detect.py

import argparse
import time
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 numpy import random
from utils.datasets import LoadImages, LoadStreams
from utils.general import (apply_classifier, check_img_size, check_imshow,
                           check_requirements, increment_path,
                           non_max_suppression, scale_coords, set_logging,
                           strip_optimizer, xyxy2xywh)
from utils.plots import plot_one_box
from utils.torch_utils import load_classifier, select_device, time_synchronized


def resize_numpy_image(img, expected_height, expected_width, device):
    """
    Resize a NumPy image to the expected height and width if necessary,
    and return the image as a PyTorch tensor.
    """
    # Check if the image is in (H, W, C) format
    if img.shape[0] == 3:  # If it's in (C, H, W), convert to (H, W, C)
        img = img.transpose(1, 2, 0)

    # Check if resizing is necessary
    if img.shape[0] != expected_height or img.shape[1] != expected_width:
        print(
            f"Resizing image from {img.shape} to ({expected_height}, {expected_width})"
        )
        img_resized_np = cv2.resize(img, (expected_width, expected_height))
    else:
        img_resized_np = img  # Use original image if resizing is not needed

    # Convert the resized NumPy array back to a PyTorch tensor (C, H, W) and move to the specified device
    img_resized = (
        torch.from_numpy(img_resized_np).permute(2, 0, 1).unsqueeze(0).to(device)
    )

    return img_resized


def detect(save_img=False):
    source, weights, view_img, save_txt, imgsz = (
        opt.source,
        opt.weights,
        opt.view_img,
        opt.save_txt,
        opt.img_size,
    )
    save_img = not opt.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 = Path(
        increment_path(Path(opt.project) / opt.name, exist_ok=opt.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(opt.device)
    half = device.type != "cpu"  # half precision only supported on CUDA

    # Load model
    model = attempt_load(weights, map_location=device)  # load FP32 model
    stride = int(model.stride.max())  # model stride
    imgsz = check_img_size(imgsz, s=stride)  # check img_size
    if half:
        model.half()  # to FP16

    # Second-stage classifier
    classify = False
    if classify:
        modelc = load_classifier(name="resnet101", n=2)  # initialize
        modelc.load_state_dict(
            torch.load("weights/resnet101.pt", map_location=device)["model"]
        ).to(device).eval()

    # Set Dataloader
    vid_path, vid_writer = None, None
    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)
    else:
        dataset = LoadImages(source, img_size=imgsz, stride=stride)

    # Get names and colors
    names = model.module.names if hasattr(model, "module") else model.names
    colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]

    # Run inference
    if device.type != "cpu":
        model(
            torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))
        )  # run once
    t0 = time.time()
    
    number_list = []
    
    for path, img, im0s, vid_cap in dataset:
        # Check the image size
        print("Image size:", img.shape)
        print("im0s size:", im0s.shape)
        # Resize the image if necessary
        img = resize_numpy_image(img, imgsz, imgsz, device)
        img = img.half() if half else img.float()  # uint8 to fp16/32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        if img.ndimension() == 3:
            img = img.unsqueeze(0)

        # Inference
        t1 = time_synchronized()
        pred = model(img, augment=opt.augment)[0]

        # Apply NMS
        pred = non_max_suppression(
            pred,
            opt.conf_thres,
            opt.iou_thres,
            classes=opt.classes,
            agnostic=opt.agnostic_nms,
        )
        t2 = time_synchronized()

        # Apply Classifier
        if classify:
            pred = apply_classifier(pred, modelc, img, im0s)

        # Process detections
        for i, det in enumerate(pred):  # detections per image
            if webcam:  # batch_size >= 1
                p, s, im0, frame = path[i], "%g: " % i, im0s[i].copy(), dataset.count
            else:
                p, s, im0, frame = path, "", im0s, 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
            if len(det):
                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()

                # 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
                # Process the detected objects and draw bounding boxes and labels on the image
                for *xyxy, conf, cls in reversed(det):  # Loop through detections
                    # Draw bounding box
                    label = f'{names[int(cls)]} {conf:.2f}'
                    plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)

                # Optional: Draw custom text like the number of people detected
                if torch.is_tensor(n):
                    prediction = n.item()  # Convert tensor to Python number if needed
                else:
                    prediction = n
                cv2.putText(im0, 'Number of people=' + str(prediction), (30, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

            # Print time (inference + NMS)
            print(f'{s}Done. ({t2 - t1:.3f}s)')
            
            if torch.is_tensor(n):
            	prediction = n.item()
            else:
            	prediction = n
            cv2.putText(im0, 'Number of people=' + str(prediction), (30, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)	

            number_list.append(prediction)
            
            # Stream results
            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 != save_path:  # new video
                        vid_path = save_path
                        if isinstance(vid_writer, cv2.VideoWriter):
                            vid_writer.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 = cv2.VideoWriter(
                            save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)
                        )
                    vid_writer.write(im0)
        
        # Save the average number of people detected to a text file
        with open(save_dir / "average_number.txt", "w") as f:
            f.write(f"{sum(number_list) / len(number_list)}")
            f.write("\n")
            f.write(f"{number_list}")

    if save_txt or save_img:
        s = (
            f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to"
            f" {save_dir / 'labels'}"
            if save_txt
            else ""
        )
        print(f"Results saved to {save_dir}{s}")

    print(f"Done. ({time.time() - t0:.3f}s)")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--weights", nargs="+", type=str, default="yolov5s.pt", help="model.pt path(s)"
    )
    parser.add_argument(
        "--source", type=str, default="data/images", help="source"
    )  # file/folder, 0 for webcam
    parser.add_argument(
        "--img-size", type=int, default=640, help="inference size (pixels)"
    )
    parser.add_argument(
        "--conf-thres", type=float, default=0.25, help="object confidence threshold"
    )
    parser.add_argument(
        "--iou-thres", type=float, default=0.45, help="IOU threshold for NMS"
    )
    parser.add_argument(
        "--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu"
    )
    parser.add_argument("--view-img", action="store_true", help="display results")
    parser.add_argument("--save-txt", action="store_true", help="save results to *.txt")
    parser.add_argument(
        "--save-conf", action="store_true", help="save confidences in --save-txt labels"
    )
    parser.add_argument(
        "--nosave", action="store_true", help="do not save images/videos"
    )
    parser.add_argument(
        "--classes",
        nargs="+",
        type=int,
        help="filter by class: --class 0, or --class 0 2 3",
    )
    parser.add_argument(
        "--agnostic-nms", action="store_true", help="class-agnostic NMS"
    )
    parser.add_argument("--augment", action="store_true", help="augmented inference")
    parser.add_argument("--update", action="store_true", help="update all models")
    parser.add_argument(
        "--project", default="runs/detect", help="save results to project/name"
    )
    parser.add_argument("--name", default="exp", help="save results to project/name")
    parser.add_argument(
        "--exist-ok",
        action="store_true",
        help="existing project/name ok, do not increment",
    )
    opt = parser.parse_args()
    print(opt)
    check_requirements(exclude=("pycocotools", "thop"))

    with torch.no_grad():
        if opt.update:  # update all models (to fix SourceChangeWarning)
            for opt.weights in ["yolov5s.pt", "yolov5m.pt", "yolov5l.pt", "yolov5x.pt"]:
                detect()
                strip_optimizer(opt.weights)
        else:
            detect()