在OpenCV中运行的神经网络时：“（-215）pbBlob.raw_data_type（）==朱古力:: FLOAT16在功能blobFromProto错误”如何解决，

Question

我目前正在使用Nvidia的数字来训练CNN对目标检测的自定义数据集，并最终我想运行在Nvidia的杰特森TX2该网络。我跟着建议说明从泊坞窗下载数字图像，我能够成功地培养出网络合理准确。但是，当我尝试运行我的网络中使用Python OpenCV中，我得到这个错误，

“错误：（-215）pbBlob.raw_data_type（）== CAFFE :: FLOAT16在功能blobFromProto”

我已经在其他几个线程，这是由于DIGITS存储其网络的形式与OpenCV中的DNN功能不兼容这一事实读取。

训练我的网络之前，我曾尝试选择在应该使与其他软件的网络兼容的数字，但是这似乎并没有改变网络在所有的选项，并运行我的Python脚本时，我得到了同样的错误。这是我运行创建错误的脚本（它来自本教程https://www.pyimagesearch.com/2017/09/11/object-detection-with-deep-learning-and-opencv/）

# import the necessary packages
import numpy as np
import argparse
import cv2

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
help="path to input image")
ap.add_argument("-p", "--prototxt", required=True,
help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m", "--model", required=True,
help="path to Caffe pre-trained model")
ap.add_argument("-c", "--confidence", type=float, default=0.2,
help="minimum probability to filter weak detections")
args = vars(ap.parse_args())

# initialize the list of class labels MobileNet SSD was trained to
# detect, then generate a set of bounding box colors for each class
CLASSES = ["dontcare", "HatchPanel"]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
# load our serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])

# load the input image and construct an input blob for the image
# by resizing to a fixed 300x300 pixels and then normalizing it
# (note: normalization is done via the authors of the MobileNet SSD
# implementation)
image = cv2.imread(args["image"])
(h, w) = image.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 0.007843,
    (300, 300), 127.5)
# pass the blob through the network and obtain the detections and
# predictions
print("[INFO] computing object detections...")
net.setInput(blob)
detections = net.forward()

# loop over the detections
for i in np.arange(0, detections.shape[2]):
    # extract the confidence (i.e., probability) associated with the  
    # prediction
    confidence = detections[0, 0, i, 2]

    # filter out weak detections by ensuring the `confidence` is
    # greater than the minimum confidence
    if confidence > args["confidence"]:
        # extract the index of the class label from the `detections`,
        # then compute the (x, y)-coordinates of the bounding box for
        # the object
        idx = int(detections[0, 0, i, 1])
        box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
        (startX, startY, endX, endY) = box.astype("int")

        # display the prediction
        label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100)
        print("[INFO] {}".format(label))
        cv2.rectangle(image, (startX, startY), (endX, endY),
            COLORS[idx], 2)
        y = startY - 15 if startY - 15 > 15 else startY + 15
        cv2.putText(image, label, (startX, y),
            cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
# show the output image
cv2.imshow("Output", image)
cv2.waitKey(0)

这应该输出在调用脚本中的图像指定，绘制在图像上的神经网络的输出。但是相反，脚本与之前提到的错误崩溃。我所看到的其他线程与具有同样的错误的人，但作为然而，他们在那个以数字的当前版本有效的解决方案已经抵达。

我完全设置如下：

操作系统：Ubuntu的4.16

Nvidia的DIGITS泊坞窗图片版本：19.01，朱古力

DIGITS版本：6.1.1

朱古力版本：0.17.2

朱古力味：Nvidia公司

OpenCV的版本：4.0.0

Python版本：3.5

任何帮助深表感谢。

Answer 1

哈里森·麦金太尔，谢谢！这PR修复它：https://github.com/opencv/opencv/pull/13800。请注意，有型“ClusterDetections”层。它不是由OpenCV的支持，但你可以使用自定义层技工（见tutorial）实现它