Predict

.gitignore

@@ -15,5 +15,6 @@ fault_jpg/
 fault_jpg_1/
 keras-yolo3-master/log_experimento_fault_gpu/
 Result_ssd300_fault_4/
+Result_yolo3_fault_4/result_otros/
 
 *.h5

Result_ssd300_fault_1/.gitignore

@@ -1 +0,0 @@
-experimento_ssd300_fault_1.h5

Result_yolo3_fault_4/.gitignore

@@ -1 +0,0 @@
-result_otros/

Test_Predict.py

@@ -0,0 +1,104 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Feb 20 11:22:54 2020
+
+@author: dlsaavedra
+"""
+
+import time
+import os
+import argparse
+import json
+import cv2
+import sys
+sys.path += [os.path.abspath('keras-yolo3-master')]
+
+from utils.utils import get_yolo_boxes, makedirs
+from utils.bbox import draw_boxes
+from keras.models import load_model
+from tqdm import tqdm
+import numpy as np
+
+#%%
+
+
+def predict(infer_model_1, infer_model_2, config_1, config_2, images_paths):
+
+    images = []
+
+    for image_path in images_paths:
+        image = cv2.imread(image_path)
+        images.append(image)
+
+    net_h, net_w = 416, 416 # a multiple of 32, the smaller the faster
+    obj_thresh, nms_thresh = 0.5, 0.45
+
+    ###############################
+    #   Load the model
+    ###############################
+
+    labels_1 = config_1['model']['labels']
+    labels_2 = config_2['model']['labels']
+
+
+    boxes_p_1 = get_yolo_boxes(infer_model_1, images, net_h, net_w, config_1['model']['anchors'], obj_thresh, nms_thresh)
+    boxes_p_2 = get_yolo_boxes(infer_model_2, images, net_h, net_w, config_2['model']['anchors'], obj_thresh, nms_thresh)
+
+    list_dict = []
+    for i in range(len(images)):
+        dict_boxes = { 'name_file' : images_paths[i],
+                   'objects' : []}
+        for boxes in boxes_p_1[i]:
+            dict_boxes['objects'].append({
+                    'class': labels_1[boxes.label],
+                    'score': boxes.score,
+                    'xmax':  boxes.xmax,
+                    'xmin':  boxes.xmin,
+                    'ymax':  boxes.ymax,
+                    'ymin':  boxes.ymax
+                    })
+
+        for boxes in boxes_p_2[i]:
+            dict_boxes['objects'].append({
+                    'class': labels_2[boxes.label],
+                    'score': boxes.score,
+                    'xmax':  boxes.xmax,
+                    'xmin':  boxes.xmin,
+                    'ymax':  boxes.ymax,
+                    'ymin':  boxes.ymax
+                    })
+
+        list_dict.append(dict_boxes.copy())
+
+    return list_dict
+
+
+
+config_model_1 = 'config_full_yolo_fault_1_infer.json'
+config_model_2 = 'config_full_yolo_fault_4_infer.json'
+input_path = 'fault_jpg/'
+
+
+with open(config_model_1) as config_buffer:
+        config_1 = json.load(config_buffer)
+with open(config_model_2) as config_buffer:
+    config_2 = json.load(config_buffer)
+
+os.environ['CUDA_VISIBLE_DEVICES'] = config_1['train']['gpus']
+infer_model_1 = load_model(config_1['train']['saved_weights_name'])
+infer_model_2 = load_model(config_2['train']['saved_weights_name'])
+
+images_paths = []
+
+if os.path.isdir(input_path):
+    for inp_file in os.listdir(input_path):
+        images_paths += [input_path + inp_file]
+else:
+    images_paths += [input_path]
+
+images_paths = [inp_file for inp_file in images_paths if (inp_file[-4:] in ['.jpg', '.png', 'JPEG'])]
+
+L = predict(infer_model_1, infer_model_2, config_1, config_2, images_paths)
+
+js = json.dumps(L)