caffe-ssd测试demo

在jupyter下跑通的测试demo，修改了文件加载的绝对路径，以及下面代码中原来的xrange改为range，以及deploy.prototxt中的相对路径“data/VOC0712/labelmap_voc.prototxt” 改为绝对路径 。

Detection with SSD

In this example, we will load a SSD model and use it to detect objects.

 

1. Setup

• First, Load necessary libs and set up caffe and caffe_root

In [1]:

 

 

 

 

 

import numpy as np

import matplotlib.pyplot as plt

%matplotlib inline

​

plt.rcParams['figure.figsize'] = (10, 10)

plt.rcParams['image.interpolation'] = 'nearest'

plt.rcParams['image.cmap'] = 'gray'

​

# Make sure that caffe is on the python path:

caffe_root = '../'  # this file is expected to be in {caffe_root}/examples

import os

os.chdir(caffe_root)

import sys

sys.path.insert(0, 'python')

​

import caffe

caffe.set_device(0)

caffe.set_mode_gpu()

 

 

 

• Load LabelMap.

In [2]:

 

 

 

 

 

from google.protobuf import text_format

from caffe.proto import caffe_pb2

​

# load PASCAL VOC labels

labelmap_file = 'E:/GitHub/caffe-ssd-windows/data/VOC0712/labelmap_voc.prototxt'

file = open(labelmap_file, 'r')

labelmap = caffe_pb2.LabelMap()

text_format.Merge(str(file.read()), labelmap)

​

def get_labelname(labelmap, labels):

    num_labels = len(labelmap.item)

    labelnames = []

    if type(labels) is not list:

        labels = [labels]

    for label in labels:

        found = False

        for i in range(0, num_labels):

            if label == labelmap.item[i].label:

                found = True

                labelnames.append(labelmap.item[i].display_name)

                break

        assert found == True

    return labelnames

 

 

 

• Load the net in the test phase for inference, and configure input preprocessing.

In [3]:

 

 

 

 

 

model_def = 'E:/GitHub/caffe-ssd-windows/models/VGGNet/VOC0712/SSD_300x300/deploy.prototxt'

model_weights = 'E:/GitHub/caffe-ssd-windows/models/VGGNet/VOC0712/SSD_300x300/VGG_VOC0712_SSD_300x300_iter_120000.caffemodel'

​

net = caffe.Net(model_def,      # defines the structure of the model

                model_weights,  # contains the trained weights

                caffe.TEST)     # use test mode (e.g., don't perform dropout)

​

# input preprocessing: 'data' is the name of the input blob == net.inputs[0]

transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})

transformer.set_transpose('data', (2, 0, 1))

transformer.set_mean('data', np.array([104,117,123])) # mean pixel

transformer.set_raw_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]

transformer.set_channel_swap('data', (2,1,0)) # the reference model has channels in BGR order instead of RGB

 

 

 

2. SSD detection

 

• Load an image.

In [4]:

 

 

 

 

 

# set net to batch size of 1

image_resize = 300

net.blobs['data'].reshape(1,3,image_resize,image_resize)

​

image = caffe.io.load_image('E:/GitHub/caffe-ssd-windows/examples/images/fish-bike.jpg')

plt.imshow(image)

 

 

Out[4]:

<matplotlib.image.AxesImage at 0x2772beda908>

 

 

• Run the net and examine the top_k results

In [5]:

 

 

 

 

 

transformed_image = transformer.preprocess('data', image)

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

​

# Forward pass.

detections = net.forward()['detection_out']

​

# Parse the outputs.

det_label = detections[0,0,:,1]

det_conf = detections[0,0,:,2]

det_xmin = detections[0,0,:,3]

det_ymin = detections[0,0,:,4]

det_xmax = detections[0,0,:,5]

det_ymax = detections[0,0,:,6]

​

# Get detections with confidence higher than 0.6.

top_indices = [i for i, conf in enumerate(det_conf) if conf >= 0.6]

​

top_conf = det_conf[top_indices]

top_label_indices = det_label[top_indices].tolist()

top_labels = get_labelname(labelmap, top_label_indices)

top_xmin = det_xmin[top_indices]

top_ymin = det_ymin[top_indices]

top_xmax = det_xmax[top_indices]

top_ymax = det_ymax[top_indices]

 

 

 

D:\Anaconda3\envs\py35\lib\site-packages\skimage\transform\_warps.py:84: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.
  warn("The default mode, 'constant', will be changed to 'reflect' in "

 

• Plot the boxes

In [7]:

 

 

 

 

 

colors = plt.cm.hsv(np.linspace(0, 1, 21)).tolist()

​

plt.imshow(image)

currentAxis = plt.gca()

​

for i in range(top_conf.shape[0]):

    xmin = int(round(top_xmin[i] * image.shape[1]))

    ymin = int(round(top_ymin[i] * image.shape[0]))

    xmax = int(round(top_xmax[i] * image.shape[1]))

    ymax = int(round(top_ymax[i] * image.shape[0]))

    score = top_conf[i]

    label = int(top_label_indices[i])

    label_name = top_labels[i]

    display_txt = '%s: %.2f'%(label_name, score)

    coords = (xmin, ymin), xmax-xmin+1, ymax-ymin+1

    color = colors[label]

    currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor=color, linewidth=2))

    currentAxis.text(xmin, ymin, display_txt, bbox={'facecolor':color, 'alpha':0.5})