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There are many ways you could offer source, and different +solutions will be better for different programs; see section 13 for the +specific requirements. + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU AGPL, see +. \ No newline at end of file diff --git a/object_detection/yolov11/README.md b/object_detection/yolov11/README.md new file mode 100644 index 000000000..868a2ff34 --- /dev/null +++ b/object_detection/yolov11/README.md @@ -0,0 +1,72 @@ +# YOLOv11 + +## Input + +![Input](input.jpg) + +(Image from https://pixabay.com/ja/photos/%E3%83%AD%E3%83%B3%E3%83%89%E3%83%B3%E5%B8%82-%E9%8A%80%E8%A1%8C-%E3%83%AD%E3%83%B3%E3%83%89%E3%83%B3-4481399/) + +Ailia input shape : (1, 3, h, w) +Range : [0.0, 1.0] +Color : RGB + +## Output + +![Output](output.png) + +## Usage + +Automatically downloads the onnx and prototxt files on the first run. +It is necessary to be connected to the Internet while downloading. + +For the sample image, + +```bash +$ python3 yolov11.py +``` + +If you want to specify the input image, put the image path after the `--input` option. +You can use `--savepath` option to change the name of the output file to save. + +```bash +$ python3 yolov11.py --input IMAGE_PATH --savepath SAVE_IMAGE_PATH +``` + +By adding the `--video` option, you can input the video. +If you pass `0` as an argument to VIDEO_PATH, you can use the webcam input instead of the video file. + +```bash +$ python3 yolov11.py --video VIDEO_PATH +``` + +By adding the `--model_type` option, you can specify model type which is selected from "v11n", "v11s" , "v11m", "v11l", "v11x"(default is "v11x"). + +```bash +$ python3 yolov11.py --model_type v11x +``` + +You can use `--detection_size` option to change the detection resolution. + +```bash +$ python3 yolov11.py --detection_size 1280 +``` + +## Reference + +- [YOLOv11](https://github.com/ultralytics/ultralytics) + +## Framework + +Pytorch + +## Model Format + +ONNX opset=17 + +## Netron + +- [yolov11n.onnx.prototxt](https://netron.app/?url=https://storage.googleapis.com/ailia-models/yolov11/yolov11n.onnx.prototxt) +- [yolov11s.onnx.prototxt](https://netron.app/?url=https://storage.googleapis.com/ailia-models/yolov11/yolov11s.onnx.prototxt) +- [yolov11m.onnx.prototxt](https://netron.app/?url=https://storage.googleapis.com/ailia-models/yolov11/yolov11m.onnx.prototxt) +- [yolov11l.onnx.prototxt](https://netron.app/?url=https://storage.googleapis.com/ailia-models/yolov11/yolov11l.onnx.prototxt) +- [yolov11x.onnx.prototxt](https://netron.app/?url=https://storage.googleapis.com/ailia-models/yolov11/yolov11x.onnx.prototxt) diff --git a/object_detection/yolov11/input.jpg b/object_detection/yolov11/input.jpg new file mode 100644 index 000000000..b97f5d27d Binary files /dev/null and b/object_detection/yolov11/input.jpg differ diff --git a/object_detection/yolov11/output.png b/object_detection/yolov11/output.png new file mode 100644 index 000000000..114ef79b3 Binary files /dev/null and b/object_detection/yolov11/output.png differ diff --git a/object_detection/yolov11/yolov11.py b/object_detection/yolov11/yolov11.py new file mode 100644 index 000000000..8cd787159 --- /dev/null +++ b/object_detection/yolov11/yolov11.py @@ -0,0 +1,376 @@ +import sys +import time + +import cv2 +import numpy as np + +import ailia + +# import original modules +sys.path.append('../../util') +from arg_utils import get_base_parser, update_parser, get_savepath # noqa +from model_utils import check_and_download_models # noqa +from image_utils import normalize_image # noqa +from detector_utils import plot_results, write_predictions, load_image # noqa +from nms_utils import batched_nms +from webcamera_utils import get_capture, get_writer # noqa + +# logger +from logging import getLogger # noqa + +logger = getLogger(__name__) + + +# ====================== +# Parameters +# ====================== + +WEIGHT_YOLOV11N_PATH = 'yolov11n.onnx' +MODEL_YOLOV11N_PATH = 'yolov11n.onnx.prototxt' +WEIGHT_YOLOV11S_PATH = 'yolov11s.onnx' +MODEL_YOLOV11S_PATH = 'yolov11s.onnx.prototxt' +WEIGHT_YOLOV11M_PATH = 'yolov11m.onnx' +MODEL_YOLOV11M_PATH = 'yolov11m.onnx.prototxt' +WEIGHT_YOLOV11L_PATH = 'yolov11l.onnx' +MODEL_YOLOV11L_PATH = 'yolov11l.onnx.prototxt' +WEIGHT_YOLOV11X_PATH = 'yolov11x.onnx' +MODEL_YOLOV11X_PATH = 'yolov11x.onnx.prototxt' +REMOTE_PATH = 'https://storage.googleapis.com/ailia-models/yolov11/' + +IMAGE_PATH = 'input.jpg' +SAVE_IMAGE_PATH = 'output.png' + +COCO_CATEGORY = [ + "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", + "truck", "boat", "traffic light", "fire hydrant", "stop sign", + "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", + "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", + "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", + "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", + "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", + "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", + "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", + "couch", "potted plant", "bed", "dining table", "toilet", "tv", + "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", + "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", + "scissors", "teddy bear", "hair drier", "toothbrush" +] + +THRESHOLD = 0.25 +IOU = 0.7 +DETECTION_SIZE = 640 + + +# ====================== +# Arguemnt Parser Config +# ====================== + +parser = get_base_parser('YOLOv11', IMAGE_PATH, SAVE_IMAGE_PATH) +parser.add_argument( + '-th', '--threshold', + default=THRESHOLD, type=float, + help='The detection threshold for yolo.' +) +parser.add_argument( + '-iou', '--iou', + default=IOU, type=float, + help='The detection iou for yolo.' +) +parser.add_argument( + '-w', '--write_prediction', + nargs='?', + const='txt', + choices=['txt', 'json'], + type=str, + help='Output results to txt or json file.' +) +parser.add_argument( + '-ds', '--detection_size', + default=DETECTION_SIZE, type=int, + help='The detection width and height for yolo.' +) +parser.add_argument( + '-m', '--model_type', default='v11x', + choices=('v11n', 'v11s', 'v11m', 'v11l', 'v11x'), + help='model type' +) +parser.add_argument( + '--onnx', + action='store_true', + help='execute onnxruntime version.' +) +args = update_parser(parser) + + +# ====================== +# Secondaty Functions +# ====================== + +def xywh2xyxy(x): + y = np.copy(x) + y[..., 0] = x[..., 0] - x[..., 2] / 2 # top left x + y[..., 1] = x[..., 1] - x[..., 3] / 2 # top left y + y[..., 2] = x[..., 0] + x[..., 2] / 2 # bottom right x + y[..., 3] = x[..., 1] + x[..., 3] / 2 # bottom right y + + return y + + +def scale_boxes(img1_shape, boxes, img0_shape): + """ + Rescales bounding boxes (in the format of xyxy) from the shape of the image they were originally specified in + (img1_shape) to the shape of a different image (img0_shape). + + Args: + img1_shape (tuple): The shape of the image that the bounding boxes are for, in the format of (height, width). + boxes (numpy.ndarray): the bounding boxes of the objects in the image, in the format of (x1, y1, x2, y2) + img0_shape (tuple): the shape of the target image, in the format of (height, width). + Returns: + boxes (numpy.ndarray): The scaled bounding boxes, in the format of (x1, y1, x2, y2) + """ + gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding + + boxes[..., [0, 2]] -= pad[0] # x padding + boxes[..., [1, 3]] -= pad[1] # y padding + boxes[..., :4] /= gain + + boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, img0_shape[1]) # x1, x2 + boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, img0_shape[0]) # y1, y2 + + return boxes + + +# ====================== +# Main functions +# ====================== + +def preprocess(img): + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + im_h, im_w, _ = img.shape + size = args.detection_size + + r = min(size / im_h, size / im_w) + oh, ow = int(round(im_h * r)), int(round(im_w * r)) + if ow != im_w or oh != im_h: + img = cv2.resize(img, (ow, oh), interpolation=cv2.INTER_LINEAR) + + dh, dw = size - oh, size - ow + if True: + stride = 32 + dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding + # divide padding into 2 sides + dw /= 2 + dh /= 2 + + top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) + left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) + img = cv2.copyMakeBorder( + img, top, bottom, left, right, cv2.BORDER_CONSTANT, + value=(114, 114, 114)) # add border + + # Scale input pixel value to 0 to 1 + img = normalize_image(img, normalize_type='255') + img = img.transpose((2, 0, 1)) # HWC -> CHW + img = np.expand_dims(img, axis=0) + img = img.astype(np.float32) + + return img + +def post_processing(preds, img, orig_shape): + conf_thres = args.threshold + iou_thres = args.iou + + xc = np.max(preds[:, 4:], axis=1) > conf_thres + + none_out = np.zeros((0, 6)) + + x = preds[0].T[xc[0]] # confidence + if not x.shape[0]: + return none_out + + box, cls = np.split(x, [4], axis=1) + box = xywh2xyxy(box) # center_x, center_y, width, height) to (x1, y1, x2, y2) + + j = np.argmax(cls, axis=1) + conf = cls[np.arange(len(cls)), j] + + x = np.concatenate((box, conf.reshape(-1, 1), j.reshape(-1, 1)), axis=1) + + # Check shape + n = x.shape[0] # number of boxes + if not n: # no boxes + return none_out + + # sort by confidence and remove excess boxes + max_nms = 30000 + x = x[np.argsort(-x[:, 4])[:max_nms]] + + c = x[:, 5] + boxes, scores = x[:, :4], x[:, 4] # boxes, scores + + # Batched NMS + i = batched_nms(boxes, scores, c, iou_thres) + + max_det = 300 + i = i[:max_det] # limit detections + preds = x[i] + + preds[:, :4] = np.round(scale_boxes(img.shape[2:], preds[:, :4], orig_shape)) + + return preds + +def predict(net, img): + orig_shape = img.shape + + img = preprocess(img) + + # feedforward + if not args.onnx: + output = net.predict([img]) + else: + # output = net.run(None, {'images': img}) + output = net.run([x.name for x in net.get_outputs()], {net.get_inputs()[0].name: img}) + + preds = output[0] + + preds = post_processing(preds, img, orig_shape) + + return preds + +def convert_to_detector_object(preds, im_w, im_h): + detector_object = [] + for i in range(len(preds)): + (x1, y1, x2, y2) = preds[i, :4] + score = float(preds[i, 4]) + cls = int(preds[i, 5]) + + r = ailia.DetectorObject( + category=COCO_CATEGORY[cls], + prob=score, + x=x1 / im_w, + y=y1 / im_h, + w=(x2 - x1) / im_w, + h=(y2 - y1) / im_h, + ) + detector_object.append(r) + + return detector_object + +def recognize_from_image(net): + # input image loop + for image_path in args.input: + logger.info(image_path) + + # prepare input data + img = load_image(image_path) + img = cv2.cvtColor(img, cv2.COLOR_BGRA2BGR) + + # inference + logger.info('Start inference...') + if args.benchmark: + logger.info('BENCHMARK mode') + total_time = 0 + for i in range(args.benchmark_count): + start = int(round(time.time() * 1000)) + preds = predict(net, img) + end = int(round(time.time() * 1000)) + if i != 0: + total_time = total_time + (end - start) + logger.info(f'\tailia processing time {end - start} ms') + logger.info(f'\taverage time {total_time / (args.benchmark_count - 1)} ms') + else: + preds = predict(net, img) + + # plot result + detect_object = convert_to_detector_object(preds, img.shape[1], img.shape[0]) + res_img = plot_results(detect_object, img) + + savepath = get_savepath(args.savepath, image_path, ext='.png') + logger.info(f'saved at : {savepath}') + cv2.imwrite(savepath, res_img) + + # write prediction + if args.write_prediction is not None: + ext = args.write_prediction + pred_file = "%s.%s" % (savepath.rsplit('.', 1)[0], ext) + write_predictions(pred_file, detect_object, img, category=COCO_CATEGORY, file_type=ext) + + logger.info('Script finished successfully.') + +def recognize_from_video(net): + video_file = args.video if args.video else args.input[0] + capture = get_capture(video_file) + assert capture.isOpened(), 'Cannot capture source' + + # create video writer if savepath is specified as video format + if args.savepath != SAVE_IMAGE_PATH: + f_h = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)) + f_w = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)) + writer = get_writer(args.savepath, f_h, f_w) + else: + writer = None + + frame_shown = False + while True: + ret, frame = capture.read() + if (cv2.waitKey(1) & 0xFF == ord('q')) or not ret: + break + if frame_shown and cv2.getWindowProperty('frame', cv2.WND_PROP_VISIBLE) == 0: + break + + # inference + preds = predict(net, frame) + + # plot result + detect_object = convert_to_detector_object(preds, frame.shape[1], frame.shape[0]) + res_img = plot_results(detect_object, frame) + + # show + cv2.imshow('frame', res_img) + frame_shown = True + + # save results + if writer is not None: + res_img = res_img.astype(np.uint8) + writer.write(res_img) + + capture.release() + cv2.destroyAllWindows() + if writer is not None: + writer.release() + + logger.info('Script finished successfully.') + +def main(): + dic_model = { + 'v11n': (WEIGHT_YOLOV11N_PATH, MODEL_YOLOV11N_PATH), + 'v11s': (WEIGHT_YOLOV11S_PATH, MODEL_YOLOV11S_PATH), + 'v11m': (WEIGHT_YOLOV11M_PATH, MODEL_YOLOV11M_PATH), + 'v11l': (WEIGHT_YOLOV11L_PATH, MODEL_YOLOV11L_PATH), + 'v11x': (WEIGHT_YOLOV11X_PATH, MODEL_YOLOV11X_PATH) + } + WEIGHT_PATH, MODEL_PATH = dic_model[args.model_type] + + # model files check and download + check_and_download_models(WEIGHT_PATH, MODEL_PATH, REMOTE_PATH) + + # initialize + if not args.onnx: + net = ailia.Net(MODEL_PATH, WEIGHT_PATH, env_id=args.env_id) + else: + import onnxruntime + cuda = 0 < ailia.get_gpu_environment_id() + providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider'] + net = onnxruntime.InferenceSession(WEIGHT_PATH, providers=providers) + + if args.video is not None: + # video mode + recognize_from_video(net) + else: + # image mode + recognize_from_image(net) + + +if __name__ == '__main__': + main()