AI Camera - IMX500 • Re: CCTV security environment

Code:

#!/usr/bin/env python3"""Based on imx500_object_detection_demo.py."""import argparseimport sysfrom functools import lru_cacheimport cv2import numpy as npimport timeimport datetimefrom picamera2 import MappedArray, Picamera2, Previewfrom picamera2.devices import IMX500from picamera2.devices.imx500 import (NetworkIntrinsics,postprocess_nanodet_detection)from picamera2.encoders import H264Encoderfrom picamera2.outputs import CircularOutput2, PyavOutputfrom libcamera import controls# detection objectsobjects      = ["cat","bear","clock","person"]threshold    = 0.5   # set detection threshold   # video settingsv_width      = 2028  # video widthv_height     = 1520  # video heightv_length     = 5     # secondsshow_detects = 0     # show detections on video# initialiselast_detections = []label    = " ( "encoding = Falseclass Detection:    def __init__(self, coords, category, conf, metadata):        """Create a Detection object, recording the bounding box, category and confidence."""        self.category = category        self.conf = conf        self.box = imx500.convert_inference_coords(coords, metadata, picam2)    def parse_detections(metadata: dict):    """Parse the output tensor into a number of detected objects, scaled to the ISP output."""    global last_detections    bbox_normalization = intrinsics.bbox_normalization    bbox_order = intrinsics.bbox_order    threshold = args.threshold    iou = args.iou    max_detections = args.max_detections    np_outputs = imx500.get_outputs(metadata, add_batch=True)    input_w, input_h = imx500.get_input_size()    if np_outputs is None:        return last_detections    if intrinsics.postprocess == "nanodet":        boxes, scores, classes = \            postprocess_nanodet_detection(outputs=np_outputs[0], conf=threshold, iou_thres=iou,                                          max_out_dets=max_detections)[0]        from picamera2.devices.imx500.postprocess import scale_boxes        boxes = scale_boxes(boxes, 1, 1, input_h, input_w, False, False)    else:        boxes, scores, classes = np_outputs[0][0], np_outputs[1][0], np_outputs[2][0]        if bbox_normalization:            boxes = boxes / input_h        if bbox_order == "xy":            boxes = boxes[:, [1, 0, 3, 2]]        boxes = np.array_split(boxes, 4, axis=1)        boxes = zip(*boxes)    last_detections = [        Detection(box, category, score, metadata)        for box, score, category in zip(boxes, scores, classes)        if score > threshold    ]    return last_detections@lru_cachedef get_labels():    labels = intrinsics.labels    if intrinsics.ignore_dash_labels:        labels = [label for label in labels if label and label != "-"]    return labelsdef draw_detections(request, stream="main"):    """Draw the detections for this request onto the ISP output."""    global label,show_detects, mp4_anno,scale    detections = last_results    if detections is None:        return    labels = get_labels()    with MappedArray(request, stream) as m:        for detection in detections:            x, y, w, h = detection.box            label = f"{labels[int(detection.category)]} ({detection.conf:.2f})"            if show_detects == 1:                # Calculate text size and position                (text_width, text_height), baseline = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)                text_x = x + 5                text_y = y + 15                # Create a copy of the array to draw the background with opacity                overlay = m.array.copy()                # Draw the background rectangle on the overlay                cv2.rectangle(overlay,                          (text_x, text_y - text_height),                          (text_x + text_width, text_y + baseline),                          (255, 255, 255),  # Background color (white)                          cv2.FILLED)                alpha = 0.30                cv2.addWeighted(overlay, alpha, m.array, 1 - alpha, 0, m.array)                # Draw text on top of the background                cv2.putText(m.array, label, (text_x, text_y),                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)                # Draw detection box                cv2.rectangle(m.array, (x, y), (x + w, y + h), (0, 255, 0, 0), thickness=2)        if intrinsics.preserve_aspect_ratio:            b_x, b_y, b_w, b_h = imx500.get_roi_scaled(request)            color = (255, 0, 0)  # red            cv2.putText(m.array, "ROI", (b_x + 5, b_y + 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 1)            cv2.rectangle(m.array, (b_x, b_y), (b_x + b_w, b_y + b_h), (255, 0, 0, 0))def get_args():    parser = argparse.ArgumentParser()    parser.add_argument("--model", type=str, help="Path of the model",                        default="/usr/share/imx500-models/imx500_network_ssd_mobilenetv2_fpnlite_320x320_pp.rpk")    parser.add_argument("--fps", type=int, help="Frames per second")    parser.add_argument("--bbox-normalization", action=argparse.BooleanOptionalAction, help="Normalize bbox")    parser.add_argument("--bbox-order", choices=["yx", "xy"], default="yx",                        help="Set bbox order yx -> (y0, x0, y1, x1) xy -> (x0, y0, x1, y1)")    parser.add_argument("--threshold", type=float, default=0.55, help="Detection threshold")    parser.add_argument("--iou", type=float, default=0.65, help="Set iou threshold")    parser.add_argument("--max-detections", type=int, default=10, help="Set max detections")    parser.add_argument("--ignore-dash-labels", action=argparse.BooleanOptionalAction, help="Remove '-' labels ")    parser.add_argument("--postprocess", choices=["", "nanodet"],                        default=None, help="Run post process of type")    parser.add_argument("-r", "--preserve-aspect-ratio", action=argparse.BooleanOptionalAction,                        help="preserve the pixel aspect ratio of the input tensor")    parser.add_argument("--labels", type=str,                        help="Path to the labels file")    parser.add_argument("--print-intrinsics", action="store_true",                        help="Print JSON network_intrinsics then exit")    return parser.parse_args()if __name__ == "__main__":    args = get_args()    # This must be called before instantiation of Picamera2    imx500 = IMX500(args.model)    intrinsics = imx500.network_intrinsics    if not intrinsics:        intrinsics = NetworkIntrinsics()        intrinsics.task = "object detection"    elif intrinsics.task != "object detection":        print("Network is not an object detection task", file=sys.stderr)        exit()    # Override intrinsics from args    for key, value in vars(args).items():        if key == 'labels' and value is not None:            with open(value, 'r') as f:                intrinsics.labels = f.read().splitlines()        elif hasattr(intrinsics, key) and value is not None:            setattr(intrinsics, key, value)    # Defaults    if intrinsics.labels is None:        with open("assets/coco_labels.txt", "r") as f:            intrinsics.labels = f.read().splitlines()    intrinsics.update_with_defaults()    if args.print_intrinsics:        print(intrinsics)        exit()    # Configure and start Picamera2.    model_h, model_w = imx500.get_input_size()    video_w, video_h = v_width,v_height    main  = {'size': (video_w, video_h), 'format': 'YUV420'}    lores = {'size': (model_w, model_h), 'format': 'YUV420'}    picam2 = Picamera2(imx500.camera_num)    config = picam2.create_preview_configuration(main, lores=lores,controls={"FrameRate": intrinsics.inference_rate}, buffer_count=12)    imx500.show_network_fw_progress_bar()    picam2.configure(config)    encoder = H264Encoder(bitrate=2000000)    circular = CircularOutput2(buffer_duration_ms=5000)    picam2.start_preview(Preview.QTGL, x=0, y=0, width=480, height=480)    picam2.start_recording(encoder, circular)    if intrinsics.preserve_aspect_ratio:        imx500.set_auto_aspect_ratio()    last_results = None    picam2.pre_callback = draw_detections    while True:        last_results = parse_detections(picam2.capture_metadata())        # capture frame        frame = picam2.capture_array('lores')        frame = cv2.cvtColor(frame, cv2.COLOR_YUV420p2RGB)        frame = frame[0:320, 0:320]        # detected label        data = label.split("(")        category = data[0][:-1]        value = data[1][:-1]        if category in objects and float(value) > threshold:            # restart timer            startrec = time.monotonic()            # start recording            if not encoding:                encoding = True                now = datetime.datetime.now()                timestamp = now.strftime("%y%m%d_%H%M%S")                print("New  Detection",timestamp,label)                circular.open_output(PyavOutput("/run/shm/" + timestamp +".mp4"))        # stop recording after v_length + 5 seconds to empty circular buffer        if encoding and (time.monotonic() - startrec > v_length + 5):            now = datetime.datetime.now()            timestamp2 = now.strftime("%y%m%d_%H%M%S")            print("Stopped Record", timestamp2)            circular.close_output()            encoding = False        category = ""        value = 0.0        label = " ( "