yolo_inference_util.py

import argparse
import sys
from pathlib import Path

import cv2
import numpy as np
import torch
import torch.backends.cudnn as cudnn

from models.experimental import attempt_load
from utils.datasets import LoadImages, LoadStreams
from utils.general import (
    apply_classifier,
    check_img_size,
    check_imshow,
    check_requirements,
    check_suffix,
    colorstr,
    increment_path,
    is_ascii,
    non_max_suppression,
    save_one_box,
    scale_coords,
    set_logging,
    strip_optimizer,
    xyxy2xywh,
)
from utils.plots import Annotator, colors
from utils.torch_utils import load_classifier, select_device, time_sync

# FILE = Path(__file__).resolve()
# ROOT = FILE.parents[0]  # YOLOv5 root directory
# if str(ROOT) not in sys.path:
#     sys.path.append(str(ROOT))  # add ROOT to PATH



@torch.no_grad()
def run_yolo_v5(
    weights="yolov5s.pt",  # model.pt path(s)
    source="data/images",  # file/dir/URL/glob, 0 for webcam
    imgsz=640,  # inference size (pixels)
    conf_thres=0.25,  # confidence threshold
    iou_thres=0.45,  # NMS IOU threshold
    max_det=1000,  # maximum detections per image
    device="",  # cuda device, i.e. 0 or 0,1,2,3 or cpu
    view_img=False,  # show results
    save_txt=False,  # save results to *.txt
    save_conf=False,  # save confidences in --save-txt labels
    save_crop=False,  # save cropped prediction boxes
    nosave=False,  # do not save images/videos
    classes=None,  # filter by class: --class 0, or --class 0 2 3
    agnostic_nms=False,  # class-agnostic NMS
    augment=False,  # augmented inference
    visualize=False,  # visualize features
    update=False,  # update all models
    project="runs/detect",  # save results to project/name
    name="exp",  # save results to project/name
    exist_ok=False,  # existing project/name ok, do not increment
    line_thickness=3,  # bounding box thickness (pixels)
    hide_labels=False,  # hide labels
    hide_conf=False,  # hide confidences
    half=False,  # use FP16 half-precision inference
):
    save_img = not nosave and not source.endswith(
        ".txt"
    )  # save inference images
    webcam = (
        source.isnumeric()
        or source.endswith(".txt")
        or source.lower().startswith(
            ("rtsp://", "rtmp://", "http://", "https://")
        )
    )

    # Directories
    save_dir = increment_path(
        Path(project) / name, exist_ok=exist_ok
    )  # increment run
    (save_dir / "labels" if save_txt else save_dir).mkdir(
        parents=True, exist_ok=True
    )  # make dir

    # Initialize
    set_logging()
    device = select_device(device)
    half &= device.type != "cpu"  # half precision only supported on CUDA

    # Load model
    w = weights[0] if isinstance(weights, list) else weights
    classify, suffix, suffixes = (
        False,
        Path(w).suffix.lower(),
        [".pt", ".onnx", ".tflite", ".pb", ""],
    )
    check_suffix(w, suffixes)  # check weights have acceptable suffix
    pt, onnx, tflite, pb, saved_model = (
        suffix == x for x in suffixes
    )  # backend booleans
    stride, names = 64, [f"class{i}" for i in range(1000)]  # assign defaults
    if pt:
        model = attempt_load(weights, map_location=device)  # load FP32 model
        stride = int(model.stride.max())  # model stride
        names = (
            model.module.names if hasattr(model, "module") else model.names
        )  # get class names
        if half:
            model.half()  # to FP16
        if classify:  # second-stage classifier
            modelc = load_classifier(name="resnet50", n=2)  # initialize
            modelc.load_state_dict(
                torch.load("resnet50.pt", map_location=device)["model"]
            ).to(device).eval()
    elif onnx:
        check_requirements(("onnx", "onnxruntime"))
        import onnxruntime

        session = onnxruntime.InferenceSession(w, None)
    else:  # TensorFlow models
        check_requirements(("tensorflow>=2.4.1",))
        import tensorflow as tf

        if (
            pb
        ):  # https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt

            def wrap_frozen_graph(gd, inputs, outputs):
                x = tf.compat.v1.wrap_function(
                    lambda: tf.compat.v1.import_graph_def(gd, name=""), []
                )  # wrapped import
                return x.prune(
                    tf.nest.map_structure(x.graph.as_graph_element, inputs),
                    tf.nest.map_structure(x.graph.as_graph_element, outputs),
                )

            graph_def = tf.Graph().as_graph_def()
            graph_def.ParseFromString(open(w, "rb").read())
            frozen_func = wrap_frozen_graph(
                gd=graph_def, inputs="x:0", outputs="Identity:0"
            )
        elif saved_model:
            model = tf.keras.models.load_model(w)
        elif tflite:
            interpreter = tf.lite.Interpreter(
                model_path=w
            )  # load TFLite model
            interpreter.allocate_tensors()  # allocate
            input_details = interpreter.get_input_details()  # inputs
            output_details = interpreter.get_output_details()  # outputs
            int8 = (
                input_details[0]["dtype"] == np.uint8
            )  # is TFLite quantized uint8 model
    imgsz = check_img_size(imgsz, s=stride)  # check image size
    ascii = is_ascii(names)  # names are ascii (use PIL for UTF-8)

    # Dataloader
    print("Loading data from the source", source)
    if webcam:
        view_img = check_imshow()
        cudnn.benchmark = (
            True  # set True to speed up constant image size inference
        )
        dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt)
        bs = len(dataset)  # batch_size
    else:
        dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)
        bs = 1  # batch_size
    vid_path, vid_writer = [None] * bs, [None] * bs

    # Run inference
    if pt and device.type != "cpu":
        model(
            torch.zeros(1, 3, *imgsz)
            .to(device)
            .type_as(next(model.parameters()))
        )  # run once
    dt, seen = [0.0, 0.0, 0.0], 0
    results = []
    for path, img, im0s, vid_cap in dataset:
        t1 = time_sync()
        if onnx:
            img = img.astype("float32")
        else:
            img = torch.from_numpy(img).to(device)
            img = img.half() if half else img.float()  # uint8 to fp16/32
        img = img / 255.0  # 0 - 255 to 0.0 - 1.0
        if len(img.shape) == 3:
            img = img[None]  # expand for batch dim
        t2 = time_sync()
        dt[0] += t2 - t1

        # Inference
        if pt:
            visualize = (
                increment_path(save_dir / Path(path).stem, mkdir=True)
                if visualize
                else False
            )
            pred = model(img, augment=augment, visualize=visualize)[0]
        elif onnx:
            pred = torch.tensor(
                session.run(
                    [session.get_outputs()[0].name],
                    {session.get_inputs()[0].name: img},
                )
            )
        else:  # tensorflow model (tflite, pb, saved_model)
            imn = img.permute(0, 2, 3, 1).cpu().numpy()  # image in numpy
            if pb:
                pred = frozen_func(x=tf.constant(imn)).numpy()
            elif saved_model:
                pred = model(imn, training=False).numpy()
            elif tflite:
                if int8:
                    scale, zero_point = input_details[0]["quantization"]
                    imn = (imn / scale + zero_point).astype(
                        np.uint8
                    )  # de-scale
                interpreter.set_tensor(input_details[0]["index"], imn)
                interpreter.invoke()
                pred = interpreter.get_tensor(output_details[0]["index"])
                if int8:
                    scale, zero_point = output_details[0]["quantization"]
                    pred = (
                        pred.astype(np.float32) - zero_point
                    ) * scale  # re-scale
            pred[..., 0] *= imgsz[1]  # x
            pred[..., 1] *= imgsz[0]  # y
            pred[..., 2] *= imgsz[1]  # w
            pred[..., 3] *= imgsz[0]  # h
            pred = torch.tensor(pred)
        t3 = time_sync()
        dt[1] += t3 - t2

        # NMS
        pred = non_max_suppression(
            pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det
        )
        dt[2] += time_sync() - t3

        # Second-stage classifier (optional)
        if classify:
            pred = apply_classifier(pred, modelc, img, im0s)

        # Process predictions
        for i, det in enumerate(pred):  # per image
            seen += 1
            if webcam:  # batch_size >= 1
                p, s, im0, frame = (
                    path[i],
                    f"{i}: ",
                    im0s[i].copy(),
                    dataset.count,
                )
            else:
                p, s, im0, frame = (
                    path,
                    "",
                    im0s.copy(),
                    getattr(dataset, "frame", 0),
                )

            p = Path(p)  # to Path
            save_path = str(save_dir / p.name)  # img.jpg
            txt_path = str(save_dir / "labels" / p.stem) + (
                "" if dataset.mode == "image" else f"_{frame}"
            )  # img.txt
            s += "%gx%g " % img.shape[2:]  # print string
            gn = torch.tensor(im0.shape)[
                [1, 0, 1, 0]
            ]  # normalization gain whwh
            imc = im0.copy() if save_crop else im0  # for save_crop
            annotator = Annotator(
                im0, line_width=line_thickness, pil=not ascii
            )
            if len(det):
                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_coords(
                    img.shape[2:], det[:, :4], im0.shape
                ).round()
                results.append((im0, det))
                # Print results
                for c in det[:, -1].unique():
                    n = (det[:, -1] == c).sum()  # detections per class
                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string

                # Write results
                for *xyxy, conf, cls in reversed(det):
                    if save_txt:  # Write to file
                        xywh = (
                            (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn)
                            .view(-1)
                            .tolist()
                        )  # normalized xywh
                        line = (
                            (cls, *xywh, conf) if save_conf else (cls, *xywh)
                        )  # label format
                        with open(txt_path + ".txt", "a") as f:
                            f.write(("%g " * len(line)).rstrip() % line + "\n")

                    if save_img or save_crop or view_img:  # Add bbox to image
                        c = int(cls)  # integer class
                        label = (
                            None
                            if hide_labels
                            else (
                                names[c]
                                if hide_conf
                                else f"{names[c]} {conf:.2f}"
                            )
                        )
                        annotator.box_label(xyxy, label, color=colors(c, True))
                        if save_crop:
                            save_one_box(
                                xyxy,
                                imc,
                                file=save_dir
                                / "crops"
                                / names[c]
                                / f"{p.stem}.jpg",
                                BGR=True,
                            )
            # Print time (inference-only)
            print(f"{s}Done. ({t3 - t2:.3f}s)")

            # Stream results
            im0 = annotator.result()
            if view_img:
                cv2.imshow(str(p), im0)
                cv2.waitKey(1)  # 1 millisecond

            # Save results (image with detections)
            if save_img:
                if dataset.mode == "image":
                    cv2.imwrite(save_path, im0)
                else:  # 'video' or 'stream'
                    if vid_path[i] != save_path:  # new video
                        vid_path[i] = save_path
                        if isinstance(vid_writer[i], cv2.VideoWriter):
                            vid_writer[
                                i
                            ].release()  # release previous video writer
                        if vid_cap:  # video
                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                        else:  # stream
                            fps, w, h = 30, im0.shape[1], im0.shape[0]
                            save_path += ".mp4"
                        vid_writer[i] = cv2.VideoWriter(
                            save_path,
                            cv2.VideoWriter_fourcc(*"mp4v"),
                            fps,
                            (w, h),
                        )
                    vid_writer[i].write(im0)

    # Print results
    t = tuple(x / seen * 1e3 for x in dt)  # speeds per image
    print(
        f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}"
        % t
    )
    return results
    # if save_txt or save_img:
    #     s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
    #     print(f"Results saved to {colorstr('bold', save_dir)}{s}")
    # if update:
    #     strip_optimizer(weights)  # update model (to fix SourceChangeWarning)