File size: 7,547 Bytes
346f830 025741c 346f830 025741c 346f830 025741c 346f830 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 | import json
import os
import shutil
import matplotlib.pyplot as plt
import numpy as np
import requests
from sklearn.model_selection import train_test_split
ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
PROCESSED_DIR = os.path.join(ROOT_DIR, 'dataset', 'processed')
QUICKDRAW_DIR = os.path.join(ROOT_DIR, 'dataset', 'quickdraw')
MODEL_DIR = os.path.join(ROOT_DIR, 'model')
os.makedirs(PROCESSED_DIR, exist_ok=True)
CATEGORIES = {
'Animals': [
'bear', 'bee', 'butterfly', 'cat', 'cow', 'crab', 'camel', 'dog',
'dolphin', 'duck', 'elephant', 'fish', 'flamingo', 'frog', 'giraffe',
'hedgehog', 'horse', 'kangaroo', 'lion', 'monkey', 'octopus', 'owl',
'panda', 'penguin', 'pig', 'rabbit', 'shark', 'sheep', 'snake',
'spider', 'tiger', 'whale', 'zebra',
],
'Food': [
'apple', 'banana', 'birthday cake', 'bread', 'carrot', 'cookie',
'donut', 'grapes', 'hamburger', 'hot dog', 'ice cream', 'broccoli',
'mushroom', 'pear', 'pineapple', 'pizza', 'strawberry', 'watermelon',
],
'Vehicles': [
'airplane', 'bicycle', 'bus', 'car', 'firetruck', 'helicopter',
'motorbike', 'cruise ship', 'sailboat', 'submarine', 'train', 'truck',
],
'Objects': [
'backpack', 'book', 'camera', 'chair', 'clock', 'computer', 'cup',
'drums', 'fork', 'guitar', 'hammer', 'hat', 'key', 'knife', 'lantern',
'microphone', 'pencil', 'piano', 'scissors', 'shoe', 'sword', 'umbrella',
],
'Nature': [
'cloud', 'campfire', 'flower', 'leaf', 'lightning', 'moon', 'mountain',
'rainbow', 'snowflake', 'star', 'sun', 'tree',
],
'Buildings': [
'bridge', 'castle', 'door', 'fence', 'house', 'lighthouse', 'windmill',
],
'Body': [
'ear', 'eye', 'face', 'hand', 'nose', 'tooth',
],
'Misc': [
'circle', 'crown', 'diamond', 'bowtie', 'hot air balloon', 'lollipop',
'skull', 'stop sign', 'tornado', 'cactus',
],
}
CLASSES = [cls for group in CATEGORIES.values() for cls in group]
BASE_URL = 'https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/'
def download_data(classes, data_dir=QUICKDRAW_DIR):
os.makedirs(data_dir, exist_ok=True)
for class_name in classes:
file_name = f"{class_name}.npy"
path = os.path.join(data_dir, file_name)
if os.path.exists(path):
print(f"Already exists: {file_name}")
continue
url = BASE_URL + class_name.replace(' ', '%20') + ".npy"
try:
r = requests.get(url, timeout=30)
r.raise_for_status()
with open(path, 'wb') as f:
f.write(r.content)
print(f"Downloaded: {file_name}")
except Exception as e: # pylint: disable=broad-exception-caught
print(f"Failed to download {class_name}: {e}")
def load_data(classes, max_samples_per_class=15000):
x_data, y_data, available_classes = [], [], []
for class_name in classes:
file_path = os.path.join(QUICKDRAW_DIR, f"{class_name}.npy")
if not os.path.exists(file_path):
print(f"Missing file: {class_name}")
continue
data = np.load(file_path)
if data.shape[0] > max_samples_per_class:
indices = np.random.choice(data.shape[0], max_samples_per_class, replace=False)
data = data[indices]
label_idx = len(available_classes)
x_data.append(data)
y_data.extend([label_idx] * data.shape[0])
available_classes.append(class_name)
print(f"Loaded {data.shape[0]} samples for '{class_name}'")
if not x_data:
raise RuntimeError("No data loaded. Check download step.")
x_out = np.concatenate(x_data, axis=0).reshape(-1, 28, 28, 1).astype(np.float32) / 255.0
y_out = np.array(y_data)
return x_out, y_out, available_classes
def visualize_samples(x_data, y_data, classes, samples_per_class=5):
_, axes = plt.subplots( # pylint: disable=too-many-function-args
len(classes), samples_per_class,
figsize=(samples_per_class * 2, len(classes) * 2)
)
for class_idx, class_name in enumerate(classes):
indices = np.where(y_data == class_idx)[0]
samples = np.random.choice(indices, samples_per_class, replace=False)
for i, idx in enumerate(samples):
ax = axes[class_idx, i]
ax.imshow(x_data[idx].squeeze(), cmap='gray')
ax.axis('off')
if i == 0:
ax.set_title(class_name, fontsize=10)
plt.tight_layout()
output_path = os.path.join(PROCESSED_DIR, "sample_drawings.png")
plt.savefig(output_path, dpi=150)
plt.close()
print(f"Saved sample visualization to: {output_path}")
def split_and_save(x_data, y_data):
x_temp, x_test, y_temp, y_test = train_test_split(
x_data, y_data, test_size=0.2, stratify=y_data, random_state=42
)
x_train, x_val, y_train, y_val = train_test_split(
x_temp, y_temp, test_size=0.125, stratify=y_temp, random_state=42
)
np.save(os.path.join(PROCESSED_DIR, 'X_train.npy'), x_train)
np.save(os.path.join(PROCESSED_DIR, 'X_val.npy'), x_val)
np.save(os.path.join(PROCESSED_DIR, 'X_test.npy'), x_test)
np.save(os.path.join(PROCESSED_DIR, 'y_train.npy'), y_train)
np.save(os.path.join(PROCESSED_DIR, 'y_val.npy'), y_val)
np.save(os.path.join(PROCESSED_DIR, 'y_test.npy'), y_test)
print(f"Saved datasets: {x_train.shape[0]} train, {x_val.shape[0]} val, {x_test.shape[0]} test")
def save_class_mappings(classes):
os.makedirs(MODEL_DIR, exist_ok=True)
class_to_idx = {cls: i for i, cls in enumerate(classes)}
idx_to_class = dict(enumerate(classes))
with open(os.path.join(PROCESSED_DIR, 'class_name_to_index.json'), 'w', encoding='utf-8') as f:
json.dump(class_to_idx, f, indent=2)
with open(os.path.join(PROCESSED_DIR, 'index_to_class_name.json'), 'w', encoding='utf-8') as f:
json.dump(idx_to_class, f, indent=2)
shutil.copyfile(
os.path.join(PROCESSED_DIR, 'index_to_class_name.json'),
os.path.join(MODEL_DIR, 'classes.json')
)
print("Saved class mappings")
def update_readme_classes(available_classes):
readme_path = os.path.join(ROOT_DIR, 'README.md')
available_set = set(available_classes)
lines = []
total = len(available_classes)
lines.append(f"{total} categories across {len(CATEGORIES)} groups:")
for group, members in CATEGORIES.items():
present = [m for m in members if m in available_set]
if present:
lines.append(f"**{group}**: {', '.join(present)}")
new_section = '\n'.join(lines)
with open(readme_path, 'r', encoding='utf-8') as f:
content = f.read()
import re
pattern = r'(## Supported Categories\n\n).*?(\n## )'
replacement = r'\g<1>' + new_section + r'\n\2'
new_content = re.sub(pattern, replacement, content, flags=re.DOTALL)
with open(readme_path, 'w', encoding='utf-8') as f:
f.write(new_content)
print(f"Updated README.md with {total} classes")
def main():
print("Preparing QuickDraw dataset...")
download_data(CLASSES)
x_data, y_data, available_classes = load_data(CLASSES)
visualize_samples(x_data, y_data, available_classes)
split_and_save(x_data, y_data)
save_class_mappings(available_classes)
update_readme_classes(available_classes)
print("Done. Run scripts/train_model.py to train the model.")
if __name__ == "__main__":
main()
|