Bernt
bae705aa97
- Add NFC ePassport roadmap (ICAO 9303, eIDAS) - Add TensorFlow.js edge face detection (BlazeFace) - Add structured audit logger (GDPR-compliant) - Risk scoring support Part of KYC Apple Native UX v1.1.0
77 lines
2.1 KiB
Python
77 lines
2.1 KiB
Python
"""
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Create dummy images for training
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Generate synthetic infrastructure images
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"""
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from PIL import Image, ImageDraw
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import random
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import os
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def create_dummy_image(filename, width=640, height=480, num_objects=3):
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"""Create a dummy infrastructure image"""
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# Create base image
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img = Image.new('RGB', (width, height), color=(135, 206, 235)) # Sky blue
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draw = ImageDraw.Draw(img)
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# Add ground
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draw.rectangle([0, height*0.7, width, height], fill=(100, 80, 60)) # Brown ground
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# Add random objects
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colors = [
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(255, 0, 0), # Red
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(0, 255, 0), # Green
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(0, 0, 255), # Blue
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(255, 255, 0), # Yellow
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(255, 0, 255), # Magenta
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]
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for i in range(num_objects):
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x = random.randint(50, width-100)
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y = random.randint(50, height-150)
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w = random.randint(30, 100)
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h = random.randint(30, 100)
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color = colors[i % len(colors)]
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# Draw object
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draw.rectangle([x, y, x+w, y+h], fill=color, outline=(0, 0, 0), width=2)
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# Add label
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draw.text((x, y-15), f"Object {i+1}", fill=(0, 0, 0))
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# Save image
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img.save(filename)
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return filename
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def create_training_dataset(base_path="/tmp/iom_training_data", num_train=50, num_val=10, num_test=10):
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"""Create complete training dataset with images"""
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print("=== Creating Dummy Training Images ===\n")
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# Create directories
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splits = {
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"train": num_train,
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"val": num_val,
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"test": num_test
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}
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for split, count in splits.items():
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img_dir = os.path.join(base_path, "images", split)
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os.makedirs(img_dir, exist_ok=True)
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print(f"Creating {count} {split} images...")
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for i in range(count):
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filename = os.path.join(img_dir, f"{split}_{i:04d}.jpg")
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create_dummy_image(filename, num_objects=random.randint(1, 5))
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print(f"\nDataset created:")
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print(f" Train: {num_train} images")
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print(f" Val: {num_val} images")
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print(f" Test: {num_test} images")
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return base_path
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if __name__ == '__main__':
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create_training_dataset()
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