roundabout,
created on Thursday, 7 September 2023, 05:10:06 (1694063406),
received on Tuesday, 26 March 2024, 14:36:51 (1711463811)
Author identity: vlad <vlad.muntoiu@gmail.com>
f9fcd79d28ed1f0247132f50afe3b72ddde2363b
main.ipynb
@@ -17,7 +17,7 @@
"source": [
"import os # for file operations\n",
"import json # for loading the annotations file\n",
"from PIL import Image, ImageDraw, ImageOps # for processing the image data\n",
"from PIL import Image, ImageDraw, ImageOps, ImageEnhance # for processing the image data\n",
"import numpy as np\n",
"from random import shuffle\n",
"import nvidia.cudnn\n",
@@ -98,6 +98,7 @@
"\n",
"num_images = len(via_annotations[\"_via_img_metadata\"])\n",
"from tqdm.notebook import tqdm # the progress bar\n",
"import random\n",
"for i, image_data in tqdm(enumerate(via_annotations[\"_via_img_metadata\"].items()), total=num_images):\n",
" image_info = image_data[1]\n",
" image_id = image_data[0]\n",
@@ -166,16 +167,13 @@
" pencil.rectangle(points, \"white\")\n",
" \n",
" # Resize the mask as well\n",
" mask = mask.resize(IMAGE_RESOLUTION, resample=Image.NEAREST)\n",
" \n",
" # Convert them to arrays\n",
" masked = np.asarray(Image.composite(img, black, mask)) / 255.0\n",
" mask = np.asarray(mask) / 255.0\n",
" img = np.asarray(img) / 255.0\n",
" mask = mask.resize(IMAGE_RESOLUTION, resample=Image.NEAREST).convert(\"1\")\n",
" masked = Image.composite(img, black, mask)\n",
"\n",
" # Plot the three images\n",
" import matplotlib.pyplot as plt\n",
" f, axarr = plt.subplots(1, 3)\n",
" plt.axis(\"on\")\n",
" axarr[0].imshow(img)\n",
" axarr[0].set_title(\"Original\")\n",
" \n",
@@ -186,17 +184,34 @@
" axarr[2].set_title(\"Mask\")\n",
"\n",
" plt.show()\n",
"\n",
" # Convert the PIL image to a NumPy array and normalize\n",
"\n",
" \n",
" # Get the class label from the directory structure\n",
" class_label = os.path.basename(os.path.dirname(filename))\n",
"\n",
" # Convert class label to one-hot encoding\n",
" class_index = CLASS_NAMES.index(class_label) # Assuming you have a list of class labels\n",
" class_index = CLASS_NAMES.index(class_label)\n",
" class_one_hot = to_categorical(class_index, num_classes=CLASS_COUNT)\n",
" \n",
" # Preprocess\n",
" n_copies = random.randint(1, 6)\n",
" f, axarr = plt.subplots(1, n_copies + 1)\n",
" plt.axis(\"off\")\n",
" for j in range(0, n_copies):\n",
" random.seed(i+j // random.randint(1, 4) + random.randint(0, 16777216))\n",
" \n",
" preprocessed = masked.rotate(random.randint(0, 3) * 90 + random.randint(-22, 22))\n",
" sharpness = ImageEnhance.Sharpness(img)\n",
" color = ImageEnhance.Color(img)\n",
" contrast = ImageEnhance.Contrast(img)\n",
" brightness = ImageEnhance.Brightness(img)\n",
" \n",
" sharpness.enhance(random.uniform(0.5, 1.5))\n",
" color.enhance(random.uniform(0.75, 1.25))\n",
" contrast.enhance(random.uniform(0.625, 1.375))\n",
" contrast.enhance(random.uniform(0.5, 1.5))\n",
"\n",
" annotations.append((masked, class_one_hot))\n",
" annotations.append((np.asarray(preprocessed) / 255.0, class_one_hot))\n",
" axarr[j].imshow(preprocessed)\n",
"\n",
"# Shuffle the data\n",
"shuffle(annotations)\n",
@@ -262,20 +277,6 @@
"## training"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4ab166f7-dd8f-4ae2-9062-ded152a31d1e",
"metadata": {},
"outputs": [],
"source": [
"# clear GPU memory\n",
"from tensorflow.python.framework import ops\n",
"\n",
"ops.reset_default_graph()\n",
"tf.keras.backend.clear_session()"
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -292,6 +293,7 @@
" epochs=EPOCHS,\n",
" verbose=0,\n",
" callbacks=[TqdmCallback()],\n",
" validation_split=0.2\n",
")"
]
},
@@ -356,7 +358,9 @@
"cell_type": "code",
"execution_count": null,
"id": "2d6b22f3-1a2b-4094-aecf-b6bd8f5fba02",
"metadata": {},
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"history = model.fit(\n",
@@ -366,6 +370,7 @@
" epochs=EPOCHS,\n",
" verbose=0,\n",
" callbacks=[TqdmCallback()],\n",
" validation_split=0.2\n",
")"
]
},
@@ -396,7 +401,8 @@
"source": [
"from tensorflow.keras.models import load_model\n",
"from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
"import numpy as np"
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
@@ -406,7 +412,7 @@
"metadata": {},
"outputs": [],
"source": [
"model = load_model(\"model.h5\")"
"model = load_model(\"model.h5\", compile=False)"
]
},
{