517 lines
17 KiB
Python
517 lines
17 KiB
Python
"""Benchmark UI for face comparison."""
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import asyncio
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import base64
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import logging
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from io import BytesIO
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import cv2
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import numpy as np
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from fastapi import APIRouter, File, UploadFile
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from fastapi.responses import HTMLResponse
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from app.face import face_area, get_faces_async, validate_embedding
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from app.image import read_upload_image
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from app.resources import inference_executor
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logger = logging.getLogger("face_service")
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router = APIRouter(prefix="/benchmark", tags=["benchmark"])
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def cosine_similarity(emb1: np.ndarray, emb2: np.ndarray) -> float:
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"""Compute cosine similarity between two embeddings."""
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# Embeddings are already normalized, so dot product = cosine similarity
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return float(np.dot(emb1, emb2))
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def draw_faces_on_image(img: np.ndarray, faces: list, face_indices: list[int]) -> np.ndarray:
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"""Draw bounding boxes and indices on image."""
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img_copy = img.copy()
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for idx, face in zip(face_indices, faces):
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bbox = face.bbox.astype(int)
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x1, y1, x2, y2 = bbox
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# Draw rectangle
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cv2.rectangle(img_copy, (x1, y1), (x2, y2), (0, 255, 0), 2)
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# Draw index label
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label = f"#{idx}"
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(tw, th), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)
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cv2.rectangle(img_copy, (x1, y1 - th - 10), (x1 + tw + 10, y1), (0, 255, 0), -1)
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cv2.putText(img_copy, label, (x1 + 5, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 0), 2)
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return img_copy
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def encode_image_to_base64(img: np.ndarray, max_dim: int = 800) -> str:
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"""Encode image to base64 for display in HTML, resizing if needed."""
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h, w = img.shape[:2]
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if max(h, w) > max_dim:
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scale = max_dim / max(h, w)
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img = cv2.resize(img, (int(w * scale), int(h * scale)))
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# Convert BGR to RGB for proper display
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img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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from PIL import Image
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pil_img = Image.fromarray(img_rgb)
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buffer = BytesIO()
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pil_img.save(buffer, format="JPEG", quality=85)
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return base64.b64encode(buffer.getvalue()).decode()
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def crop_face(img: np.ndarray, bbox: np.ndarray, padding: float = 0.2) -> np.ndarray:
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"""Crop face from image with padding."""
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h, w = img.shape[:2]
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x1, y1, x2, y2 = bbox.astype(int)
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# Add padding
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face_w = x2 - x1
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face_h = y2 - y1
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pad_x = int(face_w * padding)
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pad_y = int(face_h * padding)
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x1 = max(0, x1 - pad_x)
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y1 = max(0, y1 - pad_y)
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x2 = min(w, x2 + pad_x)
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y2 = min(h, y2 + pad_y)
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return img[y1:y2, x1:x2]
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@router.get("/", response_class=HTMLResponse)
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async def benchmark_ui():
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"""Serve the benchmark UI."""
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return """
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Face Benchmark</title>
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<style>
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* { box-sizing: border-box; }
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body {
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font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
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max-width: 1400px;
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margin: 0 auto;
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padding: 20px;
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background: #1a1a2e;
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color: #eee;
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}
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h1 { text-align: center; color: #00d4ff; }
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.upload-section {
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display: flex;
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gap: 20px;
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margin-bottom: 20px;
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flex-wrap: wrap;
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}
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.upload-box {
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flex: 1;
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min-width: 300px;
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border: 2px dashed #444;
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border-radius: 10px;
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padding: 20px;
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text-align: center;
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background: #16213e;
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}
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.upload-box h3 { margin-top: 0; color: #00d4ff; }
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.upload-box input[type="file"] {
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display: block;
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margin: 10px auto;
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}
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.preview {
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max-width: 100%;
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max-height: 300px;
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margin-top: 10px;
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border-radius: 5px;
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}
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button {
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display: block;
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width: 100%;
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max-width: 300px;
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margin: 20px auto;
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padding: 15px 30px;
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font-size: 18px;
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background: #00d4ff;
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color: #1a1a2e;
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border: none;
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border-radius: 5px;
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cursor: pointer;
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font-weight: bold;
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}
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button:hover { background: #00b8e6; }
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button:disabled { background: #444; cursor: not-allowed; }
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#results {
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margin-top: 30px;
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}
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.results-grid {
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display: flex;
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gap: 20px;
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flex-wrap: wrap;
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}
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.result-box {
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flex: 1;
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min-width: 300px;
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background: #16213e;
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border-radius: 10px;
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padding: 20px;
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}
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.result-box h3 { margin-top: 0; color: #00d4ff; }
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.result-box img {
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max-width: 100%;
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border-radius: 5px;
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}
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.similarity-table {
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width: 100%;
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margin-top: 20px;
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background: #16213e;
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border-radius: 10px;
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padding: 20px;
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}
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.similarity-table h3 { margin-top: 0; color: #00d4ff; }
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table {
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width: 100%;
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border-collapse: collapse;
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margin-top: 10px;
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}
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th, td {
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padding: 10px;
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text-align: center;
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border: 1px solid #333;
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}
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th { background: #0f3460; }
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.match-high { background: #00c853; color: #000; font-weight: bold; }
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.match-medium { background: #ffab00; color: #000; }
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.match-low { background: #333; }
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.loading {
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text-align: center;
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padding: 40px;
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font-size: 18px;
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}
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.spinner {
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border: 4px solid #333;
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border-top: 4px solid #00d4ff;
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border-radius: 50%;
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width: 40px;
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height: 40px;
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animation: spin 1s linear infinite;
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margin: 20px auto;
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}
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@keyframes spin {
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0% { transform: rotate(0deg); }
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100% { transform: rotate(360deg); }
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}
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.error { color: #ff5252; text-align: center; padding: 20px; }
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.stats { margin-top: 10px; font-size: 14px; color: #888; }
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.best-match {
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background: #16213e;
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border-radius: 10px;
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padding: 20px;
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margin-top: 20px;
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}
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.best-match h3 { color: #00d4ff; margin-top: 0; }
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.match-item {
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display: flex;
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align-items: center;
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gap: 15px;
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padding: 15px;
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background: #0f3460;
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border-radius: 8px;
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margin-bottom: 15px;
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}
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.match-score {
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font-size: 28px;
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font-weight: bold;
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min-width: 90px;
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text-align: center;
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}
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.face-crop {
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width: 100px;
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height: 100px;
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object-fit: cover;
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border-radius: 8px;
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border: 2px solid #00d4ff;
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}
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.match-label {
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font-size: 14px;
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color: #888;
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margin-left: auto;
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}
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</style>
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</head>
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<body>
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<h1>Face Comparison Benchmark</h1>
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<form id="uploadForm">
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<div class="upload-section">
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<div class="upload-box">
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<h3>Image 1 (Source)</h3>
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<p>Upload an image to find faces from</p>
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<input type="file" id="image1" name="image1" accept="image/*" required>
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<img id="preview1" class="preview" style="display:none;">
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</div>
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<div class="upload-box">
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<h3>Image 2 (Target)</h3>
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<p>Upload an image to search for matching faces</p>
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<input type="file" id="image2" name="image2" accept="image/*" required>
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<img id="preview2" class="preview" style="display:none;">
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</div>
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</div>
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<button type="submit" id="compareBtn">Compare Faces</button>
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</form>
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<div id="results"></div>
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<script>
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// Preview images on selection
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document.getElementById('image1').addEventListener('change', function(e) {
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previewImage(e.target, 'preview1');
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});
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document.getElementById('image2').addEventListener('change', function(e) {
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previewImage(e.target, 'preview2');
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});
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function previewImage(input, previewId) {
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const preview = document.getElementById(previewId);
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if (input.files && input.files[0]) {
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const reader = new FileReader();
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reader.onload = function(e) {
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preview.src = e.target.result;
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preview.style.display = 'block';
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};
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reader.readAsDataURL(input.files[0]);
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}
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}
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document.getElementById('uploadForm').addEventListener('submit', async function(e) {
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e.preventDefault();
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const image1 = document.getElementById('image1').files[0];
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const image2 = document.getElementById('image2').files[0];
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if (!image1 || !image2) {
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alert('Please select both images');
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return;
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}
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const resultsDiv = document.getElementById('results');
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const btn = document.getElementById('compareBtn');
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btn.disabled = true;
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btn.textContent = 'Processing...';
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resultsDiv.innerHTML = '<div class="loading"><div class="spinner"></div>Detecting faces and computing embeddings...</div>';
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const formData = new FormData();
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formData.append('image1', image1);
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formData.append('image2', image2);
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try {
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const startTime = performance.now();
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const response = await fetch('/benchmark/compare', {
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method: 'POST',
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body: formData
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});
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const endTime = performance.now();
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if (!response.ok) {
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const error = await response.json();
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throw new Error(error.detail || 'Comparison failed');
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}
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const data = await response.json();
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displayResults(data, endTime - startTime);
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} catch (error) {
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resultsDiv.innerHTML = `<div class="error">Error: ${error.message}</div>`;
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} finally {
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btn.disabled = false;
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btn.textContent = 'Compare Faces';
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}
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});
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function displayResults(data, processingTime) {
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const resultsDiv = document.getElementById('results');
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let html = `
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<div class="stats">Processing time: ${(processingTime/1000).toFixed(2)}s |
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Image 1: ${data.image1_faces} face(s) | Image 2: ${data.image2_faces} face(s)</div>
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<div class="results-grid">
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<div class="result-box">
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<h3>Image 1 - ${data.image1_faces} face(s) detected</h3>
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<img src="data:image/jpeg;base64,${data.image1_annotated}" alt="Image 1">
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</div>
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<div class="result-box">
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<h3>Image 2 - ${data.image2_faces} face(s) detected</h3>
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<img src="data:image/jpeg;base64,${data.image2_annotated}" alt="Image 2">
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</div>
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</div>
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`;
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if (data.similarities.length > 0) {
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html += `
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<div class="best-match">
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<h3>Best Matches (Image 1 faces found in Image 2)</h3>
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`;
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for (const match of data.best_matches) {
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const scorePercent = (match.similarity * 100).toFixed(1);
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const scoreClass = match.similarity >= 0.5 ? 'match-high' :
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match.similarity >= 0.3 ? 'match-medium' : 'match-low';
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html += `
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<div class="match-item">
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<img src="data:image/jpeg;base64,${match.face1_crop}" class="face-crop" alt="Face ${match.face1_idx}">
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<span class="match-score ${scoreClass}">${scorePercent}%</span>
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<img src="data:image/jpeg;base64,${match.face2_crop}" class="face-crop" alt="Face ${match.face2_idx}">
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<span class="match-label">Face #${match.face1_idx} ↔ Face #${match.face2_idx}</span>
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</div>
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`;
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}
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html += '</div>';
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// Similarity matrix
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html += `
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<div class="similarity-table">
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<h3>Full Similarity Matrix</h3>
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<table>
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<tr>
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<th>Image 1 \\ Image 2</th>
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`;
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for (let j = 0; j < data.image2_faces; j++) {
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html += `<th>Face #${j}</th>`;
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}
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html += '</tr>';
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for (let i = 0; i < data.image1_faces; i++) {
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html += `<tr><th>Face #${i}</th>`;
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for (let j = 0; j < data.image2_faces; j++) {
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const sim = data.similarity_matrix[i][j];
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const percent = (sim * 100).toFixed(1);
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const cls = sim >= 0.5 ? 'match-high' : sim >= 0.3 ? 'match-medium' : 'match-low';
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html += `<td class="${cls}">${percent}%</td>`;
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}
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html += '</tr>';
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}
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html += '</table></div>';
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} else if (data.image1_faces === 0 || data.image2_faces === 0) {
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html += '<div class="error">No faces detected in one or both images</div>';
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}
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resultsDiv.innerHTML = html;
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}
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</script>
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</body>
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</html>
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"""
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@router.post("/compare")
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async def compare_faces(
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image1: UploadFile = File(...),
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image2: UploadFile = File(...),
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):
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"""
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Compare faces between two uploaded images.
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Returns:
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- Annotated images with face bounding boxes
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- Similarity matrix between all detected faces
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- Best matches for each face in image1
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"""
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logger.info("benchmark/compare: image1=%s image2=%s", image1.filename, image2.filename)
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# Read both images
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data1 = await image1.read()
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data2 = await image2.read()
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img1 = read_upload_image(data1, image1.filename or "image1")
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img2 = read_upload_image(data2, image2.filename or "image2")
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# Detect faces in both images concurrently
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faces1, faces2 = await asyncio.gather(
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get_faces_async(img1, inference_executor),
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get_faces_async(img2, inference_executor),
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)
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logger.info(
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"benchmark/compare: detected %d faces in image1, %d faces in image2",
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len(faces1), len(faces2)
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)
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# Sort faces by area (largest first)
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faces1.sort(key=face_area, reverse=True)
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faces2.sort(key=face_area, reverse=True)
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# Draw faces on images
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face1_indices = list(range(len(faces1)))
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face2_indices = list(range(len(faces2)))
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img1_annotated = draw_faces_on_image(img1, faces1, face1_indices)
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img2_annotated = draw_faces_on_image(img2, faces2, face2_indices)
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# Encode images for response
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img1_b64 = encode_image_to_base64(img1_annotated)
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img2_b64 = encode_image_to_base64(img2_annotated)
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# Compute similarity matrix
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similarity_matrix: list[list[float]] = []
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best_matches: list[dict] = []
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for i, f1 in enumerate(faces1):
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emb1 = f1.normed_embedding.astype(np.float32)
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if not validate_embedding(emb1):
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similarity_matrix.append([0.0] * len(faces2))
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continue
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row = []
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best_sim = -1.0
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best_j = -1
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for j, f2 in enumerate(faces2):
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emb2 = f2.normed_embedding.astype(np.float32)
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if not validate_embedding(emb2):
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row.append(0.0)
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continue
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sim = cosine_similarity(emb1, emb2)
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row.append(sim)
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if sim > best_sim:
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best_sim = sim
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best_j = j
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similarity_matrix.append(row)
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if best_j >= 0:
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best_matches.append({
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"face1_idx": i,
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"face2_idx": best_j,
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"similarity": best_sim,
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})
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# Sort best matches by similarity and keep top 3
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best_matches.sort(key=lambda m: m["similarity"], reverse=True)
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best_matches = best_matches[:3]
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# Add cropped face images for top 3 matches
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for match in best_matches:
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i, j = match["face1_idx"], match["face2_idx"]
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crop1 = crop_face(img1, faces1[i].bbox)
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crop2 = crop_face(img2, faces2[j].bbox)
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match["face1_crop"] = encode_image_to_base64(crop1, max_dim=150)
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match["face2_crop"] = encode_image_to_base64(crop2, max_dim=150)
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return {
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"image1_faces": len(faces1),
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"image2_faces": len(faces2),
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"image1_annotated": img1_b64,
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"image2_annotated": img2_b64,
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"similarity_matrix": similarity_matrix,
|
|
"similarities": [
|
|
{"face1": i, "face2": j, "score": similarity_matrix[i][j]}
|
|
for i in range(len(faces1))
|
|
for j in range(len(faces2))
|
|
],
|
|
"best_matches": best_matches,
|
|
}
|