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compile and install refinedet on ubuntu 16.04

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Guide

RefineDet is based on Caffe.

See Install and Configure Caffe on ubuntu 16.04

  • ubuntu 16.04
  • CUDA 9.2 + cudnn 7.1.4 (for caffe/tensorrt/anakin)
  • opencv 3.3.0
  • python 2.7
  • caffe (from refinedet)

compile

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git clone https://github.com/sfzhang15/RefineDet.git
cd RefineDet 
mkdir build && cd build && cmake-gui ..

make -j8 && make pycaffe

options 

USE_CUDNN True
USE_OPENCV True
WITH_PYTHON_LAYER True
BLAS atlas

CMAKE_INSTALL_PREFIX /home/kezunlin/program/refinedet/build/install

tips: vim CMakeLists.txt and comment out examples and docs

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#add_subdirectory(examples)
add_subdirectory(python)
add_subdirectory(matlab)
#add_subdirectory(docs)

fix gflags error

  • caffe/include/caffe/common.hpp
  • caffe/examples/mnist/convert_mnist_data.cpp

Comment out the ifndef

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// #ifndef GFLAGS_GFLAGS_H_
namespace gflags = google;
// #endif  // GFLAGS_GFLAGS_H_

example

two version:

  • single version
  • batch version
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'''
In this example, we will load a RefineDet model and use it to detect objects.
'''
import argparse
import os
import sys
import numpy as np
import skimage.io as io
import cv2
# Make sure that caffe is on the python path:
caffe_root = './'
os.chdir(caffe_root)
sys.path.insert(0, os.path.join(caffe_root, 'python'))
import caffe

classes = ['background', 'person']

def filter_boxs(boxs, threshold=0.4):
    """
    boxs: 500*6  (xmin,ymin,xmax,ymax,confidence,class_index) 
                class_index: 0 background, 1 person
                confidence: 0-1
    return: 
            new_boxs  `list`  [b1,b2,b3,...]
    """
    new_boxs = []
    for i in range(0, boxs.shape[0]):
        xmin,ymin,xmax,ymax,confidence,class_index = boxs[i]
        #print(type(class_index)) # float32
        if int(class_index)>0 and confidence >= threshold:
            box = [int(xmin),int(ymin),int(xmax),int(ymax),confidence, int(class_index)]
            new_boxs.append(box)
    return new_boxs # list [b1,b2,b3,...]

def save_results(counter, image_file, boxs, save_fig=False):

    img = cv2.imread(image_file)
    for i in range(0, len(boxs)):
        xmin,ymin,xmax,ymax,confidence,class_index = boxs[i]

        name = classes[class_index]
        coords = (xmin, ymin), xmax - xmin, ymax - ymin

        cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 0, 255), 3) # bgr
        #display_text = '%s: %.2f' % (name, confidence)
        display_text = '%.2f' % (confidence)
        cv2.putText(img, display_text, (xmin, ymin-5), cv2.FONT_HERSHEY_SIMPLEX, 1, color=(0,0,255), thickness=2)
            
    if save_fig:
        image_filepath = 'output/{0}_results.jpg'.format(counter)
        cv2.imwrite(image_filepath, img)
        print('Saved: ' + image_filepath)


def single():
    caffe.set_device(0)
    caffe.set_mode_gpu()

    save_dir = "./output"
    if not os.path.exists(save_dir):
        os.mkdir(save_dir)

    # load model
    model_def = 'models/ResNet/coco/refinedet_resnet101_512x512/deploy.prototxt'
    model_weights = 'models/ResNet/coco/refinedet_resnet101_512x512/coco_refinedet_resnet101_512x512_iter_75000.caffemodel'
    net = caffe.Net(model_def, model_weights, caffe.TEST)

    # image preprocessing
    img_resize = 512
    net.blobs['data'].reshape(1, 3, img_resize, img_resize)
    data_shape = net.blobs['data'].data.shape
    print("data_shape=", data_shape) # 1, 3, 512, 512
    # by default, caffe use chw, bgr, 0-255, image-[104, 117, 123] 
    transformer = caffe.io.Transformer({'data':data_shape})
    transformer.set_transpose('data', (2, 0, 1)) # hwc ===> chw
    transformer.set_channel_swap('data', (2, 1, 0))  # rgb===>bgr
    transformer.set_raw_scale('data', 255)  # [0-1]===> [0,255]
    transformer.set_mean('data', np.array([104, 117, 123]))  # bgr mean pixel
    
    files = ["./images/1.png", "./images/2.png"]# 500,7  + 384,7 === 500,7 + 500,7 
    for index,image_file in enumerate(files):
        print("image_file=", image_file)
        image = caffe.io.load_image(image_file) # hwc, rgb, 0-1 
        print("image.shape=", image.shape)

        transformed_image = transformer.preprocess('data', image)
        print("transformed_image.shape=", transformed_image.shape)

        net.blobs['data'].data[...] = transformed_image

        detections = net.forward()['detection_out']
        print("detections.shape = ",detections.shape) # 1, 1, 500, 7
        det_label = detections[0, 0, :, 1] # 0 back, 1 -person (now only ==1)
        det_conf = detections[0, 0, :, 2] # 0-1 
        det_xmin = detections[0, 0, :, 3] * image.shape[1]
        det_ymin = detections[0, 0, :, 4] * image.shape[0]
        det_xmax = detections[0, 0, :, 5] * image.shape[1]
        det_ymax = detections[0, 0, :, 6] * image.shape[0]
        boxs = np.column_stack([det_xmin, det_ymin, det_xmax, det_ymax, det_conf, det_label])
        print("boxs = ", boxs.shape) # 500,6

        new_boxs = filter_boxs(boxs)
        print("new_boxs = ", len(new_boxs)) # 3 boxs

        # show result
        save_results(index, image_file, new_boxs, save_fig=True)

def batch():
    caffe.set_device(0)
    caffe.set_mode_gpu()

    save_dir = "./output"
    if not os.path.exists(save_dir):
        os.mkdir(save_dir)

    # load model
    model_def = 'models/ResNet/coco/refinedet_resnet101_512x512/deploy.prototxt'
    model_weights = 'models/ResNet/coco/refinedet_resnet101_512x512/coco_refinedet_resnet101_512x512_iter_75000.caffemodel'
    net = caffe.Net(model_def, model_weights, caffe.TEST)

    box_count_per_image = 500 
    #files = ["./images/2.png"]
    files = ["./images/1.png", "./images/2.png"]# 500,7  + 384,7 === 500,7 + 500,7 
    # update detection_output_layer.cpp and cu to keep 500 box results
    batch_size = len(files) 
    # image preprocessing
    img_resize = 512
    net.blobs['data'].reshape(batch_size, 3, img_resize, img_resize)
    data_shape = net.blobs['data'].data.shape
    print("data_shape=", data_shape) # 1, 3, 512, 512
    # by default, caffe use chw, bgr, 0-255, image-[104, 117, 123] 
    transformer = caffe.io.Transformer({'data':data_shape})
    transformer.set_transpose('data', (2, 0, 1)) # hwc ===> chw
    transformer.set_channel_swap('data', (2, 1, 0))  # rgb===>bgr
    transformer.set_raw_scale('data', 255)  # [0-1]===> [0,255]
    transformer.set_mean('data', np.array([104, 117, 123]))  # bgr mean pixel
    
    for i in range(len(files)):
        #image_file = "./images/1.png"
        image_file = files[i]
        print("image_file=", image_file)
        image = caffe.io.load_image(image_file) # hwc, rgb, 0-1 
        print("image.shape=", image.shape)

        transformed_image = transformer.preprocess('data', image)
        print("transformed_image.shape=", transformed_image.shape)

        net.blobs['data'].data[i,:,:,:] = transformed_image

    detections = net.forward()['detection_out']
    print("detections.shape = ",detections.shape) # 1, 1, 500+384, 7 ===> 1,1, 1000,7

    for i in range(batch_size):
        start = i * box_count_per_image
        end = (i+1) * box_count_per_image
        print("start-end: ",start, end)
    
        det_label = detections[0, 0, start:end, 1] # 0 back, 1 -person (now only ==1)
        print(det_label[:10])
        det_conf = detections[0, 0, start:end, 2] # 0-1 
        det_xmin = detections[0, 0, start:end, 3] * image.shape[1]
        det_ymin = detections[0, 0, start:end, 4] * image.shape[0]
        det_xmax = detections[0, 0, start:end, 5] * image.shape[1]
        det_ymax = detections[0, 0, start:end, 6] * image.shape[0]
        boxs = np.column_stack([det_xmin, det_ymin, det_xmax, det_ymax, det_conf, det_label])
        print("boxs = ", boxs.shape) # 500,6

        new_boxs = filter_boxs(boxs)
        print("new_boxs = ", len(new_boxs)) # 3 boxs

        # show result
        save_results(i, image_file, new_boxs, save_fig=True)

if __name__ == '__main__':
   #single()
   batch()

output 

('data_shape=', (2, 3, 512, 512))
('image_file=', './images/1.png')
('image.shape=', (1080, 1920, 3))
('transformed_image.shape=', (3, 512, 512))
('image_file=', './images/2.png')
('image.shape=', (1080, 1920, 3))
('transformed_image.shape=', (3, 512, 512))
('detections.shape = ', (1, 1, 1000, 7))
('start-end: ', 0, 500)
[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
('boxs = ', (500, 6))
('new_boxs = ', 3)
Saved: output/0_results.jpg
('start-end: ', 500, 1000)
[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
('boxs = ', (500, 6))
('new_boxs = ', 6)
Saved: output/1_results.jpg

Reference

History

  • 20181127: created.