python - 使用带有 zmq 和 opencv 的暗网

Question

伙计们，我在尝试封装我的神经网络以扫描从远程树莓派流式传输到我的处理服务器的图像时遇到了非常糟糕的时间。我已经设法创建了一个客户端，通过imagezmq库发送 Raspberry Pi 相机的图像，到目前为止，我正试图让我的流通过Yolov4网络，因为我已经在服务器端设置了所有内容。

我唯一的问题是获取 zeromq 流并将其放入 cv2.VideoCapture 函数中。我已遵循本指南，我正在尝试使用此视频代码示例处理我的流，但我没有成功...

我绝望了。

客户端代码（覆盆子）

import imagezmq
import argparse
import socket
import time

# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-s", "--server-ip", required=True,
    help="ip address of the server to which the client will connect")
args = vars(ap.parse_args())
# initialize the ImageSender object with the socket address of the
# server
sender = imagezmq.ImageSender(connect_to="tcp://{}:5555".format(
    args["server_ip"]))


# get the host name, initialize the video stream, and allow the
# camera sensor to warmup
rpiName = socket.gethostname()
vs = VideoStream(usePiCamera=True, resolution=(800, 600)).start()
time.sleep(2.0)
 
while True:
    # read the frame from the camera and send it to the server
    frame = vs.read()
    sender.send_image(rpiName, frame)

服务器代码（主 PC）

import random
import os
import cv2
import time
import darknet
import argparse
from threading import Thread, enumerate
from queue import Queue
import imagezmq





# construct the argument parser and parse the arguments

# initialize the ImageHub object
imageHub = imagezmq.ImageHub()
# initialize the list of class labels MobileNet SSD was trained to
# detect, then generate a set of bounding box colors for each class
# load our serialized model from disk
print("[INFO] loading model...")


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

path_cfg = "cfg/coco.data"
detector_cfg = "backup/custom-yolov4-detector.cfg"
detector_weights = "backup/custom-yolov4-detector_best.weights"

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(detector_cfg):
        raise(ValueError("Invalid config path {}".format(os.path.abspath(args.config_file))))
    if not os.path.exists(detector_weights):
        raise(ValueError("Invalid weight path {}".format(os.path.abspath(args.weights))))
    if not os.path.exists(path_cfg):
        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 = imageHub.recv_image()
        imageHub.send_reply(b'OK')
        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)
        darknet.copy_image_from_bytes(darknet_image, frame_resized.tobytes())
        darknet_image_queue.put(darknet_image)
    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
        )
    # Darknet doesn't accept numpy images.
    # Create one with image we reuse for each detect
    width = darknet.network_width(network)
    height = darknet.network_height(network)
    darknet_image = darknet.make_image(width, height, 3)
    frame = imageHub.recv_image()
    cap = cv2.imdecode("Output Frame", frame)
    cv2.waitKey(1)
    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()

标签： pythonopencvzeromqdarknet