commit adbb2bbce3f099019c5a670a44be5ee19c2e3b30 Author: chakcy <947105045@qq.com> Date: Fri Oct 17 14:11:33 2025 +0800 add first commit diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..ac7db21 --- /dev/null +++ b/.gitignore @@ -0,0 +1,16 @@ +# Python-generated files +__pycache__/ +*.py[oc] +build/ +dist/ +wheels/ +*.egg-info + +# Virtual environments +.venv + +# result +results + +# resources +resources diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..cc1923a --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.8 diff --git a/README.md b/README.md new file mode 100644 index 0000000..160d767 --- /dev/null +++ b/README.md @@ -0,0 +1,51 @@ +# 算能盒子开发 + +[TOC] + +## 盒子环境搭建 + +### 资源包 +``` +. +│ sdcard.tar # 刷机包 +│ uv-aarch64-unknown-linux-gnu.tar.gz # uv python 项目管理工具 +``` + +### 网络 + +1. 将盒子LAN口与计算机连接，WAN口与交换机/路由器连接 + +2. 通过ssh协议与连接连接 192.168.150.1 地址操作盒子 + +3. 若无交换机/路由器条件可使用电脑网络共享 + +### python环境配置 + +在计算机上输入以下命令将 uv-aarch64-unknown-linux-gnu.tar.gz 上传到盒子的 /data 路径（linaro用户的密码为linaro） + +```sh +scp uv-aarch64-unknown-linux-gnu.tar.gz linaro@192.168.150.1:/data +``` + +解压 uv-aarch64-unkown-linux-gnu.tar.gz 并配置环境 + +```sh +tar -xzvf uv-aarch64-unknown-linux-gnu.tar.gz + +# 将 uv 和 uvx 移动到 /usr/local/bin/（需要 sudo 权限） +sudo mv uv-aarch64-unknown-linux-gnu/uv /usr/local/bin/ +sudo mv uv-aarch64-unknown-linux-gnu/uvx /usr/local/bin/ + +# 确保文件有可执行权限 +sudo chmod +x /usr/local/bin/uv +sudo chmod +x /usr/local/bin/uvx + +# 验证是否能够执行uv命令 +uv --version # uv 0.9.3 +``` + +### 运行示例 + +```sh + +``` diff --git a/main.py b/main.py new file mode 100644 index 0000000..28679cf --- /dev/null +++ b/main.py @@ -0,0 +1,11 @@ +from sophon_ai_project.yolov8_bmcv import main +from sophon_ai_project.yolov8_bmcv import argsparser + +# def main(): +# print("Hello from sophon-ai-project!") + + +if __name__ == "__main__": + args = argsparser() + main(args) + print('all done.') diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..ad081ea --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,24 @@ +[project] +name = "sophon-ai-project" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.8" +dependencies = [ + "numpy>=1.24.4", + "opencv-python-headless>=4.12.0.88", +] + + +[[tool.uv.index]] +default = true +url = "https://pypi.tuna.tsinghua.edu.cn/simple" + +[tool.hatch.build.targets.wheel] +packages = ["src/sophon_ai_project"] + +[dependency-groups] +dev = [ + "dfss>=1.10.4", + "pip>=25.0.1", +] diff --git a/scripts/download.sh b/scripts/download.sh new file mode 100755 index 0000000..1e53f5d --- /dev/null +++ b/scripts/download.sh @@ -0,0 +1,127 @@ +#!/bin/bash +scripts_dir=$(dirname $(readlink -f "$0")) + +download_bm1684x=0 +download_bm1688=0 +download_cv186x=0 +download_onnx=0 +download_ckpt=0 + +while [[ $# -gt 0 ]]; do + key="$1" + + case $key in + --BM1684X) + download_bm1684x=1 + shift 1 + ;; + --BM1688) + download_bm1688=1 + shift 1 + ;; + --CV186X) + download_cv186x=1 + shift 1 + ;; + --onnx) + download_onnx=1 + shift 1 + ;; + --ckpt) + download_ckpt=1 + shift 1 + ;; + --all) + download_bm1684x=1 + download_bm1688=1 + download_cv186x=1 + download_onnx=1 + download_ckpt=1 + shift 1 + ;; + *) + echo "Invalid option: $key" >&2 + exit 1 + ;; + :) + echo "Option -$OPTARG requires an argument." >&2 + exit 1 + ;; + esac +done + +pip3 install dfss -i https://pypi.tuna.tsinghua.edu.cn/simple --upgrade +pushd $scripts_dir +# datasets +if [ ! -d "../resources/datasets" ]; +then + mkdir -p ../resources/datasets + pushd ../resources/datasets + python3 -m dfss --url=open@sophgo.com:sophon-demo/common/test.tar.gz #test pictures + tar xvf test.tar.gz && rm test.tar.gz #in case `tar xvf xx` failed. + python3 -m dfss --url=open@sophgo.com:sophon-demo/common/coco.names #coco classnames + python3 -m dfss --url=open@sophgo.com:sophon-demo/common/coco128.tar.gz #coco 128 pictures + tar xvf coco128.tar.gz && rm coco128.tar.gz + python3 -m dfss --url=open@sophgo.com:sophon-demo/common/coco_val2017_1000.tar.gz #coco 1000 pictures and json. + tar xvf coco_val2017_1000.tar.gz && rm coco_val2017_1000.tar.gz + python3 -m dfss --url=open@sophgo.com:sophon-demo/common/test_car_person_1080P.mp4 #test video + popd + echo "datasets download!" +else + echo "Datasets folder exist! Remove it if you need to update." +fi + +# models +if [ ! -d "../resources/models" ]; +then + mkdir -p ../resources/models +fi + +pushd ../resources/models + +if [ ! -d "../resources/models/BM1684X" ]; +then + if [ $download_bm1684x == 1 ]; then + python3 -m dfss --url=open@sophgo.com:sophon-demo/YOLOv8_plus_det/BM1684X.tar.gz + tar xvf BM1684X.tar.gz && rm BM1684X.tar.gz + echo "models/BM1684X download!" + fi +else + echo "models/BM1684X folder exist! Remove it if you need to update." +fi + +if [ ! -d "../resources/models/BM1688" ]; +then + if [ $download_bm1688 == 1 ]; then + python3 -m dfss --url=open@sophgo.com:sophon-demo/YOLOv8_plus_det/BM1688.tar.gz + tar xvf BM1688.tar.gz && rm BM1688.tar.gz + echo "models/BM1688 download!" + fi +else + echo "models/BM1688 folder exist! Remove it if you need to update." +fi + +if [ ! -d "../resources/models/CV186X" ]; +then + if [ $download_cv186x == 1 ]; then + python3 -m dfss --url=open@sophgo.com:sophon-demo/YOLOv8_plus_det/CV186X.tar.gz + tar xvf CV186X.tar.gz && rm CV186X.tar.gz + echo "models/CV186X download!" + fi +else + echo "models/BM1688 folder exist! Remove it if you need to update." +fi + +if [ ! -d "../resources/models/onnx" ]; +then + if [ $download_onnx == 1 ]; then + python3 -m dfss --url=open@sophgo.com:sophon-demo/YOLOv8_plus_det/onnx.tar.gz + tar xvf onnx.tar.gz && rm onnx.tar.gz + echo "models/onnx download!" + fi +else + echo "models/onnx folder exist! Remove it if you need to update." +fi +popd + +popd \ No newline at end of file diff --git a/src/sophon_ai_project/__init__.py b/src/sophon_ai_project/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/sophon_ai_project/postprocess_numpy.py b/src/sophon_ai_project/postprocess_numpy.py new file mode 100644 index 0000000..ee82d4b --- /dev/null +++ b/src/sophon_ai_project/postprocess_numpy.py @@ -0,0 +1,190 @@ +#===----------------------------------------------------------------------===# +# +# Copyright (C) 2022 Sophgo Technologies Inc. All rights reserved. +# +# SOPHON-DEMO is licensed under the 2-Clause BSD License except for the +# third-party components. +# +#===----------------------------------------------------------------------===# +import numpy as np + +class PostProcess: + def __init__(self, conf_thresh=0.001, nms_thresh=0.7, agnostic=False, multi_label=True, max_det=300): + self.conf_thresh = conf_thresh + self.nms_thresh = nms_thresh + self.agnostic_nms = agnostic + self.multi_label = multi_label + self.max_det = max_det + self.nms = pseudo_torch_nms() + + def __call__(self, preds_batch, org_size_batch, ratios_batch, txy_batch): + """ + post-processing + :param preds_batch: list of predictions in a batch + :param org_size_batch: list of (org_img_w, org_img_h) in a batch + :param ratios_batch: list of (ratio_x, ratio_y) in a batch when resize-and-center-padding + :param txy_batch: list of (tx, ty) in a batch when resize-and-center-padding + :return: + """ + + if isinstance(preds_batch, list) and len(preds_batch) == 1: + # 1 output + dets = np.concatenate(preds_batch) + else: + print('preds_batch type: '.format(type(preds_batch))) + raise NotImplementedError + + outs = self.nms.non_max_suppression(dets, + self.conf_thresh, + self.nms_thresh, + agnostic=False, + max_det=300, + multi_label=self.multi_label, + classes=None) + + # Rescale boxes from img_size to im0 size + for det, (org_w, org_h), ratio, (tx1, ty1) in zip(outs, org_size_batch, ratios_batch, txy_batch): + if len(det): + # Rescale boxes from img_size to im0 size + coords = det[:, :4] + coords[:, [0, 2]] -= tx1 # x padding + coords[:, [1, 3]] -= ty1 # y padding + coords[:, [0, 2]] /= ratio[0] + coords[:, [1, 3]] /= ratio[1] + + coords[:, [0, 2]] = coords[:, [0, 2]].clip(0, org_w - 1) # x1, x2 + coords[:, [1, 3]] = coords[:, [1, 3]].clip(0, org_h - 1) # y1, y2 + + det[:, :4] = coords + + return outs + +# numpy multiclass nms implementation from original yolov5 repo torch implementation +class pseudo_torch_nms: + def nms_boxes(self, boxes, scores, iou_thres): + x = boxes[:, 0] + y = boxes[:, 1] + w = boxes[:, 2] - boxes[:, 0] + h = boxes[:, 3] - boxes[:, 1] + + areas = w * h + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + + xx1 = np.maximum(x[i], x[order[1:]]) + yy1 = np.maximum(y[i], y[order[1:]]) + xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]]) + yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]]) + + w1 = np.maximum(0.0, xx2 - xx1 + 0.00001) + h1 = np.maximum(0.0, yy2 - yy1 + 0.00001) + inter = w1 * h1 + + ovr = inter / (areas[i] + areas[order[1:]] - inter) + inds = np.where(ovr <= iou_thres)[0] + order = order[inds + 1] + keep = np.array(keep) + return keep + + def xywh2xyxy(self, x): + # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right + y = x.copy() if isinstance(x, np.ndarray) else 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 nms(self, pred, conf_thres=0.001, iou_thres=0.5, agnostic=False, max_det=1000): + return self.non_max_suppression(pred, conf_thres, iou_thres, + classes=None, + agnostic=agnostic, + multi_label=True, + max_det=max_det) + + def non_max_suppression(self, + prediction, + conf_thres=0.25, + iou_thres=0.5, + classes=None, + agnostic=False, + multi_label=False, + labels=(), + max_det=300, + nm=0): + """Non-Maximum Suppression (NMS) on inference results to reject overlapping bounding boxes + + Returns: + list of detections, on (n,6) tensor per image [xyxy, conf, cls] + """ + + bs = prediction.shape[0] # batch size + nc = prediction.shape[2] - nm - 4 # number of classes + mi = 4 + nc # mask start index + xc = prediction[:, :, 4:mi].max(2) > conf_thres # candidates + + # Checks + # assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0' + # assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0' + + # Settings + max_wh = 7680 # (pixels) maximum box width and height + max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() + multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) + + # output = [torch.zeros((0, 6), device=prediction.device)] * bs + output = [np.zeros((0, 6 + nm))] * bs + for xi, x in enumerate(prediction): # image index, image inference + # Apply constraints + # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height + x = x[xc[xi]] # confidence + + # If none remain process next image + if not x.shape[0]: + continue + + # Detections matrix nx6 (xyxy, conf, cls) + box, cls, mask = x[:,:4], x[:, 4:nc+4], x[:, nc+4:] + + # Box (center x, center y, width, height) to (x1, y1, x2, y2) + box = self.xywh2xyxy(box) + + # Detections matrix nx6 (xyxy, conf, cls) + if multi_label: + i, j = (cls > conf_thres).nonzero() + x = np.concatenate((box[i], x[i, 4 + j, None], j[:, None].astype(np.float32), mask[i]), 1) + else: # best class only + conf = cls.max(1, keepdims=True) + j_argmax = cls.argmax(1) + j = j_argmax if j_argmax.shape == x[:, 5:].shape else \ + np.expand_dims(j_argmax, 1) # for argmax(axis, keepdims=True) + x = np.concatenate((box, conf, j.astype(np.float32), mask), 1)[conf.reshape(-1) > conf_thres] + + # Check shape + n = x.shape[0] # number of boxes + if not n: # no boxes + continue + # elif n > max_nms: # excess boxes + # x_argsort = np.argsort(x[:, 4])[:max_nms] # sort by confidence + # x = x[x_argsort] + x_argsort = np.argsort(x[:, 4])[::-1][:max_nms] # sort by confidence + x = x[x_argsort] + # Batched NMS + c = x[:, 5:6] * (0 if agnostic else max_wh) # classes + boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores + + ############################################# + # i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS + i = self.nms_boxes(boxes, scores, iou_thres) + ############################################ + + if i.shape[0] > max_det: # limit detections + i = i[:max_det] + + output[xi] = x[i] + + return output diff --git a/src/sophon_ai_project/utils.py b/src/sophon_ai_project/utils.py new file mode 100644 index 0000000..279b7c1 --- /dev/null +++ b/src/sophon_ai_project/utils.py @@ -0,0 +1,125 @@ +#===----------------------------------------------------------------------===# +# +# Copyright (C) 2022 Sophgo Technologies Inc. All rights reserved. +# +# SOPHON-DEMO is licensed under the 2-Clause BSD License except for the +# third-party components. +# +#===----------------------------------------------------------------------===# +import os +import sys +import numpy as np +import cv2 + +COCO_CLASSES = ('background', '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') + +COLORS = [ + [56, 0, 255], + [226, 255, 0], + [0, 94, 255], + [0, 37, 255], + [0, 255, 94], + [255, 226, 0], + [0, 18, 255], + [255, 151, 0], + [170, 0, 255], + [0, 255, 56], + [255, 0, 75], + [0, 75, 255], + [0, 255, 169], + [255, 0, 207], + [75, 255, 0], + [207, 0, 255], + [37, 0, 255], + [0, 207, 255], + [94, 0, 255], + [0, 255, 113], + [255, 18, 0], + [255, 0, 56], + [18, 0, 255], + [0, 255, 226], + [170, 255, 0], + [255, 0, 245], + [151, 255, 0], + [132, 255, 0], + [75, 0, 255], + [151, 0, 255], + [0, 151, 255], + [132, 0, 255], + [0, 255, 245], + [255, 132, 0], + [226, 0, 255], + [255, 37, 0], + [207, 255, 0], + [0, 255, 207], + [94, 255, 0], + [0, 226, 255], + [56, 255, 0], + [255, 94, 0], + [255, 113, 0], + [0, 132, 255], + [255, 0, 132], + [255, 170, 0], + [255, 0, 188], + [113, 255, 0], + [245, 0, 255], + [113, 0, 255], + [255, 188, 0], + [0, 113, 255], + [255, 0, 0], + [0, 56, 255], + [255, 0, 113], + [0, 255, 188], + [255, 0, 94], + [255, 0, 18], + [18, 255, 0], + [0, 255, 132], + [0, 188, 255], + [0, 245, 255], + [0, 169, 255], + [37, 255, 0], + [255, 0, 151], + [188, 0, 255], + [0, 255, 37], + [0, 255, 0], + [255, 0, 170], + [255, 0, 37], + [255, 75, 0], + [0, 0, 255], + [255, 207, 0], + [255, 0, 226], + [255, 245, 0], + [188, 255, 0], + [0, 255, 18], + [0, 255, 75], + [0, 255, 151], + [255, 56, 0], + [245, 255, 0], + ] + +def is_img(file_name): + """judge the file is available image or not + Args: + file_name (str): input file name + Returns: + (bool) : whether the file is available image + """ + fmt = os.path.splitext(file_name)[-1] + if isinstance(fmt, str) and fmt.lower() in ['.jpg','.png','.jpeg','.bmp','.jpeg','.webp']: + return True + else: + return False + diff --git a/src/sophon_ai_project/yolov8_bmcv.py b/src/sophon_ai_project/yolov8_bmcv.py new file mode 100644 index 0000000..3dbda75 --- /dev/null +++ b/src/sophon_ai_project/yolov8_bmcv.py @@ -0,0 +1,393 @@ +#===----------------------------------------------------------------------===# +# +# Copyright (C) 2022 Sophgo Technologies Inc. All rights reserved. +# +# SOPHON-DEMO is licensed under the 2-Clause BSD License except for the +# third-party components. +# +#===----------------------------------------------------------------------===# +import os +import time +import json +import argparse +import numpy as np +import sophon.sail as sail +from .postprocess_numpy import PostProcess +from .utils import COCO_CLASSES, COLORS +import logging +logging.basicConfig(level=logging.INFO) + +class YOLOv8: + def __init__(self, args): + # load bmodel + self.net = sail.Engine(args.bmodel, args.dev_id, sail.IOMode.SYSO) + logging.debug("load {} success!".format(args.bmodel)) + self.handle = sail.Handle(args.dev_id) + self.bmcv = sail.Bmcv(self.handle) + self.graph_name = self.net.get_graph_names()[0] + + # get input + self.input_name = self.net.get_input_names(self.graph_name)[0] + self.input_dtype= self.net.get_input_dtype(self.graph_name, self.input_name) + self.img_dtype = self.bmcv.get_bm_image_data_format(self.input_dtype) + self.input_scale = self.net.get_input_scale(self.graph_name, self.input_name) + self.input_shape = self.net.get_input_shape(self.graph_name, self.input_name) + self.input_shapes = {self.input_name: self.input_shape} + + # get output + self.output_names = self.net.get_output_names(self.graph_name) + self.output_tensors = {} + self.output_scales = {} + for output_name in self.output_names: + output_shape = self.net.get_output_shape(self.graph_name, output_name) + output_dtype = self.net.get_output_dtype(self.graph_name, output_name) + output_scale = self.net.get_output_scale(self.graph_name, output_name) + output = sail.Tensor(self.handle, output_shape, output_dtype, True, True) + self.output_tensors[output_name] = output + self.output_scales[output_name] = output_scale + if(output_shape[1] < output_shape[2]): + raise ValueError('Only support OPT model, please refer to the docs/YOLOv8_Export_Guide.md to export OPT model.') + # check batch size + self.batch_size = self.input_shape[0] + suppoort_batch_size = [1, 2, 3, 4, 8, 16, 32, 64, 128, 256] + if self.batch_size not in suppoort_batch_size: + raise ValueError('batch_size must be {} for bmcv, but got {}'.format(suppoort_batch_size, self.batch_size)) + self.net_h = self.input_shape[2] + self.net_w = self.input_shape[3] + + # init preprocess + self.use_resize_padding = True + self.use_vpp = False + self.ab = [x * self.input_scale / 255. for x in [1, 0, 1, 0, 1, 0]] + + # init postprocess + self.conf_thresh = args.conf_thresh + self.nms_thresh = args.nms_thresh + self.agnostic = False + self.multi_label = False + self.max_det = 300 + + self.postprocess = PostProcess( + conf_thresh=self.conf_thresh, + nms_thresh=self.nms_thresh, + agnostic=self.agnostic, + multi_label=self.multi_label, + max_det=self.max_det, + ) + + # init time + self.preprocess_time = 0.0 + self.inference_time = 0.0 + self.postprocess_time = 0.0 + + def init(self): + self.preprocess_time = 0.0 + self.inference_time = 0.0 + self.postprocess_time = 0.0 + + def preprocess_bmcv(self, input_bmimg): + rgb_planar_img = sail.BMImage(self.handle, input_bmimg.height(), input_bmimg.width(), + sail.Format.FORMAT_RGB_PLANAR, sail.DATA_TYPE_EXT_1N_BYTE) + self.bmcv.convert_format(input_bmimg, rgb_planar_img) + resized_img_rgb, ratio, txy = self.resize_bmcv(rgb_planar_img) + preprocessed_bmimg = sail.BMImage(self.handle, self.net_h, self.net_w, sail.Format.FORMAT_RGB_PLANAR, self.img_dtype) + self.bmcv.convert_to(resized_img_rgb, preprocessed_bmimg, ((self.ab[0], self.ab[1]), \ + (self.ab[2], self.ab[3]), \ + (self.ab[4], self.ab[5]))) + return preprocessed_bmimg, ratio, txy + + def resize_bmcv(self, bmimg): + """ + resize for single sail.BMImage + :param bmimg: + :return: a resize image of sail.BMImage + """ + img_w = bmimg.width() + img_h = bmimg.height() + if self.use_resize_padding: + r_w = self.net_w / img_w + r_h = self.net_h / img_h + r = min(r_w, r_h) + tw = int(round(r * img_w)) + th = int(round(r * img_h)) + tx1, ty1 = self.net_w - tw, self.net_h - th # wh padding + + tx1 /= 2 # divide padding into 2 sides + ty1 /= 2 + + ratio = (r, r) + txy = (tx1, ty1) + attr = sail.PaddingAtrr() + attr.set_stx(int(round(tx1 - 0.1))) + attr.set_sty(int(round(ty1 - 0.1))) + attr.set_w(tw) + attr.set_h(th) + attr.set_r(114) + attr.set_g(114) + attr.set_b(114) + + preprocess_fn = self.bmcv.vpp_crop_and_resize_padding if self.use_vpp else self.bmcv.crop_and_resize_padding + resized_img_rgb = preprocess_fn(bmimg, 0, 0, img_w, img_h, self.net_w, self.net_h, attr, sail.bmcv_resize_algorithm.BMCV_INTER_LINEAR) + else: + r_w = self.net_w / img_w + r_h = self.net_h / img_h + ratio = (r_w, r_h) + txy = (0, 0) + preprocess_fn = self.bmcv.vpp_resize if self.use_vpp else self.bmcv.resize + resized_img_rgb = preprocess_fn(bmimg, self.net_w, self.net_h) + return resized_img_rgb, ratio, txy + + def predict(self, input_tensor, img_num): + """ + ensure output order: loc_data, conf_preds, mask_data, proto_data + Args: + input_tensor: + Returns: + """ + input_tensors = {self.input_name: input_tensor} + self.net.process(self.graph_name, input_tensors, self.input_shapes, self.output_tensors) + outputs_dict = {} + for name in self.output_names: + # outputs_dict[name] = self.output_tensors[name].asnumpy()[:img_num] * self.output_scales[name] + outputs_dict[name] = self.output_tensors[name].asnumpy()[:img_num] + # resort + out_keys = list(outputs_dict.keys()) + ord = [] + for n in self.output_names: + for i, k in enumerate(out_keys): + if n in k: + ord.append(i) + break + out = [outputs_dict[out_keys[i]] for i in ord] + return out + + def __call__(self, bmimg_list): + img_num = len(bmimg_list) + ori_size_list = [] + ratio_list = [] + txy_list = [] + if self.batch_size == 1: + ori_h, ori_w = bmimg_list[0].height(), bmimg_list[0].width() + ori_size_list.append((ori_w, ori_h)) + start_time = time.time() + preprocessed_bmimg, ratio, txy = self.preprocess_bmcv(bmimg_list[0]) + self.preprocess_time += time.time() - start_time + ratio_list.append(ratio) + txy_list.append(txy) + + input_tensor = sail.Tensor(self.handle, self.input_shape, self.input_dtype, False, False) + self.bmcv.bm_image_to_tensor(preprocessed_bmimg, input_tensor) + + else: + BMImageArray = eval('sail.BMImageArray{}D'.format(self.batch_size)) + bmimgs = BMImageArray() + for i in range(img_num): + ori_h, ori_w = bmimg_list[i].height(), bmimg_list[i].width() + ori_size_list.append((ori_w, ori_h)) + start_time = time.time() + preprocessed_bmimg, ratio, txy = self.preprocess_bmcv(bmimg_list[i]) + self.preprocess_time += time.time() - start_time + ratio_list.append(ratio) + txy_list.append(txy) + bmimgs[i] = preprocessed_bmimg.data() + input_tensor = sail.Tensor(self.handle, self.input_shape, self.input_dtype, False, False) + self.bmcv.bm_image_to_tensor(bmimgs, input_tensor) + + start_time = time.time() + outputs = self.predict(input_tensor, img_num) + self.inference_time += time.time() - start_time + + start_time = time.time() + results = self.postprocess(outputs, ori_size_list, ratio_list, txy_list) + self.postprocess_time += time.time() - start_time + + return results + +def draw_bmcv(dev_id, image, boxes, output_img_dir, file_name, cn, masks=None, classes_ids=None, conf_scores=None, isvideo=False): + bmcv = sail.Bmcv(sail.Handle(dev_id)) + img_bgr_planar = bmcv.convert_format(image) + thickness = 2 + for idx in range(len(boxes)): + x1, y1, x2, y2 = boxes[idx, :].astype(np.int32).tolist() + if classes_ids is not None: + color = np.array(COLORS[int(classes_ids[idx]) + 1]).astype(np.uint8).tolist() + else: + color = (0, 0, 255) + if (x2 - x1) <= thickness * 2 or (y2 - y1) <= thickness * 2: + logging.info("width or height too small, this rect will not be drawed: (x1={},y1={},w={},h={})".format(x1, y1, x2-x1, y2-y1)) + else: + bmcv.rectangle(img_bgr_planar, x1, y1, (x2 - x1), (y2 - y1), color, thickness) + # bmcv.putText(image, COCO_CLASSES[int(classes_ids[idx] + 1)], x1, y1, tuple(color),1.0,1) + logging.debug("class id={}, score={}, (x1={},y1={},w={},h={})".format(int(classes_ids[idx]), conf_scores[idx], x1, y1, x2-x1, y2-y1)) + if isvideo: + return img_bgr_planar + else: + bmcv.imwrite(os.path.join(output_img_dir, file_name), img_bgr_planar) + +def main(args): + # check params + if not os.path.exists(args.input): + raise FileNotFoundError('{} is not existed.'.format(args.input)) + if not os.path.exists(args.bmodel): + raise FileNotFoundError('{} is not existed.'.format(args.bmodel)) + + # creat save path + output_dir = "./results" + if not os.path.exists(output_dir): + os.mkdir(output_dir) + output_img_dir = os.path.join(output_dir, 'images') + if not os.path.exists(output_img_dir): + os.mkdir(output_img_dir) + + yolov8 = YOLOv8(args) + batch_size = yolov8.batch_size + + # warm up + # bmimg = sail.BMImage(yolov8.handle, 1080, 1920, sail.Format.FORMAT_YUV420P, sail.DATA_TYPE_EXT_1N_BYTE) + # for i in range(10): + # results = yolov8([bmimg]) + yolov8.init() + + decode_time = 0.0 + # test images + if os.path.isdir(args.input): + bmimg_list = [] + filename_list = [] + results_list = [] + cn = 0 + yolov8_handle = sail.Handle(args.dev_id) + for root, dirs, filenames in os.walk(args.input): + for filename in filenames: + if os.path.splitext(filename)[-1].lower() not in ['.jpg','.png','.jpeg','.bmp','.webp']: + continue + img_file = os.path.join(root, filename) + cn += 1 + logging.info("{}, img_file: {}".format(cn, img_file)) + # decode + start_time = time.time() + decoder = sail.Decoder(img_file, True, args.dev_id) + bmimg = sail.BMImage() + ret = decoder.read(yolov8_handle, bmimg) + if ret != 0: + logging.error("{} decode failure.".format(img_file)) + continue + decode_time += time.time() - start_time + bmimg_list.append(bmimg) + filename_list.append(filename) + if (len(bmimg_list) == batch_size or cn == len(filenames)) and len(bmimg_list): + # predict + results = yolov8(bmimg_list) + + for i, filename in enumerate(filename_list): + det = results[i] + # save image + det_draw = det[det[:, -2] > 0.1] + draw_bmcv(args.dev_id, + bmimg_list[i], + det_draw[:,:4], + output_img_dir, + filename, + cn, + masks=None, + classes_ids=det_draw[:, -1], + conf_scores=det_draw[:, -2]) + + # save result + res_dict = dict() + res_dict['image_name'] = filename + res_dict['bboxes'] = [] + for idx in range(det.shape[0]): + bbox_dict = dict() + x1, y1, x2, y2, score, category_id = det[idx] + bbox_dict['bbox'] = [float(round(x1, 3)), float(round(y1, 3)), float(round(x2 - x1,3)), float(round(y2 -y1, 3))] + bbox_dict['category_id'] = int(category_id) + bbox_dict['score'] = float(round(score,5)) + res_dict['bboxes'].append(bbox_dict) + results_list.append(res_dict) + + bmimg_list.clear() + filename_list.clear() + + # save results + if args.input[-1] == '/': + args.input = args.input[:-1] + json_name = os.path.split(args.bmodel)[-1] + "_" + os.path.split(args.input)[-1] + "_bmcv" + "_python_result.json" + with open(os.path.join(output_dir, json_name), 'w') as jf: + # json.dump(results_list, jf) + json.dump(results_list, jf, indent=4, ensure_ascii=False) + logging.info("result saved in {}".format(os.path.join(output_dir, json_name))) + + # test videos + else: + decoder = sail.Decoder(args.input, True, args.dev_id) + if not decoder.is_opened(): + raise Exception("can not open the video") + enc_params = f"width=1920:height=1080:bitrate=2000:gop=32:gop_preset=2:framerate=25" + encoder = sail.Encoder("results/output.mp4", args.dev_id, 'h264_bm', 'NV12', enc_params, 10) + + video_name = os.path.splitext(os.path.split(args.input)[1])[0] + cn = 0 + frame_list = [] + yolov8_handle = sail.Handle(args.dev_id) + end_flag = False + while not end_flag: + frame = sail.BMImage() + start_time = time.time() + ret = decoder.read(yolov8_handle, frame) + decode_time += time.time() - start_time + if ret: #differ from cv. + end_flag = True + else: + frame_list.append(frame) + if (len(frame_list) == batch_size or end_flag) and len(frame_list): + results = yolov8(frame_list) + for i, frame in enumerate(frame_list): + det = results[i] + cn += 1 + logging.info("{}, det nums: {}".format(cn, det.shape[0])) + det_draw = det[det[:, -2] > 0.1] + drawed_bmimg = draw_bmcv(args.dev_id, + frame_list[i], + det_draw[:,:4], + output_img_dir, + video_name, + cn, + masks=None, + classes_ids=det_draw[:, -1], + conf_scores=det_draw[:, -2], + isvideo=True) + encoder.video_write(drawed_bmimg) + frame_list.clear() + decoder.release() + encoder.release() + logging.info("result saved in {}".format(output_img_dir)) + + + # calculate speed + logging.info("------------------ Predict Time Info ----------------------") + decode_time = decode_time / cn + preprocess_time = yolov8.preprocess_time / cn + inference_time = yolov8.inference_time / cn + postprocess_time = yolov8.postprocess_time / cn + logging.info("decode_time(ms): {:.2f}".format(decode_time * 1000)) + logging.info("preprocess_time(ms): {:.2f}".format(preprocess_time * 1000)) + logging.info("inference_time(ms): {:.2f}".format(inference_time * 1000)) + logging.info("postprocess_time(ms): {:.2f}".format(postprocess_time * 1000)) + +def argsparser(): + parser = argparse.ArgumentParser(prog=__file__) + parser.add_argument('--input', type=str, default='../datasets/test', help='path of input') + parser.add_argument('--bmodel', type=str, default='../models/BM1684X/yolov8s_fp32_1b.bmodel', help='path of bmodel') + parser.add_argument('--dev_id', type=int, default=0, help='dev id') + parser.add_argument('--conf_thresh', type=float, default=0.25, help='confidence threshold') + parser.add_argument('--nms_thresh', type=float, default=0.7, help='nms threshold') + args = parser.parse_args() + return args + +if __name__ == "__main__": + args = argsparser() + main(args) + print('all done.') + + + diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..67c3124 --- /dev/null +++ b/uv.lock @@ -0,0 +1,249 @@ +version = 1 +revision = 3 +requires-python = ">=3.8" 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