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b177e35 144ebc5 b177e35 144ebc5 b177e35 144ebc5 b177e35 144ebc5 b177e35 144ebc5 b177e35 144ebc5 b177e35 144ebc5 2dc1b1e 144ebc5 2dc1b1e b177e35 144ebc5 b177e35 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 | # from fastapi import APIRouter, UploadFile, File, Form, HTTPException
# from pathlib import Path
# import cv2
# import numpy as np
# from .config import UPLOAD_DIR
# from .utils import (
# validate_form,
# process_image,
# save_image,
# load_json,
# save_json,
# validate_user_and_camera,extract_metadata
# )
# router = APIRouter()
# @router.post("/predict")
# async def predict(
# user_id: str = Form(...),
# camera_name: str = Form(...),
# images: list[UploadFile] = File(...)
# ):
# images = validate_form(user_id, camera_name, images)
# validate_user_and_camera(user_id, camera_name)
# base = Path(UPLOAD_DIR) / user_id / camera_name
# base.mkdir(parents=True, exist_ok=True)
# json_path = base / f"{camera_name}_detections.json"
# data = load_json(json_path)
# new_results = []
# for file in images:
# raw = await file.read()
# metadata = extract_metadata(raw)
# nparr = np.frombuffer(raw, np.uint8)
# img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
# if img is None:
# raise HTTPException(400, f"Invalid image: {file.filename}")
# detections = process_image(img)
# url = save_image(user_id, camera_name, file.filename, raw)
# record = {
# "filename": file.filename,
# "image_url": url,
# "detections": detections,
# "metadata": metadata
# }
# data.append(record)
# new_results.append(record)
# save_json(json_path, data)
# return {
# "message": "Images processed successfully",
# "camera": camera_name,
# "results": new_results
# }
from fastapi import APIRouter, UploadFile, File, Form, HTTPException
from pydantic import BaseModel
from pathlib import Path
from typing import Optional, List
import cv2
import numpy as np
import logging
from .config import UPLOAD_DIR
from .utils import (
validate_form,
process_image,
save_image,
load_json,
save_json,
validate_user_and_camera,
extract_metadata
)
router = APIRouter()
logger = logging.getLogger(__name__)
# ─── existing endpoint (unchanged) ───────────────────────────────────────────
@router.post("/predict")
async def predict(
user_id: str = Form(...),
camera_name: str = Form(...),
images: list[UploadFile] = File(...)
):
images = validate_form(user_id, camera_name, images)
validate_user_and_camera(user_id, camera_name)
base = Path(UPLOAD_DIR) / user_id / camera_name
base.mkdir(parents=True, exist_ok=True)
json_path = base / f"{camera_name}_detections.json"
data = load_json(json_path)
new_results = []
for file in images:
raw = await file.read()
metadata = extract_metadata(raw)
nparr = np.frombuffer(raw, np.uint8)
img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
if img is None:
raise HTTPException(400, f"Invalid image: {file.filename}")
detections = process_image(img)
url = save_image(user_id, camera_name, file.filename, raw)
record = {
"filename": file.filename,
"image_url": url,
"detections": detections,
"metadata": metadata
}
data.append(record)
new_results.append(record)
save_json(json_path, data)
return {
"message": "Images processed successfully",
"camera": camera_name,
"results": new_results
}
# ─── NEW: request model ───────────────────────────────────────────────────────
class UpdateDetectionRequest(BaseModel):
user_id: str
camera_name: str
image_url: str # used to locate the record
detection_index: int = 0 # which detection inside the image to edit
new_label: str # e.g. "Buck", "Doe", "Unknown"
new_bbox: Optional[List[float]] = None # [x1,y1,x2,y2] in natural px, or null
# ─── NEW: endpoint ────────────────────────────────────────────────────────────
@router.post("/update_detection")
async def update_detection(req: UpdateDetectionRequest):
"""
Edit the label and/or bounding box of one detection on an already-processed image.
Writes the change back into user_data/<user_id>/<camera_name>/<camera_name>_detections.json
"""
# ── 1. First check if user directory exists ─────────────────────────
user_path = Path(UPLOAD_DIR) / req.user_id
if not user_path.exists() or not user_path.is_dir():
raise HTTPException(
status_code=404,
detail="user not found"
)
# ── 2. Check if camera directory exists ────────────────────────────
camera_path = user_path / req.camera_name
if not camera_path.exists() or not camera_path.is_dir():
raise HTTPException(
status_code=404,
detail="camera not found"
)
# ── 3. Check if detections JSON file exists ─────────────────────────
json_path = camera_path / f"{req.camera_name}_detections.json"
if not json_path.exists():
raise HTTPException(
status_code=404,
detail="camera not found" # Camera ke hisaab se JSON file nahi hai
)
# ── 4. Load the JSON data ───────────────────────────────────────────
data = load_json(json_path)
# ── 5. Find the record by matching the filename ─────────────────────
target_filename = req.image_url.split("/")[-1].split("?")[0]
record = None
for item in data:
stored = item.get("image_url", item.get("filename", ""))
stored_filename = stored.split("/")[-1].split("?")[0]
if stored_filename == target_filename:
record = item
break
if record is None:
raise HTTPException(
status_code=404,
detail="image not found"
)
# ── 6. Apply the edit ───────────────────────────────────────────────
dets = record.get("detections", [])
if not dets:
# Image had zero detections before — create the first one manually
record["detections"] = [{
"label": req.new_label,
"confidence": 1.0,
"bbox": req.new_bbox or [],
"manually_edited": True
}]
elif req.detection_index < len(dets):
# Normal case: update the detection at the requested index
dets[req.detection_index]["label"] = req.new_label
dets[req.detection_index]["manually_edited"] = True
if req.new_bbox is not None:
dets[req.detection_index]["bbox"] = req.new_bbox
else:
raise HTTPException(
status_code=400,
detail=f"detection_index {req.detection_index} is out of range "
f"(image has {len(dets)} detection(s))"
)
# ── 7. Save back ────────────────────────────────────────────────────
save_json(json_path, data)
logger.info(
"Detection updated | user=%s camera=%s file=%s idx=%d label=%s bbox=%s",
req.user_id, req.camera_name, target_filename,
req.detection_index, req.new_label, req.new_bbox
)
return {
"success": True,
"message": "Detection updated successfully",
"updated": {
"filename": target_filename,
"detection_index": req.detection_index,
"new_label": req.new_label,
"new_bbox": req.new_bbox,
}
} |