api_main.py

"""Run inference with a YOLOv5 model on images, videos, directories, streams

Usage:
    $ python path/to/detect.py --source path/to/img.jpg --weights yolov5s.pt --img 640
"""

import argparse
import sys
import time
from pathlib import Path
import os 
import cv2
import torch
import torch.backends.cudnn as cudnn

FILE = Path(__file__).absolute()
sys.path.append(FILE.parents[0].as_posix())  # add yolov5/ to path

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



@torch.no_grad()
def run(weights=r"D:\Forbmax User Data\waqar sahi\SmartMeter\yolo\ptv_runs\weights\best.pt",  # model.pt path(s)
        source='data/images',  # file/dir/URL/glob, 0 for webcam
        imgsz=640,  # inference size (pixels)
        conf_thres=0.4,  # confidence threshold
        iou_thres=0.4,  # 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=True,  # 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=True,  # hide confidences
        half=False,  # use FP16 half-precision inference
        ):
    save_crop=False
    save_img = True
    webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
        ('rtsp://', 'rtmp://', 'http://', 'https://'))

    # Directories
    save_dir = "Result"  # increment run
    # (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
    # save_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, pt, onnx = False, w.endswith('.pt'), w.endswith('.onnx')  # inference type
    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)
    imgsz = check_img_size(imgsz, s=stride)  # check image size

    # Dataloader
    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)
        bs = len(dataset)  # batch_size
    else:
        dataset = LoadImages(source, img_size=imgsz, stride=stride)
        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, imgsz).to(device).type_as(next(model.parameters())))  # run once
    t0 = time.time()
    # count_object=0
    for path, img, im0s, vid_cap in dataset:
        if pt:
            img = torch.from_numpy(img).to(device)
            img = img.half() if half else img.float()  # uint8 to fp16/32
        elif onnx:
            img = img.astype('float32')
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        if len(img.shape) == 3:
            img = img[None]  # expand for batch dim

        # Inference
        t1 = time_sync()
        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}))

        # NMS
        pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
        t2 = time_sync()

        # Second-stage classifier (optional)
        if classify:
            pred = apply_classifier(pred, modelc, img, im0s)
        
        # Process predictions
        for i, det in enumerate(pred):  # detections per image
            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
            # print(p.name)
            save_path = os.path.join(save_dir,p.name)  # img.jpg
            print(save_path)
            # 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
            
            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
                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}')
                        # count_object=count_object+1
                        # label=''
                        plot_one_box(xyxy, im0, label=label, color=colors(c, True), line_thickness=line_thickness)
                        if save_crop:
                            save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)

            # Print time (inference + NMS)
            print(f'{s}Done. ({t2 - t1:.3f}s)')

            # Stream results
            if view_img:
                im0=cv2.resize(im0,(700,350))
                cv2.imshow(str(p), im0)
                cv2.waitKey(0)  # 1 millisecond

            # Save results (image with detections)
            if save_img:
                if dataset.mode == 'image':
                    # cv2.putText(
                    #     im0, #numpy array on which text is written
                    #     str(count_object), #text
                    #     (100,200), #position at which writing has to start
                    #     cv2.FONT_HERSHEY_SIMPLEX, #font family
                    #     5, #font size
                    #     (209, 80, 0, 255), #font color
                    #     5)#font stroke
                    cv2.imwrite(save_path, im0)
                    return save_path
            #     else:  # 'video' or 'stream'
            #         pass
                    # 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))
                    #     els e:  # 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)

    # 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 {save_dir}{s}")

    # if update:
    #     strip_optimizer(weights)  # update model (to fix SourceChangeWarning)

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

def main(source):

    return run(source=source)


# if __name__ == "__main__":

#     main(source=r"D:\Forbmax User Data\waqar sahi\SmartMeter\yolo\TestDataset")