[OpenCV] Install OpenCV 3.3 with DNN

OpenCV 3.3
Aug 3, 2017

OpenCV 3.3 has been released with greatly improved Deep Learning module and lots of optimizations.

Adrian Rosebrock: http://www.pyimagesearch.com/author/adrian/ [nice]

Ref: Real-time object detection with deep learning and OpenCV

Thus, tracking is essential.

Multiple-thread is another consideration: Efficient, threaded video streams with OpenCV

Update: [OpenCV 3.4] Install OpenCV 3.4 with DNN

The following networks have been tested and known to work:

- AlexNet
- GoogLeNet v1 (also referred to as Inception-5h)
- ResNet-34/50/...
- SqueezeNet v1.1
- VGG-based FCN (semantical segmentation network)
- ENet (lightweight semantical segmentation network)
- VGG-based SSD (object detection network)
- MobileNet-based SSD (light-weight object detection network)

下面是我们将用到的一些函数。

在dnn中从磁盘加载图片：

- cv2.dnn.blobFromImage
- cv2.dnn.blobFromImages

用“create”方法直接从各种框架中导出模型：

- cv2.dnn.createCaffeImporter
- cv2.dnn.createTensorFlowImporter
- cv2.dnn.createTorchImporter

使用“读取”方法从磁盘直接加载序列化模型：

- cv2.dnn.readNetFromCaffe
- cv2.dnn.readNetFromTensorFlow
- cv2.dnn.readNetFromTorch
- cv2.dnn.readhTorchBlob

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.-config   python3.4m-config  python3m

python3.          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./opencv_contrib-3.3./modules       \

-D PYTHON_EXECUTABLE=/usr/local/anaconda3/bin/python3.       \

-D BUILD_EXAMPLES=ON ..

[OpenCV] Install OpenCV 3.3 with DNN

Done :-)

Installing ref:

https://hackmd.io/s/S1gWq7BwW

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:

Faster R-CNNs (Girshick et al., 2015) 7 FPS
You Only Look Once (YOLO) (Redmon and Farhadi, 2015) 155 FPS.
Single Shot Detectors (SSDs) (Liu et al., 2015)

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)

[OpenCV] Install OpenCV 3.3 with DNN

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%

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[OpenCV] Install OpenCV 3.3 with DNN

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