yolov7_plate/test_widerface.py

import argparse
import time
from pathlib import Path

import os
import copy
import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random
from tqdm import tqdm
from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages, letterbox
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, save_one_box
from utils.plots import colors, plot_one_box
from utils.torch_utils import select_device, load_classifier, time_synchronized
import numpy as np

def detect(opt):
    weights, imgsz, kpt_label = opt.weights, opt.img_size, opt.kpt_label

    # 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
    if isinstance(imgsz, (list,tuple)):
        assert len(imgsz) ==2; "height and width of image has to be specified"
        imgsz[0] = check_img_size(imgsz[0], s=stride)
        imgsz[1] = check_img_size(imgsz[1], s=stride)
    else:
        imgsz = check_img_size(imgsz, s=stride)  # check img_size
    names = model.module.names if hasattr(model, 'module') else model.names  # get class names
    if half:
        model.half()  # to FP16

    # 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()

    # testing dataset
    testset_folder = opt.dataset_folder
    testset_list = opt.dataset_folder[:-7] + "wider_val.txt"

    with open(testset_list, 'r') as fr:
        test_dataset = fr.read().split()
        num_images = len(test_dataset)
    for img_name in tqdm(test_dataset):
        image_path = testset_folder + img_name
        img0 = cv2.imread(image_path)  # BGR
        img = letterbox(img0, imgsz)[0]
        # Convert
        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
        img = np.ascontiguousarray(img)
        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 = 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, kpt_label=kpt_label)
        t2 = time_synchronized()

        save_name = opt.save_folder + img_name[:-4] + ".txt"
        dirname = os.path.dirname(save_name)
        if not os.path.isdir(dirname):
            os.makedirs(dirname)
        with open(save_name, "w") as fd:
            file_name = os.path.basename(save_name)[:-4] + "\n"
            bboxs_num = str(len(pred[0])) + "\n"
            fd.write(file_name)
            fd.write(bboxs_num)
            # Process detections
            for i, det in enumerate(pred):  # detections per image
                gn = torch.tensor(img0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
                if len(det):
                    # Rescale boxes from img_size to im0 size
                    scale_coords(img.shape[2:], det[:, :4], img0.shape, kpt_label=False)
                    scale_coords(img.shape[2:], det[:, 6:], img0.shape, kpt_label=kpt_label, step=3)

                    # Print results
                    for c in det[:, 5].unique():
                        n = (det[:, 5] == c).sum()  # detections per class

                    # Write results
                    for det_index, (*xyxy, conf, cls) in enumerate(det[:,:6]):
                        c = int(cls)  # integer class
                        label = None if opt.hide_labels else (names[c] if opt.hide_conf else f'{names[c]} {conf:.2f}')
                        kpts = det[det_index, 6:]
                        x1 = int(xyxy[0] + 0.5)
                        y1 = int(xyxy[1] + 0.5)
                        x2 = int(xyxy[2] + 0.5)
                        y2 = int(xyxy[3] + 0.5)
                        fd.write('%d %d %d %d %.03f' % (x1, y1, x2-x1, y2-y1, conf if conf <= 1 else 1) + '\n')
                        #plot_one_box(xyxy, img0, label=label, color=colors(c, True), line_thickness=opt.line_thickness, kpt_label=kpt_label, kpts=kpts, steps=3, orig_shape=img0.shape[:2])
            #cv2.imwrite('result.jpg', img0)
    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('--img-size', nargs= '+', type=int, default=640, help='inference size (pixels)')
    parser.add_argument('--conf-thres', type=float, default=0.01, help='object confidence threshold')
    parser.add_argument('--iou-thres', type=float, default=0.5, 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('--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('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
    parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
    parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
    parser.add_argument('--kpt-label', type=int, default=5, help='number of keypoints')
    parser.add_argument('--save_folder', default='./widerface_evaluate/widerface_txt/', type=str, help='Dir to save txt results')
    parser.add_argument('--dataset_folder', default='data/widerface/widerface/val/images/', type=str, help='dataset path')
    opt = parser.parse_args()
    print(opt)
    check_requirements(exclude=('tensorboard', 'pycocotools', 'thop'))

    with torch.no_grad():
       detect(opt=opt)