From bc0a66d576eb7bc734aaa1aa9dd6962d8971275e Mon Sep 17 00:00:00 2001
From: frankknoll <Knoll_Frank@web.de>
Date: Sat, 18 Jun 2022 17:14:01 +0200
Subject: [PATCH] simplifying kears code

---
 src/HowBadIsMyBatch.ipynb | 23 ++++-------------------
 1 file changed, 4 insertions(+), 19 deletions(-)

diff --git a/src/HowBadIsMyBatch.ipynb b/src/HowBadIsMyBatch.ipynb
index 41d99144fc2..ecff57b475b 100644
--- a/src/HowBadIsMyBatch.ipynb
+++ b/src/HowBadIsMyBatch.ipynb
@@ -238,8 +238,6 @@
     "# copied from value of characters variable in captcha_ocr.ipynb or captcha_ocr_trainAndSaveModel.ipynb\n",
     "characters = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'a', 'b', 'c', 'd', 'e', 'f']\n",
     "\n",
-    "batch_size = 1\n",
-    "\n",
     "img_width = 241\n",
     "img_height = 62\n",
     "\n",
@@ -270,7 +268,9 @@
     "    # dimension to correspond to the width of the image.\n",
     "    img = tf.transpose(img, perm=[1, 0, 2])\n",
     "    # 7. Return a dict as our model is expecting two inputs\n",
-    "    return img\n",
+    "    array = keras.utils.img_to_array(img)\n",
+    "    array = np.expand_dims(array, axis=0)\n",
+    "    return array\n",
     "\n",
     "\n",
     "def decode_batch_predictions(pred):\n",
@@ -294,26 +294,11 @@
     "\n",
     "\n",
     "def getTextInCaptchaImage(captchaImageFile):\n",
-    "    batchImages = getBatchImagesFromFile(captchaImageFile)\n",
+    "    batchImages = encode_single_sample(captchaImageFile)\n",
     "    preds = model.predict(batchImages)\n",
     "    return decode_batch_predictions(preds)[0]\n",
     "\n",
     "\n",
-    "def getBatchImagesFromFile(imageFile):\n",
-    "    return list(asDataset(imageFile).as_numpy_iterator())[0]\n",
-    "\n",
-    "\n",
-    "def asDataset(imageFile):\n",
-    "    dataset = tf.data.Dataset.from_tensor_slices([imageFile])\n",
-    "    dataset = (\n",
-    "        dataset\n",
-    "        .map(encode_single_sample, num_parallel_calls=tf.data.AUTOTUNE)\n",
-    "        .batch(batch_size)\n",
-    "        .prefetch(buffer_size=tf.data.AUTOTUNE)\n",
-    "    )\n",
-    "    return dataset\n",
-    "\n",
-    "\n",
     "print(\"loading model...\")\n",
     "model = load_model()\n",
     "model.summary()"