thinks size is 584, caller expects 728
pure virtual method called
terminate called without an active exception
Aborted (core dumped)
由于它在一个nano中运行良好，可能是依赖性问题，以下是darknet_video.py 中的代码
from ctypes import *
import random
import os
import cv2
import time
import darknet
import argparse
from threading import Thread, enumerate
from queue import Queue
def parser():
parser = argparse.ArgumentParser(description="YOLO Object Detection")
parser.add_argument("--input", type=str, default=0,
help="video source. If empty, uses webcam 0 stream")
parser.add_argument("--out_filename", type=str, default="",
help="inference video name. Not saved if empty")
parser.add_argument("--weights", default="yolov4.weights",
help="yolo weights path")
parser.add_argument("--dont_show", action='store_true',
help="windown inference display. For headless systems")
parser.add_argument("--ext_output", action='store_true',
help="display bbox coordinates of detected objects")
parser.add_argument("--config_file", default="./cfg/yolov4.cfg",
help="path to config file")
parser.add_argument("--data_file", default="./cfg/coco.data",
help="path to data file")
parser.add_argument("--thresh", type=float, default=.25,
help="remove detections with confidence below this value")
return parser.parse_args()
def str2int(video_path):
"""
argparse returns and string althout webcam uses int (0, 1 ...)
Cast to int if needed
"""
try:
return int(video_path)
except ValueError:
return video_path
def check_arguments_errors(args):
assert 0 < args.thresh < 1, "Threshold should be a float between zero and one (non-inclusive)"
if not os.path.exists(args.config_file):
raise(ValueError("Invalid config path {}".format(os.path.abspath(args.config_file))))
if not os.path.exists(args.weights):
raise(ValueError("Invalid weight path {}".format(os.path.abspath(args.weights))))
if not os.path.exists(args.data_file):
raise(ValueError("Invalid data file path {}".format(os.path.abspath(args.data_file))))
if str2int(args.input) == str and not os.path.exists(args.input):
raise(ValueError("Invalid video path {}".format(os.path.abspath(args.input))))
def set_saved_video(input_video, output_video, size):
fourcc = cv2.VideoWriter_fourcc(*"MJPG")
fps = int(input_video.get(cv2.CAP_PROP_FPS))
video = cv2.VideoWriter(output_video, fourcc, fps, size)
return video
def video_capture(frame_queue, darknet_image_queue):
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame_resized = cv2.resize(frame_rgb, (width, height),
interpolation=cv2.INTER_LINEAR)
frame_queue.put(frame_resized)
img_for_detect = darknet.make_image(width, height, 3)
darknet.copy_image_from_bytes(img_for_detect, frame_resized.tobytes())
darknet_image_queue.put(img_for_detect)
cap.release()
def inference(darknet_image_queue, detections_queue, fps_queue):
while cap.isOpened():
darknet_image = darknet_image_queue.get()
prev_time = time.time()
detections = darknet.detect_image(network, class_names, darknet_image, thresh=args.thresh)
detections_queue.put(detections)
fps = int(1/(time.time() - prev_time))
fps_queue.put(fps)
print("FPS: {}".format(fps))
darknet.print_detections(detections, args.ext_output)
darknet.free_image(darknet_image)
cap.release()
def drawing(frame_queue, detections_queue, fps_queue):
random.seed(3) # deterministic bbox colors
video = set_saved_video(cap, args.out_filename, (width, height))
while cap.isOpened():
frame_resized = frame_queue.get()
detections = detections_queue.get()
fps = fps_queue.get()
if frame_resized is not None:
image = darknet.draw_boxes(detections, frame_resized, class_colors)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
if args.out_filename is not None:
video.write(image)
if not args.dont_show:
cv2.imshow('Inference', image)
if cv2.waitKey(fps) == 27:
break
cap.release()
video.release()
cv2.destroyAllWindows()
if __name__ == '__main__':
frame_queue = Queue()
darknet_image_queue = Queue(maxsize=1)
detections_queue = Queue(maxsize=1)
fps_queue = Queue(maxsize=1)
args = parser()
check_arguments_errors(args)
network, class_names, class_colors = darknet.load_network(
args.config_file,
args.data_file,
args.weights,
batch_size=1
)
width = darknet.network_width(network)
height = darknet.network_height(network)
input_path = str2int(args.input)
cap = cv2.VideoCapture(input_path)
Thread(target=video_capture, args=(frame_queue, darknet_image_queue)).start()
Thread(target=inference, args=(darknet_image_queue, detections_queue, fps_queue)).start()
Thread(target=drawing, args=(frame_queue, detections_queue, fps_queue)).start()
任何想法都将不胜感激。
JPEG参数结构不匹配：库认为大小为584，调用方期望728
这是关于应用程序和低级别库使用的jpeglib.h
App是用不同的jpeglib.h编译的，而低级别库是用不同jpeglib.h编译的，在这种情况下，其在该头文件中的j_decompress_ptr在这两个不同jpeglib.h文件中的结构不同。
确保您有低级别的lib(可能是libjpeg-8b(及其客户端使用相同的libjpeg.h
请删除所有已安装的libjpeg软件包，只安装最新的软件包，然后重试。