baoshi-measure-cargo/api.py

import time
import threading

import numpy as np
import cv2
from fastapi import FastAPI, HTTPException

from pyorbbecsdk import *

ESC_KEY = 27
PRINT_INTERVAL = 1  # seconds
MIN_DEPTH = 500  # mm
MAX_DEPTH = 4000  # mm
ROI_WIDTH_CM = 10.0  # cm
ROI_HEIGHT_CM = 12.0  # cm
MEDIAN_BLUR_KSIZE = 5  # odd number, 0 to disable
MORPH_OPEN_KSIZE = 3  # odd number, 0 to disable
NEAREST_PERCENTILE = 5  # use low percentile to suppress isolated noise (0 for raw min)

SAMPLE_COUNT = 10
FRAME_TIMEOUT_MS = 200
SAMPLE_TIMEOUT_SEC = 8

app = FastAPI(title="Cargo Height API")


class TemporalFilter:
    def __init__(self, alpha):
        self.alpha = alpha
        self.previous_frame = None

    def process(self, frame):
        if self.previous_frame is None:
            result = frame
        else:
            result = cv2.addWeighted(frame, self.alpha, self.previous_frame, 1 - self.alpha, 0)
        self.previous_frame = result
        return result


_pipeline = None
_depth_intrinsics = None
_temporal_filter = None
_lock = threading.Lock()


def _init_camera():
    global _pipeline, _depth_intrinsics, _temporal_filter
    if _pipeline is not None:
        return
    config = Config()
    pipeline = Pipeline()
    try:
        profile_list = pipeline.get_stream_profile_list(OBSensorType.DEPTH_SENSOR)
        if profile_list is None:
            raise RuntimeError("depth profile list is empty")
        depth_profile = profile_list.get_default_video_stream_profile()
        if depth_profile is None:
            raise RuntimeError("default depth profile is empty")
        _depth_intrinsics = depth_profile.get_intrinsic()
        config.enable_stream(depth_profile)
    except Exception as exc:
        raise RuntimeError(f"Failed to init depth camera: {exc}") from exc
    pipeline.start(config)
    _pipeline = pipeline
    _temporal_filter = TemporalFilter(alpha=0.5)


def _shutdown_camera():
    global _pipeline
    if _pipeline is None:
        return
    _pipeline.stop()
    _pipeline = None


def _measure_once():
    frames = _pipeline.wait_for_frames(FRAME_TIMEOUT_MS)
    if frames is None:
        return None
    depth_frame = frames.get_depth_frame()
    if depth_frame is None:
        return None
    depth_format = depth_frame.get_format()
    if depth_format != OBFormat.Y16:
        return None

    width = depth_frame.get_width()
    height = depth_frame.get_height()
    scale = depth_frame.get_depth_scale()

    depth_data = np.frombuffer(depth_frame.get_data(), dtype=np.uint16)
    depth_data = depth_data.reshape((height, width))

    depth_data = depth_data.astype(np.float32) * scale
    depth_data = np.where((depth_data > MIN_DEPTH) & (depth_data < MAX_DEPTH), depth_data, 0)
    depth_data = depth_data.astype(np.uint16)
    if MEDIAN_BLUR_KSIZE and MEDIAN_BLUR_KSIZE % 2 == 1:
        depth_data = cv2.medianBlur(depth_data, MEDIAN_BLUR_KSIZE)
    if MORPH_OPEN_KSIZE and MORPH_OPEN_KSIZE % 2 == 1:
        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (MORPH_OPEN_KSIZE, MORPH_OPEN_KSIZE))
        valid_mask = (depth_data > 0).astype(np.uint8)
        valid_mask = cv2.morphologyEx(valid_mask, cv2.MORPH_OPEN, kernel)
        depth_data = np.where(valid_mask > 0, depth_data, 0).astype(np.uint16)
    depth_data = _temporal_filter.process(depth_data)

    center_y = height // 2
    center_x = width // 2
    center_distance = depth_data[center_y, center_x]
    if center_distance == 0:
        return None
    center_distance_m = center_distance / 1000.0
    half_width_m = (ROI_WIDTH_CM / 100.0) / 2.0
    half_height_m = (ROI_HEIGHT_CM / 100.0) / 2.0
    half_width_px = int(_depth_intrinsics.fx * half_width_m / center_distance_m)
    half_height_px = int(_depth_intrinsics.fy * half_height_m / center_distance_m)
    if half_width_px <= 0 or half_height_px <= 0:
        return None
    half_width_px = min(half_width_px, center_x, width - center_x - 1)
    half_height_px = min(half_height_px, center_y, height - center_y - 1)
    if half_width_px <= 0 or half_height_px <= 0:
        return None
    x_start = center_x - half_width_px
    x_end = center_x + half_width_px + 1
    y_start = center_y - half_height_px
    y_end = center_y + half_height_px + 1
    roi = depth_data[y_start:y_end, x_start:x_end]
    valid_values = roi[(roi >= MIN_DEPTH) & (roi <= MAX_DEPTH)]
    if valid_values.size == 0:
        return None
    if NEAREST_PERCENTILE and 0 < NEAREST_PERCENTILE < 100:
        return int(np.percentile(valid_values, NEAREST_PERCENTILE))
    return int(valid_values.min())


@app.on_event("startup")
def on_startup():
    _init_camera()


@app.on_event("shutdown")
def on_shutdown():
    _shutdown_camera()


@app.get("/height")
def get_height():
    start_time = time.time()
    samples = []
    with _lock:
        while len(samples) < SAMPLE_COUNT and (time.time() - start_time) < SAMPLE_TIMEOUT_SEC:
            value = _measure_once()
            if value is not None:
                samples.append(value)
    if len(samples) < SAMPLE_COUNT:
        raise HTTPException(status_code=503, detail="Insufficient valid samples from depth camera")
    median_value = int(np.median(np.array(samples, dtype=np.int32)))
    return {
        "height_mm": median_value,
        "samples": samples,
        "unit": "mm",
        "sample_count": SAMPLE_COUNT,
    }


@app.get("/health")
def health():
    return {"status": "ok"}