行业知识
GIS之深度学习10：运行Faster RCNN算法
2026-11-12 21:28:09
来源：互联网转载
（���完成，待补充）
获取Faster RCNN源码
（开源的很多，论文里也有，在这里不多赘述）
替换自己的数据集（图片+标签文件）
（需要使用labeling生成标签文件）
打开终端，进入gpupytorch环境
运行voc_annotation.py文件生成与训练文件
E:\DeepLearningModel\Model01>activate gpupytorch(gpupytorch) E:\DeepLearningModel\Model01>python voc_annotation.pyD:\Anaconda\envs\gpupytorch\lib\site-packages\numpy\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:D:\Anaconda\envs\gpupytorch\lib\site-packages\numpy\.libs\libopenblas.PYQHXLVVQ7VESDPUVUADXEVJOBGHJPAY.gfortran-win_amd64.dllD:\Anaconda\envs\gpupytorch\lib\site-packages\numpy\.libs\libopenblas64__v0.3.21-gcc_10_3_0.dll warnings.warn("loaded more than 1 DLL from .libs:\n%s" %Generate txt in ImageSets.train and val size 777train size 699Generate txt in ImageSets done.Generate 2007_train.txt and 2007_val.txt for train. 结果所示：
(gpupytorch) E:\DeepLearningModel\Model01>python voc_annotation.pyD:\Anaconda\envs\gpupytorch\lib\site-packages\numpy\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:D:\Anaconda\envs\gpupytorch\lib\site-packages\numpy\.libs\libopenblas.PYQHXLVVQ7VESDPUVUADXEVJOBGHJPAY.gfortran-win_amd64.dllD:\Anaconda\envs\gpupytorch\lib\site-packages\numpy\.libs\libopenblas64__v0.3.21-gcc_10_3_0.dll warnings.warn("loaded more than 1 DLL from .libs:\n%s" %Generate txt in ImageSets.train and val size 777train size 699Generate txt in ImageSets done.Generate 2007_train.txt and 2007_val.txt for train.Generate 2007_train.txt and 2007_val.txt for train done.| leopard | 174 || boar | 491 || roe_deer | 352 |(gpupytorch) E:\DeepLearningModel\Model01> 运行：train.py文件
import colorsysimport osimport timeimport numpy as npimport torchimport torch.nn as nnfrom PIL import Image, ImageDraw, ImageFontfrom nets.frcnn import FasterRCNNfrom utils.utils import (cvtColor, get_classes, get_new_img_size, resize_image, preprocess_input, show_config)from utils.utils_bbox import DecodeBoxclass FRCNN(object): _defaults = { "model_path" : 'logs/loss_2024_03_05_22_26_24.pth', "classes_path" : 'model_data/voc_classes.txt', "backbone" : "resnet50", "confidence" : 0.5, "nms_iou" : 0.3, 'anchors_size' : [8, 16, 32], "cuda" : True, } @classmethod def get_defaults(cls, n): if n in cls._defaults: return cls._defaults[n] else: return "Unrecognized attribute name '" + n + "'" def __init__(self, **kwargs): self.__dict__.update(self._defaults) for name, value in kwargs.items(): setattr(self, name, value) self._defaults[name] = value self.class_names, self.num_classes = get_classes(self.classes_path) self.std = torch.Tensor([0.1, 0.1, 0.2, 0.2]).repeat(self.num_classes + 1)[None] if self.cuda: self.std = self.std.cuda() self.bbox_util = DecodeBox(self.std, self.num_classes) #---------------------------------------------------# hsv_tuples = [(x / self.num_classes, 1., 1.) for x in range(self.num_classes)] self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples)) self.colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), self.colors)) self.generate() show_config(**self._defaults) #---------------------------------------------------# # 载入模型 #---------------------------------------------------# def generate(self): self.net = FasterRCNN(self.num_classes, "predict", anchor_scales = self.anchors_size, backbone = self.backbone) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') self.net.load_state_dict(torch.load(self.model_path, map_location=device)) self.net = self.net.eval() print('{} model, anchors, and classes loaded.'.format(self.model_path)) if self.cuda: self.net = nn.DataParallel(self.net) self.net = self.net.cuda() #---------------------------------------------------# # 检测图片 #---------------------------------------------------# def detect_image(self, image, crop = False, count = False): #---------------------------------------------------# # 计算输入图片的高和宽 #---------------------------------------------------# image_shape = np.array(np.shape(image)[0:2]) #---------------------------------------------------# # 计算resize后的图片的大小，resize后的图片短边为600 #---------------------------------------------------# input_shape = get_new_img_size(image_shape[0], image_shape[1]) #---------------------------------------------------------# # 在这里将图像转换成RGB图像，防止灰度图在预测时报错。 # 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB #---------------------------------------------------------# image = cvtColor(image) #---------------------------------------------------------# # 给原图像进行resize，resize到短边为600的大小上 #---------------------------------------------------------# image_data = resize_image(image, [input_shape[1], input_shape[0]]) #---------------------------------------------------------# # 添加上batch_size维度 #---------------------------------------------------------# image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0) with torch.no_grad(): images = torch.from_numpy(image_data) if self.cuda: images = images.cuda() #-------------------------------------------------------------# # roi_cls_locs 建议框的调整参数 # roi_scores 建议框的种类得分 # rois 建议框的坐标 #-------------------------------------------------------------# roi_cls_locs, roi_scores, rois, _ = self.net(images) #-------------------------------------------------------------# # 利用classifier的预测结果对建议框进行解码，获得预测框 #-------------------------------------------------------------# results = self.bbox_util.forward(roi_cls_locs, roi_scores, rois, image_shape, input_shape, nms_iou = self.nms_iou, confidence = self.confidence) #---------------------------------------------------------# # 如果没有检测出物体，返回原图 #---------------------------------------------------------# if len(results[0]) 0: print(self.class_names[i], " : ", num) classes_nums[i] = num print("classes_nums:", classes_nums) #---------------------------------------------------------# # 是否进行目标的裁剪 #---------------------------------------------------------# if crop: for i, c in list(enumerate(top_label)): top, left, bottom, right = top_boxes[i] top = max(0, np.floor(top).astype('int32')) left = max(0, np.floor(left).astype('int32')) bottom = min(image.size[1], np.floor(bottom).astype('int32')) right = min(image.size[0], np.floor(right).astype('int32')) dir_save_path = "img_crop" if not os.path.exists(dir_save_path): os.makedirs(dir_save_path) crop_image = image.crop([left, top, right, bottom]) crop_image.save(os.path.join(dir_save_path, "crop_" + str(i) + ".png"), quality=95, subsampling=0) print("save crop_" + str(i) + ".png to " + dir_save_path) #---------------------------------------------------------# # 图像绘制 #---------------------------------------------------------# for i, c in list(enumerate(top_label)): predicted_class = self.class_names[int(c)] box = top_boxes[i] score = top_conf[i] top, left, bottom, right = box top = max(0, np.floor(top).astype('int32')) left = max(0, np.floor(left).astype('int32')) bottom = min(image.size[1], np.floor(bottom).astype('int32')) right = min(image.size[0], np.floor(right).astype('int32')) label = '{} {:.2f}'.format(predicted_class, score) draw = ImageDraw.Draw(image) label_size = draw.textsize(label, font) label = label.encode('utf-8') # print(label, top, left, bottom, right) if top - label_size[1] >= 0: text_origin = np.array([left, top - label_size[1]]) else: text_origin = np.array([left, top + 1]) for i in range(thickness): draw.rectangle([left + i, top + i, right - i, bottom - i], outline=self.colors[c]) draw.rectangle([tuple(text_origin), tuple(text_origin + label_size)], fill=self.colors[c]) draw.text(text_origin, str(label,'UTF-8'), fill=(0, 0, 0), font=font) del draw return image def get_FPS(self, image, test_interval): #---------------------------------------------------# # 计算输入图片的高和宽 #---------------------------------------------------# image_shape = np.array(np.shape(image)[0:2]) input_shape = get_new_img_size(image_shape[0], image_shape[1]) #---------------------------------------------------------# # 在这里将图像转换成RGB图像，防止灰度图在预测时报错。 # 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB #---------------------------------------------------------# image = cvtColor(image) #---------------------------------------------------------# # 给原图像进行resize，resize到短边为600的大小上 #---------------------------------------------------------# image_data = resize_image(image, [input_shape[1], input_shape[0]]) #---------------------------------------------------------# # 添加上batch_size维度 #---------------------------------------------------------# image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0) with torch.no_grad(): images = torch.from_numpy(image_data) if self.cuda: images = images.cuda() roi_cls_locs, roi_scores, rois, _ = self.net(images) #-------------------------------------------------------------# # 利用classifier的预测结果对建议框进行解码，获得预测框 #-------------------------------------------------------------# results = self.bbox_util.forward(roi_cls_locs, roi_scores, rois, image_shape, input_shape, nms_iou = self.nms_iou, confidence = self.confidence) t1 = time.time() for _ in range(test_interval): with torch.no_grad(): roi_cls_locs, roi_scores, rois, _ = self.net(images) #-------------------------------------------------------------# # 利用classifier的预测结果对建议框进行解码，获得预测框 #-------------------------------------------------------------# results = self.bbox_util.forward(roi_cls_locs, roi_scores, rois, image_shape, input_shape, nms_iou = self.nms_iou, confidence = self.confidence) t2 = time.time() tact_time = (t2 - t1) / test_interval return tact_time #---------------------------------------------------# # 检测图片 #---------------------------------------------------# def get_map_txt(self, image_id, image, class_names, map_out_path): f = open(os.path.join(map_out_path, "detection-results/"+image_id+".txt"),"w") #---------------------------------------------------# # 计算输入图片的高和宽 #---------------------------------------------------# image_shape = np.array(np.shape(image)[0:2]) input_shape = get_new_img_size(image_shape[0], image_shape[1]) #---------------------------------------------------------# # 在这里将图像转换成RGB图像，防止灰度图在预测时报错。 # 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB #---------------------------------------------------------# image = cvtColor(image) #---------------------------------------------------------# # 给原图像进行resize，resize到短边为600的大小上 #---------------------------------------------------------# image_data = resize_image(image, [input_shape[1], input_shape[0]]) #---------------------------------------------------------# # 添加上batch_size维度 #---------------------------------------------------------# image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0) with torch.no_grad(): images = torch.from_numpy(image_data) if self.cuda: images = images.cuda() roi_cls_locs, roi_scores, rois, _ = self.net(images) #-------------------------------------------------------------# # 利用classifier的预测结果对建议框进行解码，获得预测框 #-------------------------------------------------------------# results = self.bbox_util.forward(roi_cls_locs, roi_scores, rois, image_shape, input_shape, nms_iou = self.nms_iou, confidence = self.confidence) #--------------------------------------# # 如果没有检测到物体，则返回原图 #--------------------------------------# if len(results[0]) activate gpupytorch(gpupytorch) E:\DeepLearningModel\Model01>python train.pyD:\Anaconda\envs\gpupytorch\lib\site-packages\numpy\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:D:\Anaconda\envs\gpupytorch\lib\site-packages\numpy\.libs\libopenblas.PYQHXLVVQ7VESDPUVUADXEVJOBGHJPAY.gfortran-win_amd64.dllD:\Anaconda\envs\gpupytorch\lib\site-packages\numpy\.libs\libopenblas64__v0.3.21-gcc_10_3_0.dll warnings.warn("loaded more than 1 DLL from .libs:\n%s" %Number of devices: 1initialize network with normal typeLoad weights model_data/voc_weights_resnet.pth.Successful Load Key: ['extractor.0.weight', 'extractor.1.weight', 'extractor.1.bias', 'extractor.1.running_mean', 'extractor.1.running_var', 'extractor.1.num_batches_tracked', 'extractor.4.0.conv1.weight', 'extractor.4.0.bn1.weight', 'extractor.4.0.bn1.bias', 'extractor.4.0.bn1.running_mean', 'extractor.4.0.bn1.running_var', 'extractor.4.0.bn1.num_batches_tracked', 'extractor.4.0.conv2.weight', 'extractor.4.0.bn2.weight', 'extractor.4.0.bn2.bias', 'extractor.4.0.bn2.running_mean', 'extractor.4.0.bn2.running_var', 'e ……Successful Load Key Num: 324Fail To Load Key: ['head.cls_loc.weight', 'head.cls_loc.bias', 'head.score.weight', 'head.score.bias'] ……Fail To Load Key num: 4温馨提示，head部分没有载入是正常现象，Backbone部分没有载入是错误的。Configurations:----------------------------------------------------------------------| keys | values|----------------------------------------------------------------------| classes_path | model_data/voc_classes.txt|| model_path | model_data/voc_weights_resnet.pth|| input_shape | [600, 600]|| Init_Epoch | 0|| Freeze_Epoch | 50|| UnFreeze_Epoch | 100|| Freeze_batch_size | 4|| Unfreeze_batch_size | 2|| Freeze_Train | True|| Init_lr | 0.0001|| Min_lr | 1.0000000000000002e-06|| optimizer_type | adam|| momentum | 0.9|| lr_decay_type | cos|| save_period | 5|| save_dir | logs|| num_workers | 4|| num_train | 699|| num_val | 78|----------------------------------------------------------------------Start TrainEpoch 1/100: 0%| | 0/174 [00:00
