app.py

import gradio as gr
import os

#os.system("wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt")
os.system("wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e.pt")
os.system("wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt")
os.system("wget https://github.com/CAIC-AD/YOLOPv2/releases/download/V0.0.1/yolopv2.pt")

import argparse
import time
from pathlib import Path

import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random

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

from utils.functions import \
    time_synchronized,select_device, increment_path,\
    scale_coords,xyxy2xywh,non_max_suppression,split_for_trace_model,\
    driving_area_mask,lane_line_mask,plot_one_box,show_seg_result,\
    AverageMeter,\
    LoadImages


from PIL import Image
 



def detect(img,model):
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', nargs='+', type=str, default=model+".pt", help='model.pt path(s)')
    parser.add_argument('--source', type=str, default='Inference/', 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')
    parser.add_argument('--trace', action='store_true', help='trace model')
    opt = parser.parse_args()
    img.save("Inference/test.jpg")
    source, weights, view_img, save_txt, imgsz, trace = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size, opt.trace
    save_img = True  # save inference images
    #webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
        #('rtsp://', 'rtmp://', 'http://', 'https://'))
    #print(webcam)
    # 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)
    print(device)
    half = device.type != 'cpu'  # half precision only supported on CUDA

    # Load model
    inf_time = AverageMeter()
    waste_time = AverageMeter()
    nms_time = AverageMeter()

    # 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
    print(weights)
    stride =32
    model  = torch.jit.load(weights,map_location=device)
    model.eval()

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

    # 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()
    for path, img, im0s, vid_cap in dataset:
        img = torch.from_numpy(img).to(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,anchor_grid],seg,ll= model(img)
        t2 = time_synchronized()

        # waste time: the incompatibility of  torch.jit.trace causes extra time consumption in demo version 
        # but this problem will not appear in offical version 
        tw1 = time_synchronized()
        pred = split_for_trace_model(pred,anchor_grid)
        tw2 = time_synchronized()

        # Apply NMS
        t3 = time_synchronized()
        pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
        t4 = time_synchronized()

        da_seg_mask = driving_area_mask(seg)
        ll_seg_mask = lane_line_mask(ll)

        # Process detections
        for i, det in enumerate(pred):  # detections per image
          
            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
                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 opt.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 :  # Add bbox to image
                        plot_one_box(xyxy, im0, line_thickness=3)

            # Print time (inference)
            print(f'{s}Done. ({t2 - t1:.3f}s)')
            show_seg_result(im0, (da_seg_mask,ll_seg_mask), is_demo=True)

            # Save results (image with detections)
            if save_img:
                if dataset.mode == 'image':
                    cv2.imwrite(save_path, im0)
                    print(f" The image with the result is saved in: {save_path}")
                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))
                            w,h = im0.shape[1], im0.shape[0]
                        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)

    inf_time.update(t2-t1,img.size(0))
    nms_time.update(t4-t3,img.size(0))
    waste_time.update(tw2-tw1,img.size(0))
    print('inf : (%.4fs/frame)   nms : (%.4fs/frame)' % (inf_time.avg,nms_time.avg))
    print(f'Done. ({time.time() - t0:.3f}s)')

    return Image.fromarray(im0[:,:,::-1])

   
gr.Interface(detect,[gr.Image(type="pil"),gr.Dropdown(choices=["yolopv2"])], gr.Image(type="pil"),title="Yolopv2",examples=[["horses.jpeg", "yolopv2"]],description="demo for <a href='https://github.com/CAIC-AD/YOLOPv2' style='text-decoration: underline' target='_blank'>WongKinYiu/yolov7</a> Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors").launch()