Initial commit

Tensorflow/tutoriel19-1/detection.py

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+import tensorflow as tf
+import os 
+import numpy as np
+import cv2
+
+width=160
+height=120
+
+dir='dataE/'
+
+with tf.Session() as s:
+    saver=tf.train.import_meta_graph('./mon_modele/modele.meta')
+    saver.restore(s, tf.train.latest_checkpoint('./mon_modele/'))
+    graph=tf.get_default_graph()
+    images=graph.get_tensor_by_name("entree:0")
+    sortie=graph.get_tensor_by_name("sortie:0")
+
+    for file in os.listdir(dir+'CameraRGB/'):
+        img=cv2.resize(cv2.imread(dir+'CameraRGB/'+file), (width, height))/255                
+        cv2.imshow("image", img)
+        m=cv2.resize(cv2.imread(dir+'CameraSeg/'+file)[:,:,2], (width, height))
+        m[m==7]=255
+        m[m!=255]=0
+        cv2.imshow("mask 7", m)
+        m=cv2.resize(cv2.imread(dir+'CameraSeg/'+file)[:,:,2], (width, height))
+        m[m==9]=255
+        m[m!=255]=0
+        cv2.imshow("mask 9", m)
+        prediction=s.run(sortie, feed_dict={images:[img]})
+        cv2.imshow("mask prediction 7", prediction[0][:,:,0])
+        cv2.imshow("mask prediction 9", prediction[0][:,:,1])
+        if cv2.waitKey()&0xFF==ord('q'):
+            break
+            
+