yolotv5和resnet152模型预测

我已经训练完成了yolov5检测和resnet152分类的模型，下面开始对一张图片进行检测分类。

首先用yolo算法对猫和狗进行检测，然后将检测到的目标进行裁剪，然后用resnet152对裁剪的图片进行分类。

首先我有以下这些训练好的模型

 猫狗检测的，猫的分类，狗的分类

 

我的预测文件my_detect.py

import os import sys from pathlib import Path  from tools_detect import draw_box_and_save_img, dataLoad, predict_classify, detect_img_2_classify_img, get_time_uuid  FILE = Path(__file__).resolve() ROOT = FILE.parents[0]  # YOLOv5 root directory if str(ROOT) not in sys.path:     sys.path.append(str(ROOT))  # add ROOT to PATH ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative  from models.common import DetectMultiBackend from utils.general import (non_max_suppression) from utils.plots import save_one_box  import config as cfg  conf_thres = cfg.conf_thres iou_thres = cfg.iou_thres  detect_size = cfg.detect_img_size classify_size = cfg.classify_img_size   def detect_img(img, device, detect_weights='', detect_class=[], save_dir=''):     # 选择计算设备     # device = select_device(device)     # 加载数据     imgsz = (detect_size, detect_size)     im0s, im = dataLoad(img, imgsz, device)     # print(im0)     # print(im)     # 加载模型     model = DetectMultiBackend(detect_weights, device=device)     stride, names, pt = model.stride, model.names, model.pt     # print((1, 3, *imgsz))     model.warmup(imgsz=(1, 3, *imgsz))  # warmup      pred = model(im, augment=False, visualize=False)     # print(pred)     pred = non_max_suppression(pred, conf_thres, iou_thres, None, False, max_det=1000)     # print(pred)     im0 = im0s.copy()     # 画框，保存图片     # ret_bytes= None     ret_bytes = draw_box_and_save_img(pred, names, detect_class, save_dir, im0, im)     ret_li = list()     # print(pred)     im0_arc = int(im0.shape[0]) * int(im0.shape[1])     count = 1     for det in reversed(pred[0]):         # print(det)         # print(det)         # 目标太小跳过         xyxy_arc = (int(det[2]) - int(det[0])) * (int(det[3]) - int(det[1]))         # print(xyxy_arc)         if xyxy_arc / im0_arc < 0.01:             continue         # 裁剪图片         xyxy = det[:4]         im_crop = save_one_box(xyxy, im0, file=Path('im.jpg'), gain=1.1, pad=10, square=False, BGR=False, save=False)         # 将裁剪的图片转为分类的大小及tensor类型         im_crop = detect_img_2_classify_img(im_crop, classify_size, device)          d = dict()         # print(det)         c = int(det[-1])         label = detect_class[c]         # 开始做具体分类         if label == detect_class[0]:             classify_predict = predict_classify(cfg.cat_weight, im_crop, device)             classify_label = cfg.cat_class[int(classify_predict)]         else:             classify_predict = predict_classify(cfg.dog_weight, im_crop, device)             classify_label = cfg.dog_class[int(classify_predict)]         # print(classify_label)         d['details'] = classify_label         conf = round(float(det[-2]), 2)         d['label'] = label+str(count)         d['conf'] = conf         ret_li.append(d)         count += 1      return ret_li, ret_bytes   def start_predict(img, save_dir=''):     weights = cfg.detect_weight     detect_class = cfg.detect_class     device = cfg.device     ret_li, ret_bytes = detect_img(img, device, weights, detect_class, save_dir)     # print(ret_li)     return ret_li, ret_bytes   if __name__ == '__main__':     name = get_time_uuid()     save_dir = f'./save/{name}.jpg'     # path = r'./test_img/hashiqi20230312_00010.jpg'     path = r'./test_img/hashiqi20230312_00116.jpg'     # path = r'./test_img/kejiquan20230312_00046.jpg'     f = open(path, 'rb')     img = f.read()     f.close()     # print(img)     # print(type(img))     img_ret_li, img_bytes = start_predict(img, save_dir=save_dir)     print(img_ret_li)

 

我的tools_detect.py文件

import datetime import os import random import sys import time from pathlib import Path  import torch from PIL import Image from torch import nn  from utils.augmentations import letterbox  FILE = Path(__file__).resolve() ROOT = FILE.parents[0]  # YOLOv5 root directory if str(ROOT) not in sys.path:     sys.path.append(str(ROOT))  # add ROOT to PATH ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative  from utils.general import (cv2,                            scale_boxes, xyxy2xywh) from utils.plots import Annotator, colors import numpy as np  def bytes_to_ndarray(byte_img):     """     图片二进制转numpy格式     """     image = np.asarray(bytearray(byte_img), dtype="uint8")     image = cv2.imdecode(image, cv2.IMREAD_COLOR)     return image   def ndarray_to_bytes(ndarray_img):     """     图片numpy格式转二进制     """     ret, buf = cv2.imencode(".jpg", ndarray_img)     img_bin = Image.fromarray(np.uint8(buf)).tobytes()     # print(type(img_bin))     return img_bin  def get_time_uuid():     """         :return: 20220525140635467912         :PS ：并发较高时尾部随机数增加     """     uid = str(datetime.datetime.fromtimestamp(time.time())).replace("-", "").replace(" ", "").replace(":","").replace(".", "") + str(random.randint(100, 999))     return uid   def dataLoad(img, img_size, device, half=False):     image = bytes_to_ndarray(img)     # print(image.shape)     im = letterbox(image, img_size)[0]  # padded resize     im = im.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB     im = np.ascontiguousarray(im)  # contiguous      im = torch.from_numpy(im).to(device)     im = im.half() if half else im.float()  # uint8 to fp16/32     im /= 255  # 0 - 255 to 0.0 - 1.0     if len(im.shape) == 3:         im = im[None]  # expand for batch dim      return image, im   def draw_box_and_save_img(pred, names, class_names, save_dir, im0, im):      save_path = save_dir     fontpath = "./simsun.ttc"     for i, det in enumerate(pred):         annotator = Annotator(im0, line_width=3, example=str(names), font=fontpath, pil=True)         if len(det):             det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()             count = 1             im0_arc = int(im0.shape[0]) * int(im0.shape[1])             gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]             base_path = os.path.split(save_path)[0]             file_name = os.path.split(save_path)[1].split('.')[0]             txt_path = os.path.join(base_path, 'labels')             if not os.path.exists(txt_path):                 os.mkdir(txt_path)             txt_path = os.path.join(txt_path, file_name)             for *xyxy, conf, cls in reversed(det):                 # 目标太小跳过                 xyxy_arc = (int(xyxy[2]) - int(xyxy[0])) * (int(xyxy[3]) - int(xyxy[1]))                 # print(im0.shape, xyxy, xyxy_arc, im0_arc, xyxy_arc / im0_arc)                 if xyxy_arc / im0_arc < 0.01:                     continue                 # print(im0.shape, xyxy)                 c = int(cls)  # integer class                 label = f"{class_names[c]}{count} {round(float(conf), 2)}" #  .encode('utf-8')                 # print(xyxy)                 annotator.box_label(xyxy, label, color=colors(c, True))                  im0 = annotator.result()                 count += 1                 # print(im0)                  # print(type(im0))                 # im0 为 numpy.ndarray类型                  # Write to file                 # print('+++++++++++')                 xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh                 # print(xywh)                 line = (cls, *xywh)  # label format                 with open(f'{txt_path}.txt', 'a') as f:                     f.write(('%g ' * len(line)).rstrip() % line + 'n')     cv2.imwrite(save_path, im0)      ret_bytes = ndarray_to_bytes(im0)     return ret_bytes   def predict_classify(model_path, img, device):     # im = torch.nn.functional.interpolate(img, (160, 160), mode='bilinear', align_corners=True)     # print(device)     if torch.cuda.is_available():         model = torch.load(model_path)     else:         model = torch.load(model_path, map_location='cpu')     # print(help(model))     model.to(device)     model.eval()     predicts = model(img)     _, preds = torch.max(predicts, 1)     pred = torch.squeeze(preds)     # print(pred)     return pred   def detect_img_2_classify_img(img, classify_size, device):     im_crop1 = img.copy()     im_crop1 = np.float32(im_crop1)     image = cv2.resize(im_crop1, (classify_size, classify_size))     image = image.transpose((2, 0, 1))     im = torch.from_numpy(image).unsqueeze(0)     im_crop = im.to(device)     return im_crop

 

我的config.py文件

import torch import os  base_path = r'.weights'  detect_weight = os.path.join(base_path, r'cat_dog_detect/best.pt') detect_class = ['猫', '狗']  cat_weight = os.path.join(base_path, r'cat_predict/best.pt') cat_class = ['东方短毛猫', '亚洲豹猫', '加菲猫', '安哥拉猫', '布偶猫', '德文卷毛猫', '折耳猫', '无毛猫', '暹罗猫', '森林猫', '橘猫', '奶牛猫', '狞猫', '狮子猫', '狸花猫', '玳瑁猫', '白猫', '蓝猫', '蓝白猫', '薮猫', '金渐层猫', '阿比西尼亚猫', '黑猫']  dog_weight = os.path.join(base_path, r'dog_predict/best.pt') dog_class = ['中华田园犬', '博美犬', '吉娃娃', '哈士奇', '喜乐蒂', '巴哥犬', '德牧', '拉布拉多犬', '杜宾犬', '松狮犬', '柯基犬', '柴犬', '比格犬', '比熊', '法国斗牛犬', '秋田犬', '约克夏', '罗威纳犬', '腊肠犬', '萨摩耶', '西高地白梗犬', '贵宾犬', '边境牧羊犬', '金毛犬', '阿拉斯加犬', '雪纳瑞', '马尔济斯犬']  # device = 0 # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') device = torch.device('cpu') conf_thres = 0.5 iou_thres = 0.45  detect_img_size = 416 classify_img_size = 160

 

整体文件结构

 其中models和utils文件夹都是yolov5源码的文件

运行my_detect.py的结果