Update detect.py
class text 추출 text 추출한걸 음성으로 출력 Signed-off-by: Leedong414 <165615367+Leedong414@users.noreply.github.com>pull/13048/head
Leedong414
GitHub
parent
5329de2af7
commit
ba176b29a3
175
detect.py
175
detect.py
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@ -42,34 +42,27 @@ from gtts import gTTS
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def detect(opt):
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source, weights, conf, save_txt_path = opt.source, opt.weights, opt.conf, opt.save_txt_path
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# 모델 로드
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model = torch.hub.load("ultralytics/yolov5", "custom", path=weights)
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# 이미지 로드 및 추론 수행
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results = model(source)
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# 결과를 pandas 데이터프레임으로 변환
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results_df = results.pandas().xyxy[0]
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# 데이터프레임의 클래스 컬럼 추출
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classes = results_df["name"].tolist()
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# 클래스 정보를 텍스트 파일로 저장
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with open(save_txt_path, "w") as f:
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for cls in classes:
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f.write(f"{cls}\n")
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# 콘솔에 출력
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print("Detected Classes:")
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for cls in classes:
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print(cls)
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# TTS를 사용하여 클래스 이름들을 음성으로 변환
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if classes:
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text_to_speak = "Detected classes are: " + ", ".join(classes)
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tts = gTTS(text=text_to_speak, lang="en")
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tts.save("detected_classes.mp3")
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os.system("mpg321 detected_classes.mp3") # mpg321 설치 필요 (리눅스의 경우)
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os.system("mpg321 detected_classes.mp3")
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if __name__ == "__main__":
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@ -79,7 +72,7 @@ if __name__ == "__main__":
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)
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parser.add_argument(
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"--source", type=str, default="/HongRyeon_test01.jpg", help="source"
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) # file/folder, 0 for webcam
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)
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parser.add_argument("--conf", type=float, default=0.5, help="object confidence threshold")
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parser.add_argument(
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"--save_txt_path", type=str, default="detected_classes.txt", help="path to save detected classes txt file"
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@ -91,10 +84,10 @@ if __name__ == "__main__":
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FILE = Path(__file__).resolve()
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ROOT = FILE.parents[0] # YOLOv5 root directory
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ROOT = FILE.parents[0]
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if str(ROOT) not in sys.path:
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sys.path.append(str(ROOT)) # add ROOT to PATH
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ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
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sys.path.append(str(ROOT))
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ROOT = Path(os.path.relpath(ROOT, Path.cwd()))
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from ultralytics.utils.plotting import Annotator, colors, save_one_box
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@ -121,56 +114,56 @@ from utils.torch_utils import select_device, smart_inference_mode
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@smart_inference_mode()
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def run(
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weights=ROOT / "yolov5s.pt", # model path or triton URL
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source=ROOT / "data/images", # file/dir/URL/glob/screen/0(webcam)
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data=ROOT / "data/coco128.yaml", # dataset.yaml path
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imgsz=(640, 640), # inference size (height, width)
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conf_thres=0.25, # confidence threshold
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iou_thres=0.45, # NMS IOU threshold
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max_det=1000, # maximum detections per image
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device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu
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view_img=False, # show results
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save_txt=False, # save results to *.txt
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save_csv=False, # save results in CSV format
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save_conf=False, # save confidences in --save-txt labels
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save_crop=False, # save cropped prediction boxes
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nosave=False, # do not save images/videos
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classes=None, # filter by class: --class 0, or --class 0 2 3
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agnostic_nms=False, # class-agnostic NMS
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augment=False, # augmented inference
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visualize=False, # visualize features
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update=False, # update all models
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project=ROOT / "runs/detect", # save results to project/name
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name="exp", # save results to project/name
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exist_ok=False, # existing project/name ok, do not increment
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line_thickness=3, # bounding box thickness (pixels)
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hide_labels=False, # hide labels
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hide_conf=False, # hide confidences
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half=False, # use FP16 half-precision inference
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dnn=False, # use OpenCV DNN for ONNX inference
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vid_stride=1, # video frame-rate stride
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weights=ROOT / "yolov5s.pt",
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source=ROOT / "data/images",
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data=ROOT / "data/coco128.yaml",
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imgsz=(640, 640),
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conf_thres=0.25,
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iou_thres=0.45,
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max_det=1000,
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device="",
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view_img=False,
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save_txt=False,
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save_csv=False,
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save_conf=False,
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save_crop=False,
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nosave=False,
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classes=None,
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agnostic_nms=False,
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augment=False,
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visualize=False,
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update=False,
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project=ROOT / "runs/detect",
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name="exp",
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exist_ok=False,
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line_thickness=3,
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hide_labels=False,
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hide_conf=False,
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half=False,
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dnn=False,
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vid_stride=1,
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):
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source = str(source)
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save_img = not nosave and not source.endswith(".txt") # save inference images
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save_img = not nosave and not source.endswith(".txt")
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is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
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is_url = source.lower().startswith(("rtsp://", "rtmp://", "http://", "https://"))
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webcam = source.isnumeric() or source.endswith(".streams") or (is_url and not is_file)
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screenshot = source.lower().startswith("screen")
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if is_url and is_file:
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source = check_file(source) # download
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source = check_file(source)
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save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)
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(save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True)
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# Directories
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save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
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(save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
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# Load model
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device = select_device(device)
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model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
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stride, names, pt = model.stride, model.names, model.pt
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imgsz = check_img_size(imgsz, s=stride) # check image size
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imgsz = check_img_size(imgsz, s=stride)
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# Dataloader
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bs = 1 # batch_size
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bs = 1
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if webcam:
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view_img = check_imshow(warn=True)
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dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
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@ -181,20 +174,20 @@ def run(
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dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
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vid_path, vid_writer = [None] * bs, [None] * bs
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# Run inference
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model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup
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model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz))
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seen, windows, dt = 0, [], (Profile(device=device), Profile(device=device), Profile(device=device))
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for path, im, im0s, vid_cap, s in dataset:
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with dt[0]:
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im = torch.from_numpy(im).to(model.device)
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im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
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im /= 255 # 0 - 255 to 0.0 - 1.0
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im = im.half() if model.fp16 else im.float()
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im /= 255
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if len(im.shape) == 3:
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im = im[None] # expand for batch dim
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im = im[None]
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if model.xml and im.shape[0] > 1:
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ims = torch.chunk(im, im.shape[0], 0)
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# Inference
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with dt[1]:
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visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
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if model.xml and im.shape[0] > 1:
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@ -207,17 +200,13 @@ def run(
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pred = [pred, None]
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else:
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pred = model(im, augment=augment, visualize=visualize)
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# NMS
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with dt[2]:
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pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
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# Second-stage classifier (optional)
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# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
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# Define the path for the CSV file
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csv_path = save_dir / "predictions.csv"
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# Create or append to the CSV file
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def write_to_csv(image_name, prediction, confidence):
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"""Writes prediction data for an image to a CSV file, appending if the file exists."""
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data = {"Image Name": image_name, "Prediction": prediction, "Confidence": confidence}
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@ -227,34 +216,33 @@ def run(
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writer.writeheader()
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writer.writerow(data)
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# Process predictions
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for i, det in enumerate(pred): # per image
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for i, det in enumerate(pred):
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seen += 1
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if webcam: # batch_size >= 1
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if webcam:
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p, im0, frame = path[i], im0s[i].copy(), dataset.count
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s += f"{i}: "
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else:
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p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0)
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p = Path(p) # to Path
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save_path = str(save_dir / p.name) # im.jpg
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txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}") # im.txt
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s += "%gx%g " % im.shape[2:] # print string
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gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
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imc = im0.copy() if save_crop else im0 # for save_crop
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p = Path(p)
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save_path = str(save_dir / p.name)
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txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}")
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s += "%gx%g " % im.shape[2:]
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gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]
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imc = im0.copy() if save_crop else im0
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annotator = Annotator(im0, line_width=line_thickness, example=str(names))
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if len(det):
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# Rescale boxes from img_size to im0 size
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det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
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# Print results
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for c in det[:, 5].unique():
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n = (det[:, 5] == c).sum() # detections per class
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s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
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n = (det[:, 5] == c).sum()
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s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "
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# Write results
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for *xyxy, conf, cls in reversed(det):
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c = int(cls) # integer class
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c = int(cls)
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label = names[c] if hide_conf else f"{names[c]}"
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confidence = float(conf)
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confidence_str = f"{confidence:.2f}"
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@ -262,59 +250,56 @@ def run(
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if save_csv:
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write_to_csv(p.name, label, confidence_str)
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if save_txt: # Write to file
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xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
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line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
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if save_txt:
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xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()
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line = (cls, *xywh, conf) if save_conf else (cls, *xywh)
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with open(f"{txt_path}.txt", "a") as f:
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f.write(("%g " * len(line)).rstrip() % line + "\n")
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if save_img or save_crop or view_img: # Add bbox to image
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c = int(cls) # integer class
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if save_img or save_crop or view_img:
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c = int(cls)
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label = None if hide_labels else (names[c] if hide_conf else f"{names[c]} {conf:.2f}")
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annotator.box_label(xyxy, label, color=colors(c, True))
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if save_crop:
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save_one_box(xyxy, imc, file=save_dir / "crops" / names[c] / f"{p.stem}.jpg", BGR=True)
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# Stream results
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im0 = annotator.result()
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if view_img:
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if platform.system() == "Linux" and p not in windows:
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windows.append(p)
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cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
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cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)
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cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
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cv2.imshow(str(p), im0)
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cv2.waitKey(1) # 1 millisecond
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cv2.waitKey(1)
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# Save results (image with detections)
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if save_img:
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if dataset.mode == "image":
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cv2.imwrite(save_path, im0)
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else: # 'video' or 'stream'
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if vid_path[i] != save_path: # new video
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else:
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if vid_path[i] != save_path:
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vid_path[i] = save_path
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if isinstance(vid_writer[i], cv2.VideoWriter):
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vid_writer[i].release() # release previous video writer
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if vid_cap: # video
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vid_writer[i].release()
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if vid_cap:
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fps = vid_cap.get(cv2.CAP_PROP_FPS)
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w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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else: # stream
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else:
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fps, w, h = 30, im0.shape[1], im0.shape[0]
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save_path = str(Path(save_path).with_suffix(".mp4")) # force *.mp4 suffix on results videos
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save_path = str(Path(save_path).with_suffix(".mp4"))
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vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
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vid_writer[i].write(im0)
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# Print time (inference-only)
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LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
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# Print results
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t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image
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t = tuple(x.t / seen * 1e3 for x in dt)
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LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}" % t)
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if save_txt or save_img:
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s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ""
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LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
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if update:
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strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning)
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strip_optimizer(weights[0])
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def parse_opt():
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@ -349,7 +334,7 @@ def parse_opt():
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parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference")
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parser.add_argument("--vid-stride", type=int, default=1, help="video frame-rate stride")
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opt = parser.parse_args()
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opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
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opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1
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print_args(vars(opt))
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return opt
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