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5a9df95 947d8e5 5a9df95 947d8e5 5a9df95 947d8e5 5a9df95 947d8e5 5a9df95 947d8e5 5a9df95 947d8e5 5a9df95 947d8e5 5a9df95 947d8e5 5a9df95 947d8e5 5a9df95 947d8e5 5a9df95 947d8e5 5a9df95 b6437c4 | 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 | import numpy as np
import cv2
from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse
from ultralytics import YOLO
import insightface
from PIL import Image, ImageOps
import pillow_heif
import io
# Enable HEIC/HEIF support
pillow_heif.register_heif_opener()
app = FastAPI()
# ----------------------------
# Load Models (CPU mode)
# ----------------------------
yolo = YOLO("yolov8n.pt")
face_model = insightface.app.FaceAnalysis(name="buffalo_l")
face_model.prepare(ctx_id=-1)
# ----------------------------
# Utility: Normalize embedding
# ----------------------------
def normalize(vec):
vec = np.array(vec, dtype=np.float32)
norm = np.linalg.norm(vec)
if norm == 0:
return vec.tolist()
return (vec / norm).tolist()
# ----------------------------
# Decode Image (JPEG/PNG/WEBP/HEIC/HEIF)
# ----------------------------
def decode_image(body: bytes):
# 🔥 Fast path: OpenCV (JPEG/PNG/WebP)
np_arr = np.frombuffer(body, np.uint8)
image = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
if image is not None:
return image
# 🔥 Fallback: Pillow (HEIC/HEIF support)
try:
image = Image.open(io.BytesIO(body))
# Auto-rotate based on EXIF
image = ImageOps.exif_transpose(image)
image = image.convert("RGB")
image_np = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
return image_np
except Exception:
return None
# ----------------------------
# Face Processing
# ----------------------------
def process_image_np(image_np):
results = yolo(image_np)
faces_output = []
for r in results:
boxes = r.boxes
for box, cls, conf in zip(boxes.xyxy, boxes.cls, boxes.conf):
if int(cls) != 0: # YOLO class 0 = person
continue
if float(conf) < 0.4:
continue
xmin, ymin, xmax, ymax = map(int, box.cpu().numpy())
h, w, _ = image_np.shape
xmin = max(0, xmin)
ymin = max(0, ymin)
xmax = min(w, xmax)
ymax = min(h, ymax)
person_crop = image_np[ymin:ymax, xmin:xmax]
if person_crop.size == 0:
continue
detected_faces = face_model.get(person_crop)
for face in detected_faces:
embedding = normalize(face.embedding)
fxmin, fymin, fxmax, fymax = face.bbox.astype(int)
faces_output.append({
"cx": float((fxmin + fxmax) / 2 + xmin),
"cy": float((fymin + fymax) / 2 + ymin),
"confidence": float(conf),
"box": {
"xmin": int(fxmin + xmin),
"ymin": int(fymin + ymin),
"xmax": int(fxmax + xmin),
"ymax": int(fymax + ymin)
},
"embedding": embedding
})
return faces_output
# ----------------------------
# API Endpoint
# ----------------------------
@app.post("/detect")
async def detect(request: Request):
body = await request.body()
if not body:
return JSONResponse(
{"error": "Empty request body"},
status_code=400
)
image_np = decode_image(body)
if image_np is None:
return JSONResponse(
{"error": "Unsupported or invalid image format"},
status_code=400
)
result = process_image_np(image_np)
return result
from fastapi import Response
# ----------------------------
# Convert Image To JPEG (For Browser Display)
# ----------------------------
@app.post("/convert")
async def convert(request: Request):
body = await request.body()
if not body:
return JSONResponse(
{"error": "Empty request body"},
status_code=400
)
image_np = decode_image(body)
if image_np is None:
return JSONResponse(
{"error": "Unsupported or invalid image format"},
status_code=400
)
# Convert BGR -> RGB
image_rgb = cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB)
pil_image = Image.fromarray(image_rgb)
buffer = io.BytesIO()
pil_image.save(buffer, format="JPEG", quality=90)
return Response(
content=buffer.getvalue(),
media_type="image/jpeg"
) |