[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)

 

 

下面是咱們將用到的一些函數。

在dnn中從磁盤加載圖片：

• • cv2.dnn.blobFromImage
  • cv2.dnn.blobFromImages

用「create」方法直接從各類框架中導出模型：

• • cv2.dnn.createCaffeImporter
  • cv2.dnn.createTensorFlowImporter
  • cv2.dnn.createTorchImporter

使用「讀取」方法從磁盤直接加載序列化模型：

• • cv2.dnn.readNetFromCaffe
  • cv2.dnn.readNetFromTensorFlow
  • cv2.dnn.readNetFromTorch
  • cv2.dnn.readhTorchBlob

從磁盤加載完模型以後，能夠用.forward方法來向前傳播咱們的圖像，獲取分類結果。

 

看樣子就是好東西，那麼，一塊兒來安裝：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: 

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)

 

 

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%