Initial commit

Divers/tutoriel25-2/train_is_panneau.py

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+import tensorflow as tf
+from tensorflow.keras import layers, models
+import numpy as np
+from sklearn.utils import shuffle
+from sklearn.model_selection import train_test_split
+import cv2
+import os
+import common
+import time
+import dataset
+
+batch_size=64
+nbr_entrainement=20
+
+tab_images=np.array([]).reshape(0, common.size, common.size, 3)
+
+tab_panneau, tab_image_panneau=common.lire_images_panneaux(common.dir_images_panneaux, common.size)
+
+if not os.path.exists(common.dir_images_autres_panneaux):
+    quit("Le repertoire d'image n'existe pas: {}".format(common.dir_images_autres_panneaux))
+
+if not os.path.exists(common.dir_images_sans_panneaux):
+    quit("Le repertoire d'image n'existe pas:".format(common.dir_images_sans_panneaux))
+
+nbr=0
+for image in tab_image_panneau:
+    lot=dataset.create_lot_img(image, 12000)
+    tab_images=np.concatenate([tab_images, lot])
+    nbr+=len(lot)
+
+tab_labels=np.full(nbr, 1)
+
+print("Image panneaux:", nbr)
+
+files=os.listdir(common.dir_images_autres_panneaux)
+if files is None:
+    quit("Le repertoire d'image est vide:".format(common.dir_images_autres_panneaux))
+
+nbr=0
+for file in files:
+    if file.endswith("png"):
+        path=os.path.join(common.dir_images_autres_panneaux, file)
+        image=cv2.resize(cv2.imread(path), (common.size, common.size), cv2.INTER_LANCZOS4)
+        lot=dataset.create_lot_img(image, 700)
+        tab_images=np.concatenate([tab_images, lot])
+        nbr+=len(lot)
+
+tab_labels=np.concatenate([tab_labels, np.full(nbr, 0)])
+        
+print("Image autres panneaux:", nbr)
+
+nbr_np=int(len(tab_images)/2)
+print("nbr_np", nbr_np)
+
+id=1
+nbr=0
+tab=[]
+for cpt in range(nbr_np):
+    file=common.dir_images_sans_panneaux+"/{:d}.png".format(id)
+    if not os.path.isfile(file):
+        break
+    image=cv2.resize(cv2.imread(file), (common.size, common.size))
+    tab.append(image)
+    id+=1
+    nbr+=1
+
+tab_images=np.concatenate([tab_images, tab])
+tab_labels=np.concatenate([tab_labels, np.full(nbr, 0)])
+print("Image sans panneaux:", nbr)
+
+tab_images=np.array(tab_images, dtype=np.float32)/255
+tab_labels=np.array(tab_labels, dtype=np.float32).reshape([-1, 1])
+
+tab_images, tab_labels=shuffle(tab_images, tab_labels)
+train_images, test_images, train_labels, test_labels=train_test_split(tab_images, tab_labels, test_size=0.10)
+
+train_ds=tf.data.Dataset.from_tensor_slices((train_images, train_labels)).batch(batch_size)
+test_ds=tf.data.Dataset.from_tensor_slices((test_images, test_labels)).batch(batch_size)
+
+print("train_images", len(train_images))
+print("test_images", len(test_images))
+print("nbr panneau", len(np.where(train_labels==0.)[1]), train_labels.shape)
+
+@tf.function
+def train_step(images, labels):
+  with tf.GradientTape() as tape:
+    predictions=model_is_panneau(images)
+    loss=loss_object(labels, predictions)
+  gradients=tape.gradient(loss, model_is_panneau.trainable_variables)
+  optimizer.apply_gradients(zip(gradients, model_is_panneau.trainable_variables))
+  train_loss(loss)
+  train_accuracy(labels, predictions)
+
+def train(train_ds, nbr_entrainement):
+  for entrainement in range(nbr_entrainement):
+    start=time.time()
+    for images, labels in train_ds:
+      train_step(images, labels)
+    message='Entrainement {:04d}, loss: {:6.4f}, accuracy: {:7.4f}%, temps: {:7.4f}'
+    print(message.format(entrainement+1,
+                         train_loss.result(),
+                         train_accuracy.result()*100,
+                         time.time()-start))
+    train_loss.reset_states()
+    train_accuracy.reset_states()
+    test(test_ds)
+
+def test(test_ds):
+  start=time.time()
+  for test_images, test_labels in test_ds:
+    predictions=model_is_panneau(test_images)
+    t_loss=loss_object(test_labels, predictions)
+    test_loss(t_loss)
+    test_accuracy(test_labels, predictions)
+  message='   >>> Test: loss: {:6.4f}, accuracy: {:7.4f}%, temps: {:7.4f}'
+  print(message.format(test_loss.result(),
+                       test_accuracy.result()*100,
+                       time.time()-start))
+  test_loss.reset_states()
+  test_accuracy.reset_states()
+  
+optimizer=tf.keras.optimizers.Adam()
+loss_object=tf.keras.losses.BinaryCrossentropy()
+train_loss=tf.keras.metrics.Mean()
+train_accuracy=tf.keras.metrics.BinaryAccuracy()
+test_loss=tf.keras.metrics.Mean()
+test_accuracy=tf.keras.metrics.BinaryAccuracy()
+model_is_panneau=common.is_panneau_model()
+checkpoint=tf.train.Checkpoint(model_is_panneau=model_is_panneau)
+
+print("Entrainement")
+train(train_ds, nbr_entrainement)
+test(test_ds)
+
+checkpoint.save(file_prefix="./training_is_panneau/is_panneau")