python detect.py

--crop_mode=selective_search

--pretrained_model=改成你自己的路径\models\bvlc_reference_rcnn_ilsvrc13\bvlc_reference_rcnn_ilsvrc13.caffemodel

--model_def=改成你自己的路径\models\bvlc_reference_rcnn_ilsvrc13\deploy.prototxt

--gpu

--raw_scale=

C:\Users\改成你自己\Desktop\det_input.txt

C:\Users\改成你自己\Desktop\det_output.h5

 import numpy as np

 import pandas as pd

 import matplotlib.pyplot as plt

 df = pd.read_hdf('det_output.h5', 'df')

 print(df.shape)

 print(df.iloc[0])

 with open('a\caffe-windows\data\ilsvrc12\det_synset_words.txt') as f:

     labels_df = pd.DataFrame([

         {

             'synset_id': l.strip().split(' ')[0],

             'name': ' '.join(l.strip().split(' ')[1:]).split(',')[0]

         }

         for l in f.readlines()

     ])

 labels_df.sort_values(by='synset_id')# sort('synset_id')

 predictions_df = pd.DataFrame(np.vstack(df.prediction.values), columns=labels_df['name'])

 print(predictions_df.iloc[0])

 max_s = predictions_df.max(0)

 max_s = max_s.sort_values(ascending=False)

 # Find, print, and display the top detections: person and bicycle.

 i = predictions_df[max_s.keys()[0]].argmax()  #person

 j = predictions_df[max_s.keys()[1]].argmax()  #bicycle

 # Show top predictions for top detection.

 f = pd.Series(df['prediction'].iloc[i], index=labels_df['name'])

 print('Top detection:')

 print(f.sort_values(ascending=False)[:5])

 # Show top predictions for second-best detection.

 f = pd.Series(df['prediction'].iloc[j], index=labels_df['name'])

 print('Second-best detection:')

 print(f.sort_values(ascending=False)[:5])

 # Show top detection in red, second-best top detection in blue.

 im = plt.imread('a\caffe-windows\examples\images\\fish-bike.jpg')

 plt.imshow(im)

 currentAxis = plt.gca()

 det = df.iloc[i]

 coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']

 currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='r', linewidth=5))

 det = df.iloc[j]

 coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']

 currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='b', linewidth=5))

 plt.show()

Top detection:

name

person             1.835771

swimming trunks   -1.150371

rubber eraser     -1.231106

turtle            -1.266038

plastic bag       -1.303266

dtype: float32

Second-best detection:

name

bicycle     0.866110

unicycle   -0.359140

scorpion   -0.811621

lobster    -0.982891

lamp       -1.096809

dtype: float32