Update: [OpenCV 3.4] Install OpenCV 3.4 with DNN
The following networks have been tested and known to work:
下面是我们将用到的一些函数。
在dnn中从磁盘加载图片:
用“create”方法直接从各种框架中导出模型:
使用“读取”方法从磁盘直接加载序列化模型:
从磁盘加载完模型之后,可以用.forward方法来向前传播我们的图像,获取分类结果。
看样子就是好东西,那么,一起来安装:Installing OpenCV 3.3.0 on Ubuntu 16.04 LTS
You may meet the trouble in conflicting with python in anaconda3. Solve it as following:
lolo@lolo-UX303UB$ mv /usr/bin/python3 python3 python3.4-config python3.4m-config python3m python3.4 python3.4m python3-config python3m-config lolo: Move them away.
cmake -D CMAKE_BUILD_TYPE=RELEASE \ -D CMAKE_INSTALL_PREFIX=/usr/local/anaconda3 \ -D INSTALL_PYTHON_EXAMPLES=ON \ -D INSTALL_C_EXAMPLES=OFF \ -D OPENCV_EXTRA_MODULES_PATH=/home/unsw/Android/opencv-3.3.0/opencv_contrib-3.3.0/modules \ -D PYTHON_EXECUTABLE=/usr/local/anaconda3/bin/python3.5 \ -D BUILD_EXAMPLES=ON ..
Done :-)
Installing ref:
http://www.linuxfromscratch.org/blfs/view/cvs/general/opencv.html
https://medium.com/@debugvn/installing-opencv-3-3-0-on-ubuntu-16-04-lts-7db376f93961
Now, you have got everything. Let's practice.
From: http://www.pyimagesearch.com/2017/09/11/object-detection-with-deep-learning-and-opencv/
In the first part of today’s post on object detection using deep learning we’ll discuss Single Shot Detectors and MobileNets.
SSD Paper: http://lib.csdn.net/article/deeplearning/53059
SSD Paper: https://arxiv.org/abs/1512.02325 [Origin]
When it comes to deep learning-based object detection there are three primary object detection methods that you’ll likely encounter:
If we combine both the MobileNet architecture and the Single Shot Detector (SSD) framework, we arrive at a fast, efficient deep learning-based method to object detection.
The model we’ll be using in this blog post is a Caffe versionof the original TensorFlow implementation by Howard et al. and was trained by chuanqi305 (see GitHub).
In this section we will use the MobileNet SSD + deep neural network ( dnn ) module in OpenCV to build our object detector.
Code analysis:
# USAGE # python deep_learning_object_detection.py --image images/example_01.jpg \ # --prototxt MobileNetSSD_deploy.prototxt.txt --model MobileNetSSD_deploy.caffemodel # 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 = ["background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"] 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(image, 0.007843, (300, 300), 127.5) # --> NCHW # pass the blob through the network and obtain the detections and # predictions print("[INFO] computing object detections...") net.setInput(blob) detections = net.forward() # --> 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)
NCHW
There is a comment that explains this, but in a different source file, ConvolutionalNodes.h, pasted below.
Note that the NVidia abbreviations refer to row-major layout, so to map them to column-major tensor indices are used by CNTK, you will need to reverse their order. E.g. cudnn stores images in “NCHW,” which is a [W x H x C x N] tensor in CNTK notation (W being the fastest-changing dimension; and there are N objects of dimension [W x H x C] concatenated).
Note that the “legacy” (non-cuDNN) memory layout is old code written before NCHW became the standard, so we are likely phasing out the old representation eventually.
net.forward
[INFO] loading model... [INFO] computing object detections... (1, 1, 2, 7) [[[[ 0. 12. 0.95878285 0.49966827 0.6235761 0.69597626 0.87614471] [ 0. 15. 0.99952459 0.04266162 0.20033446 0.45632178 0.84977102]]]] [INFO] dog: 95.88% [INFO] person: 99.95%