engine.py

import argparse
import time
from pathlib import Path
import os
import pandas as pd

# import sys
# sys.path.append("../")  # Add root path to the system path
import path_config as config

import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import diff, 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 organize.final import finalize
from organize.preprocess import rename, preprocess
from organize.framediff import difference
from utils.count import do_count


from matplotlib import pyplot as plt
from PIL import Image
import shutil


def detect(opt):
    source, weights, view_img, save_txt, imgsz, trace = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size, not opt.no_trace
    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 trace:
        model = TracedModel(model, device, opt.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
    obj_list = []
    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 = 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()

                for c in det[:, -1].unique():
                    n = (det[:, -1] == c).sum()  # detections per class
                    # add to string
                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "
                    obj_dict = {'Name': p.name, 'Swelling': int(n)}
                    obj_list.append(obj_dict.copy())

                # 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 or view_img:  # Add bbox to image
                        # label = f'{names[int(cls)]} {conf:.2f}'
                        label = f'{conf:.2f}'
                        plot_one_box(xyxy,
                                     im0,
                                     label=label,
                                     color=(144, 238, 144),
                                     line_thickness=1)
                        im0 = cv2.putText(im0,
                                          f"Est. amount of swelling organoids: {n}",
                                          (1, 20),
                                          cv2.FONT_HERSHEY_SIMPLEX,
                                          0.7,
                                          color=(255, 255, 255),
                                          thickness=2)

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

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

            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:
                    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)

    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}")

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


# make dirs with mode
def mkdir_with_mode(directory, mode):
    if not os.path.isdir(directory):
        oldmask = os.umask(000)
        os.makedirs(directory, 0o777)
        os.umask(oldmask)


def do_detect(weights_i, sources_i, img_size_i, conf_thres_i, output_dir_i):
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights',
                        nargs='+',
                        type=str,
                        default=weights_i,
                        help='model.pt path(s)')
    parser.add_argument('--source', type=str, default=sources_i,
                        help='source')  # file/folder, 0 for webcam
    parser.add_argument('--img-size',
                        type=int,
                        default=img_size_i,
                        help='inference size (pixels)')
    parser.add_argument('--conf-thres',
                        type=float,
                        default=conf_thres_i,
                        help='object confidence threshold')
    parser.add_argument('--iou-thres',
                        type=float,
                        default=0.45,
                        help='IOU threshold for NMS')
    parser.add_argument('--device',
                        default='cpu',
                        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=output_dir_i,
                        help='save results to project/name')
    parser.add_argument('--name',
                        default='img3',
                        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('--no-trace',
                        action='store_true',
                        help='don`t trace model')
    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 ['pt']:
                yolo_list = detect(opt)
                strip_optimizer(opt.weights)
        else:
            yolo_list = detect(opt)
    # return yolo_list
    writer = pd.ExcelWriter(f"{output_dir_i}/excel/swelling amount.xlsx",
                            engine='xlsxwriter')

    df = pd.DataFrame.from_dict(yolo_list)
    df.to_excel(writer, index=False, header=True)
    # writer.save()
    writer.close()
    print("Excel saved in /excel.")
    print(yolo_list)


if __name__ == '__main__':
    # extension = ".tif"
    
    img_size = 512
    conf_thred = 0.3
    # modify the paths here:
    # # --------------------------------------------------------------------------------------
    # # folder with all the images
    # folder_images = "data/Input"
    # # path for the model to count organoids
    # model_baylos = "data/trained_models/bayesian/best_model.pth"
    # # where you want to store the results
    # output_folder = "data/Output"
    # model_yolov7 = 'data/trained_models/yolov7/last.pt'
    # output_folder = 'data/Output'
    
    # # # Use config variables
    
    # # folder_images = config.folder_images
    # # model_baylos = config.model_baylos
    # # output_folder = config.output_folder
    # # model_yolov7 = config.model_yolov7
    # # output_folder = config.output_folder
    # # --------------------------------------------------------------------------------------
    # num_exps = rename(folder_images)
    # start_folder, end_folder = preprocess(folder_images, output_folder, num_exps)
    # difference(start_folder, end_folder, output_folder)
    # docount(start_folder, model_baylos, output_folder)
    # diff_images = f'{output_folder}/diff_images'
    # do_detect(model_yolov7, diff_images, img_size, conf_thred, output_folder)
    # finalize(output_folder)