3467dbcf03
Full-stack application for predicting where humans look in images using DeepGaze saliency models. Includes heatmap overlays, gaze sequence prediction, hotspot detection, AOI analysis, rule-based insights, optional Claude AI design analysis, and professional PDF report generation. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
24 lines
832 B
Python
24 lines
832 B
Python
import numpy as np
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from PIL import Image
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from scipy.ndimage import zoom
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MAX_INFERENCE_SIZE = 1024
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def prepare_for_inference(image: Image.Image) -> tuple[Image.Image, float]:
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"""Resize for model input. Returns (resized_image, scale_factor)."""
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w, h = image.size
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scale = MAX_INFERENCE_SIZE / max(w, h)
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if scale < 1.0:
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new_size = (int(w * scale), int(h * scale))
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return image.resize(new_size, Image.LANCZOS), scale
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return image, 1.0
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def upscale_saliency(saliency: np.ndarray, target_h: int, target_w: int) -> np.ndarray:
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"""Upscale saliency map to original image dimensions."""
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if saliency.shape == (target_h, target_w):
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return saliency
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h_scale = target_h / saliency.shape[0]
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w_scale = target_w / saliency.shape[1]
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return zoom(saliency, (h_scale, w_scale), order=1)
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