Summary

2020-02-06 16:47:03 -03:00
parent 6328265287
commit b586f22bf0
318 changed files with 25111 additions and 664 deletions
--- a/ssd_keras-master/evaluate.py
+++ b/ssd_keras-master/evaluate.py
@@ -0,0 +1,119 @@
+from keras import backend as K
+from keras.models import load_model
+from keras.optimizers import Adam
+#from scipy.misc import imread
+import numpy as np
+from matplotlib import pyplot as plt
+import argparse
+import json
+
+from models.keras_ssd300 import ssd_300
+from keras_loss_function.keras_ssd_loss import SSDLoss
+from keras_layers.keras_layer_AnchorBoxes import AnchorBoxes
+from keras_layers.keras_layer_DecodeDetections import DecodeDetections
+from keras_layers.keras_layer_DecodeDetectionsFast import DecodeDetectionsFast
+from keras_layers.keras_layer_L2Normalization import L2Normalization
+from data_generator.object_detection_2d_data_generator import DataGenerator
+from eval_utils.average_precision_evaluator import Evaluator
+
+def _main_(args):
+
+    config_path = args.conf
+
+    with open(config_path) as config_buffer:
+        config = json.loads(config_buffer.read())
+
+    ###############################
+    #   Parse the annotations
+    ###############################
+    path_imgs_test = config['test']['test_image_folder']
+    path_anns_test = config['test']['test_annot_folder']
+    labels = config['model']['labels']
+    categories = {}
+    #categories = {"Razor": 1, "Gun": 2, "Knife": 3, "Shuriken": 4} #la categoría 0 es la background
+    for i in range(len(labels)): categories[labels[i]] = i+1
+    print('\nTraining on: \t' + str(categories) + '\n')
+
+    img_height = config['model']['input'] # Height of the model input images
+    img_width = config['model']['input'] # Width of the model input images
+    img_channels = 3 # Number of color channels of the model input images
+    n_classes = len(labels) # Number of positive classes, e.g. 20 for Pascal VOC, 80 for MS COCO
+    classes = ['background'] + labels
+
+    model_mode = 'training'
+    # TODO: Set the path to the `.h5` file of the model to be loaded.
+    model_path = config['train']['saved_weights_name']
+
+    # We need to create an SSDLoss object in order to pass that to the model loader.
+    ssd_loss = SSDLoss(neg_pos_ratio=3, alpha=1.0)
+
+    K.clear_session() # Clear previous models from memory.
+
+    model = load_model(model_path, custom_objects={'AnchorBoxes': AnchorBoxes,
+                                                   'L2Normalization': L2Normalization,
+                                                   'DecodeDetections': DecodeDetections,
+                                                   'compute_loss': ssd_loss.compute_loss})
+
+    test_dataset = DataGenerator()
+    test_dataset.parse_xml(images_dirs= [config['test']['test_image_folder']],
+                           image_set_filenames=[config['test']['test_image_set_filename']],
+                           annotations_dirs=[config['test']['test_annot_folder']],
+                           classes=classes,
+                           include_classes='all',
+                           exclude_truncated=False,
+                           exclude_difficult=False,
+                           ret=False)
+    evaluator = Evaluator(model=model,
+                          n_classes=n_classes,
+                          data_generator=test_dataset,
+                          model_mode=model_mode)
+
+    results = evaluator(img_height=img_height,
+                        img_width=img_width,
+                        batch_size=4,
+                        data_generator_mode='resize',
+                        round_confidences=False,
+                        matching_iou_threshold=0.5,
+                        border_pixels='include',
+                        sorting_algorithm='quicksort',
+                        average_precision_mode='sample',
+                        num_recall_points=11,
+                        ignore_neutral_boxes=True,
+                        return_precisions=True,
+                        return_recalls=True,
+                        return_average_precisions=True,
+                        verbose=True)
+
+    mean_average_precision, average_precisions, precisions, recalls = results
+
+    total_instances = []
+    precisions = []
+    for i in range(1, len(average_precisions)):
+        print('{:.0f} instances of class'.format(len(recalls[i])),
+              classes[i], 'with average precision: {:.4f}'.format(average_precisions[i]))
+        total_instances.append(len(recalls[i]))
+        precisions.append(average_precisions[i])
+
+    if sum(total_instances) == 0:
+        print('No test instances found.')
+        return
+
+    print('mAP using the weighted average of precisions among classes: {:.4f}'.format(sum([a * b for a, b in zip(total_instances, precisions)]) / sum(total_instances)))
+    print('mAP: {:.4f}'.format(sum(precisions) / sum(x > 0 for x in total_instances)))
+
+    for i in range(1, len(average_precisions)):
+        print("{:<14}{:<6}{}".format(classes[i], 'AP', round(average_precisions[i], 3)))
+    print()
+    print("{:<14}{:<6}{}".format('','mAP', round(mean_average_precision, 3)))
+
+
+
+
+
+
+if __name__ == '__main__':
+    argparser = argparse.ArgumentParser(description='train and evaluate ssd model on any dataset')
+    argparser.add_argument('-c', '--conf', help='path to configuration file')
+
+    args = argparser.parse_args()
+    _main_(args)