SOL4Py Samples

#******************************************************************************
#
#  Copyright (c) 2018-2019 Antillia.com TOSHIYUKI ARAI. ALL RIGHTS RESERVED.
#
#    This program is free software: you can redistribute it and/or modify
#    it under the terms of the GNU General Public License as published by
#    the Free Software Foundation, either version 3 of the License, or
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#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
#    GNU General Public License for more details.
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#    You should have received a copy of the GNU General Public License
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#******************************************************************************

 
# 2019/06/20

import os
import sys
import cv2
import traceback
import errno

import configparser

from keras.applications.imagenet_utils import preprocess_input
from keras.backend.tensorflow_backend import set_session
from keras.preprocessing import image
import matplotlib.pyplot as plt
import numpy as np
from scipy.misc import imread
import tensorflow as tf

from ssd_v2 import SSD300v2
from ssd_utils import BBoxUtility


############################################################
#  This class is based on SSD300v2 in ssd_v2.py and run_ssd.py program
#
class SSDDetector:

  def __init__(self, argv):
 
    app_name  = os.path.basename(argv[0])
    name, _ = app_name.split(".")
    inifile = name + ".ini"
    
    print("inifile {}".format(inifile))
    
    parser = configparser.ConfigParser()
    parser.read(inifile)
    self.weightfile  = parser.get("WEIGHT_FILE",     "filename") 
    self.showimage   = int(parser.get("SHOW_IMAGE",   "show"))
    self.confidence  = float(parser.get("DETECTION", "confidence") )
    
    # For example, this may take a string C:/ssd_keras/weights_SSD300.hdf5

    self.classes = ['Aeroplane', 'Bicycle', 'Bird', 'Boat', 'Bottle',
               'Bus', 'Car', 'Cat', 'Chair', 'Cow', 'Diningtable',
               'Dog', 'Horse','Motorbike', 'Person', 'Pottedplant',
               'Sheep', 'Sofa', 'Train', 'Tvmonitor']
 
    self.n_classes = len(self.classes) + 1

    self.input_shape = (300, 300, 3)
    self.model = SSD300v2(self.input_shape, num_classes=self.n_classes)
        
    self.model.load_weights(self.weightfile, by_name=True)
    
    self.bbox_util = BBoxUtility(self.n_classes)



  # For simplicity, we assume single imagefilename
  def detect(self, imagefilename, save_imagefilename, save_csv_filename):
    inputs = []
    images = []
    img = image.load_img(imagefilename, target_size=(300, 300))
    img = image.img_to_array(img)

    images.append(imread(imagefilename))
    inputs.append(img.copy())

    inputs  = preprocess_input(np.array(inputs))

    preds   = self.model.predict(inputs, batch_size=1, verbose=1)
    self.bbox_util = BBoxUtility(self.n_classes)

    results = self.bbox_util.detection_out(preds)
    
    plt.clf()
    
    # Open a csv file
    with open(save_csv_filename, "w") as csvf:
      header = "id, object, score, x, y, w, h\n"
      csvf.write(header)
      
      for i, img in enumerate(images):
        # Parse the outputs.
        det_label = results[i][:, 0]
        det_conf = results[i][:, 1]
        det_xmin = results[i][:, 2]
        det_ymin = results[i][:, 3]
        det_xmax = results[i][:, 4]
        det_ymax = results[i][:, 5]

        # Get detections with confidence higher than 0.3.
        top_indices = [i for i, conf in enumerate(det_conf) if conf >= self.confidence]

        top_conf = det_conf[top_indices]
        top_label_indices = det_label[top_indices].tolist()
        top_xmin = det_xmin[top_indices]
        top_ymin = det_ymin[top_indices]
        top_xmax = det_xmax[top_indices]
        top_ymax = det_ymax[top_indices]

        colors = plt.cm.hsv(np.linspace(0, 1, 21)).tolist()
        fig = plt.imshow(img / 255.)
        current_axis = plt.gca()
        
        # We don't need any axis and extra spaces arround the image.
        current_axis.set_axis_off()
        current_axis.autoscale(False)
        extent = current_axis.get_window_extent().transformed(plt.gcf().dpi_scale_trans.inverted())

        for i in range(top_conf.shape[0]):
            xmin = int(round(top_xmin[i] * img.shape[1]))
            ymin = int(round(top_ymin[i] * img.shape[0]))
            xmax = int(round(top_xmax[i] * img.shape[1]))
            ymax = int(round(top_ymax[i] * img.shape[0]))
            score = top_conf[i]
            label = int(top_label_indices[i])
            label_name = self.classes[label - 1]
            
            # Display id only to avoid overwrapping of display texts.
            display_txt = "{}".format(i + 1)
            coords = (xmin, ymin), xmax-xmin+1, ymax-ymin+1
            csv = "{}, {}, {:0.2f}, {}, {}, {}, {}\n".format(i+1, label_name, 
                                score, xmin, ymin, xmax-xmin+1, ymax-ymin+1)
            print(csv)
            csvf.write(csv)
            
            color = colors[label]
            current_axis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor=color, linewidth=2))
            current_axis.text(xmin, ymin, display_txt, bbox={'facecolor':color, 'alpha':0.5})

        plt.savefig(save_imagefilename, bbox_inches=extent)
        
        if self.showimage == 1:
          self.show_image(save_imagefilename)

  # Read and show an image by using OpenCV APIs. 
  def show_image(self, filename):
      print("filename {}".format(filename))
      cvimage = cv2.imread(filename);
      title = filename + " - " + "SSDDetector"
      cv2.imshow(title, cvimage);
      cv2.waitKey(0)


############################################################
#    
if __name__=="__main__":

  try:
    np.set_printoptions(suppress=True)

    config = tf.ConfigProto()
    config.gpu_options.per_process_gpu_memory_fraction = 0.45
    set_session(tf.Session(config=config))


    detector = SSDDetector(sys.argv)

    filename = "GinzaMitukoshi.jpg"
    
    imagefilepath      = "C:/images/test_images/" + filename
    save_imagefilename = "./" + filename
    save_csvfilename   = "./" + filename + ".csv"
 
    detector.detect(imagefilepath, save_imagefilename, save_csvfilename)

  except:
    traceback.print_exc()
    pass