├── .idea ├── inspectionProfiles │ └── profiles_settings.xml ├── misc.xml ├── modules.xml ├── workspace.xml └── 垃圾识别.iml ├── 32.jpg ├── README.md ├── __init__.py ├── __pycache__ ├── model.cpython-38.pyc └── model.cpython-39.pyc ├── bestmodel.pth ├── class_indices.json ├── data_set ├── train │ ├── A1bottle │ │ ├── bottle (1).JPG │ │ ├── bottle (10).JPG │ │ ├── bottle (11).JPG │ │ ├── bottle (12).JPG │ │ ├── bottle (13).JPG │ │ ├── bottle (14).JPG │ │ ├── bottle (15).JPG │ │ ├── bottle (16).JPG │ │ ├── bottle (17).JPG │ │ ├── bottle (18).JPG │ │ ├── bottle (19).JPG │ │ ├── bottle (2).JPG │ │ ├── bottle (20).JPG │ │ ├── bottle (3).JPG │ │ ├── bottle (4).JPG │ │ ├── bottle (5).JPG │ │ ├── bottle (6).JPG │ │ ├── bottle (7).JPG │ │ ├── bottle (8).JPG │ │ └── bottle (9).JPG │ ├── A2injector │ │ ├── injector (1).JPG │ │ ├── injector (10).JPG │ │ ├── injector (11).JPG │ │ ├── injector (12).JPG │ │ ├── injector (13).JPG │ │ ├── injector (14).JPG │ │ ├── injector (15).JPG │ │ ├── injector (16).JPG │ │ ├── injector (17).JPG │ │ ├── injector (18).JPG │ │ ├── injector (19).JPG │ │ ├── injector (2).JPG │ │ ├── injector (20).JPG │ │ ├── injector (3).JPG │ │ ├── injector (4).JPG │ │ ├── injector (5).JPG │ │ ├── injector (6).JPG │ │ ├── injector (7).JPG │ │ ├── injector (8).JPG │ │ └── injector (9).JPG │ ├── B1vegetable │ │ ├── vegetable (1).jpg │ │ ├── vegetable (10).jpg │ │ ├── vegetable (11).jpg │ │ ├── vegetable (12).jpg │ │ ├── vegetable (13).jpg │ │ ├── vegetable (14).jpg │ │ ├── vegetable (15).jpg │ │ ├── vegetable (16).jpg │ │ ├── vegetable (17).jpg │ │ ├── vegetable (18).jpg │ │ ├── vegetable (19).jpg │ │ ├── vegetable (2).jpg │ │ ├── vegetable (20).jpg │ │ ├── vegetable (3).jpg │ │ ├── vegetable (4).jpg │ │ ├── vegetable (5).jpg │ │ ├── vegetable (6).jpg │ │ ├── vegetable (7).jpg │ │ ├── vegetable (8).jpg │ │ └── vegetable (9).jpg │ ├── C1mask │ │ ├── mask (1).JPG │ │ ├── mask (10).JPG │ │ ├── mask (11).JPG │ │ ├── mask (12).JPG │ │ ├── mask (13).JPG │ │ ├── mask (14).JPG │ │ ├── mask (15).JPG │ │ ├── mask (16).JPG │ │ ├── mask (17).JPG │ │ ├── mask (18).JPG │ │ ├── mask (19).JPG │ │ ├── mask (2).JPG │ │ ├── mask (20).JPG │ │ ├── mask (3).JPG │ │ ├── mask (4).jpg │ │ ├── mask (5).JPG │ │ ├── mask (6).JPG │ │ ├── mask (7).JPG │ │ ├── mask (8).jpg │ │ └── mask (9).JPG │ ├── C2swab │ │ ├── swab (1).JPG │ │ ├── swab (10).JPG │ │ ├── swab (11).JPG │ │ ├── swab (12).JPG │ │ ├── swab (13).JPG │ │ ├── swab (14).JPG │ │ ├── swab (15).JPG │ │ ├── swab (16).JPG │ │ ├── swab (17).JPG │ │ ├── swab (18).JPG │ │ ├── swab (19).JPG │ │ ├── swab (2).JPG │ │ ├── swab (20).JPG │ │ ├── swab (3).JPG │ │ ├── swab (4).JPG │ │ ├── swab (5).JPG │ │ ├── swab (6).JPG │ │ ├── swab (7).JPG │ │ ├── swab (8).JPG │ │ └── swab (9).JPG │ ├── D1battery │ │ ├── battery (1).jpg │ │ ├── battery (10).JPG │ │ ├── battery (11).jpg │ │ ├── battery (12).jpg │ │ ├── battery (13).jpg │ │ ├── battery (14).jpg │ │ ├── battery (15).jpg │ │ ├── battery (16).jpg │ │ ├── battery (17).jpg │ │ ├── battery (18).jpg │ │ ├── battery (19).jpg │ │ ├── battery (2).jpg │ │ ├── battery (20).jpg │ │ ├── battery (3).jpg │ │ ├── battery (4).jpg │ │ ├── battery (5).JPG │ │ ├── battery (6).JPG │ │ ├── battery (7).JPG │ │ ├── battery (8).JPG │ │ └── battery (9).JPG │ └── D2cigarette │ │ ├── cigarette (1).jpg │ │ ├── cigarette (10).jpg │ │ ├── cigarette (11).jpg │ │ ├── cigarette (12).jpg │ │ ├── cigarette (13).jpg │ │ ├── cigarette (14).jpg │ │ ├── cigarette (15).jpg │ │ ├── cigarette (16).jpg │ │ ├── cigarette (17).jpg │ │ ├── cigarette (18).jpg │ │ ├── cigarette (19).jpg │ │ ├── cigarette (2).jpg │ │ ├── cigarette (20).jpg │ │ ├── cigarette (3).jpg │ │ ├── cigarette (4).jpg │ │ ├── cigarette (5).jpg │ │ ├── cigarette (6).jpg │ │ ├── cigarette (7).jpg │ │ ├── cigarette (8).jpg │ │ └── cigarette (9).jpg └── val │ ├── A1bottle │ ├── bottle (1).JPG │ ├── bottle (2).JPG │ ├── bottle (3).JPG │ ├── bottle (4).JPG │ └── bottle (5).JPG │ ├── A2injector │ ├── injector (1).JPG │ ├── injector (2).JPG │ ├── injector (3).JPG │ ├── injector (4).JPG │ └── injector (5).JPG │ ├── B1vegetable │ ├── vegetable (1).jpg │ ├── vegetable (2).jpg │ ├── vegetable (3).jpg │ ├── vegetable (4).jpg │ └── vegetable (5).jpg │ ├── C1mask │ ├── mask (1).JPG │ ├── mask (2).JPG │ ├── mask (3).JPG │ ├── mask (4).JPG │ └── mask (5).JPG │ ├── C2swab │ ├── swab (1).JPG │ ├── swab (2).JPG │ ├── swab (3).JPG │ ├── swab (4).JPG │ └── swab (5).JPG │ ├── D1battery │ ├── battery (1).jpg │ ├── battery (2).JPG │ ├── battery (3).JPG │ ├── battery (4).jpg │ └── battery (5).jpg │ └── D2cigarette │ ├── cigarette (1).jpg │ ├── cigarette (2).jpg │ ├── cigarette (3).jpg │ ├── cigarette (4).jpg │ └── cigarette (5).jpg ├── mobilenet_v2-b0353104.pth ├── model.py ├── predict.py ├── train.py └── video.py /.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 6 | -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 6 | 7 | -------------------------------------------------------------------------------- /.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/workspace.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 10 | 11 | 12 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 49 | 50 | 51 | 71 | 72 | 73 | 93 | 94 | 95 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 1647591678007 131 | 138 | 139 | 140 | 141 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | 183 | 184 | 185 | 186 | 187 | 188 | 189 | 190 | 191 | 192 | 193 | 194 | 195 | 196 | 197 | 198 | 199 | -------------------------------------------------------------------------------- /.idea/垃圾识别.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /32.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/17513146506/Garbage-identification/04cfbeabbbc7b2e1c75a19cfd1cb8e012923bbcd/32.jpg -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Garbage-identification 2 | 基于深度学习的垃圾识别分类 3 | 基于yolov5垃圾识别与分类----模型训练到数据分割，预测 4 | #作者： 5 | 1466284085@qq.com 6 | 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divisor 12 | return new_ch 13 | 14 | class ConvBNReLU(nn.Sequential): 15 | def __init__(self, in_channel, out_channel, kernel_size=3, stride=1, groups=1): 16 | padding = (kernel_size - 1) // 2 17 | super(ConvBNReLU, self).__init__( 18 | nn.Conv2d(in_channel, out_channel, kernel_size, stride, padding, groups=groups, bias=False), 19 | nn.BatchNorm2d(out_channel), 20 | nn.ReLU6(inplace=True) 21 | ) 22 | 23 | class InvertedResidual(nn.Module): 24 | def __init__(self, in_channel, out_channel, stride, expand_ratio): 25 | super(InvertedResidual, self).__init__() 26 | hidden_channel = in_channel * expand_ratio 27 | self.use_shortcut = stride == 1 and in_channel == out_channel 28 | 29 | layers = [] 30 | if expand_ratio != 1: 31 | # 1x1 pointwise conv 32 | layers.append(ConvBNReLU(in_channel, hidden_channel, kernel_size=1)) 33 | layers.extend([ 34 | # 3x3 depthwise conv 35 | ConvBNReLU(hidden_channel, hidden_channel, stride=stride, groups=hidden_channel), 36 | # 1x1 pointwise conv(linear) 37 | nn.Conv2d(hidden_channel, out_channel, kernel_size=1, bias=False), 38 | nn.BatchNorm2d(out_channel), 39 | ]) 40 | 41 | self.conv = nn.Sequential(*layers) 42 | 43 | def forward(self, x): 44 | if self.use_shortcut: 45 | return x + self.conv(x) 46 | else: 47 | return self.conv(x) 48 | 49 | class MobileNetV2(nn.Module): 50 | def __init__(self, num_classes=1000, alpha=1.0, round_nearest=8): 51 | super(MobileNetV2, self).__init__() 52 | block = InvertedResidual 53 | input_channel = _make_divisible(32 * alpha, round_nearest) 54 | last_channel = _make_divisible(1280 * alpha, round_nearest) 55 | 56 | inverted_residual_setting = [ 57 | # t, c, n, s 58 | [1, 16, 1, 1], 59 | [6, 24, 2, 2], 60 | [6, 32, 3, 2], 61 | [6, 64, 4, 2], 62 | [6, 96, 3, 1], 63 | [6, 160, 3, 2], 64 | [6, 320, 1, 1], 65 | ] 66 | 67 | features = [] 68 | # conv1 layer 69 | features.append(ConvBNReLU(3, input_channel, stride=2)) 70 | # building inverted residual residual blockes 71 | for t, c, n, s in inverted_residual_setting: 72 | output_channel = _make_divisible(c * alpha, round_nearest) 73 | for i in range(n): 74 | stride = s if i == 0 else 1 75 | features.append(block(input_channel, output_channel, stride, expand_ratio=t)) 76 | input_channel = output_channel 77 | # building last several layers 78 | features.append(ConvBNReLU(input_channel, last_channel, 1)) 79 | # combine feature layers 80 | self.features = nn.Sequential(*features) 81 | 82 | # building classifier 83 | self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) 84 | self.classifier = nn.Sequential( 85 | nn.Dropout(0.2), 86 | nn.Linear(last_channel, num_classes) 87 | ) 88 | 89 | # weight initialization 90 | for m in self.modules(): 91 | if isinstance(m, nn.Conv2d): 92 | nn.init.kaiming_normal_(m.weight, mode='fan_out') 93 | if m.bias is not None: 94 | nn.init.zeros_(m.bias) 95 | elif isinstance(m, nn.BatchNorm2d): 96 | nn.init.ones_(m.weight) 97 | nn.init.zeros_(m.bias) 98 | elif isinstance(m, nn.Linear): 99 | nn.init.normal_(m.weight, 0, 0.01) 100 | nn.init.zeros_(m.bias) 101 | 102 | def forward(self, x): 103 | x = self.features(x) 104 | x = self.avgpool(x) 105 | x = torch.flatten(x, 1) 106 | x = self.classifier(x) 107 | return x -------------------------------------------------------------------------------- /predict.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from PIL import Image 3 | from torchvision import transforms 4 | import matplotlib.pyplot as plt 5 | import json 6 | import time 7 | import cv2 8 | import numpy as np 9 | cur_time = time.time() 10 | from model import MobileNetV2 11 | 12 | 13 | 14 | print(torch.__version__) 15 | data_transform = transforms.Compose( 16 | [transforms.Resize(256), 17 | transforms.CenterCrop(224), 18 | transforms.ToTensor(), 19 | transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) 20 | # load image 21 | cap=cv2.VideoCapture(0) 22 | while(1): 23 | ret,frame =cap.read() 24 | if ret==True: 25 | # print (type(frame)) 26 | print (ret)#观察frame和ret的类型 27 | img = Image.fromarray(frame)#完成np.array向PIL.Image格式的转换 28 | id=0 29 | img = data_transform(img) 30 | # expand batch dimension 31 | img = torch.unsqueeze(img, dim=0) 32 | # print("img:", img) 33 | # read class_indict 34 | try: 35 | json_file = open('./class_indices.json', 'r') 36 | class_indict = json.load(json_file) 37 | except Exception as e: 38 | print(e) 39 | exit(-1) 40 | # create model 41 | model = MobileNetV2(num_classes=7) 42 | # load model weights 43 | model_weight_path = "bestmodel.pth" 44 | model.load_state_dict(torch.load(model_weight_path)) 45 | model.eval() 46 | with torch.no_grad(): 47 | output = torch.squeeze(model(img)) 48 | out=np.array(output) 49 | predict = torch.softmax(output, dim=0) 50 | predict_cla = torch.argmax(predict).numpy() 51 | print(predict_cla) 52 | predict_cla = int(predict_cla) 53 | type=['unknow','D1battery','A2injector','B1vegetable','C1mask','C2swab','A1bottle','D2cigarette'] 54 | predict_cla=type[predict_cla] 55 | print(predict_cla) 56 | font = cv2.FONT_HERSHEY_SIMPLEX 57 | show=cv2.putText(frame,str(predict_cla), (10,50), font, 2, (255,255,0), 5) 58 | cv2.imshow('123',show) 59 | if cv2.waitKey(1) & 0xFF == ord('q'): 60 | break 61 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | from torchvision import transforms, datasets 4 | import json 5 | import os 6 | import torch.optim as optim 7 | from model import MobileNetV2 8 | device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") 9 | print(device) 10 | data_transform = { 11 | "train": transforms.Compose([transforms.RandomResizedCrop(224), 12 | transforms.RandomHorizontalFlip(), 13 | transforms.ToTensor(), 14 | transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]), 15 | "val": transforms.Compose([transforms.Resize(256), 16 | transforms.CenterCrop(224), 17 | transforms.ToTensor(), 18 | transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])} 19 | 20 | image_path = "./data_set/" # flower data set path 21 | train_dataset = datasets.ImageFolder(root=image_path+"train", 22 | transform=data_transform["train"]) 23 | train_num = len(train_dataset) 24 | flower_list = train_dataset.class_to_idx 25 | cla_dict = dict((val, key) for key, val in flower_list.items()) 26 | # write dict into json file 27 | json_str = json.dumps(cla_dict, indent=6) 28 | with open('class_indices.json', 'w') as json_file: 29 | json_file.write(json_str) 30 | 31 | 32 | batch_size = 16 33 | train_loader = torch.utils.data.DataLoader(train_dataset, 34 | batch_size=batch_size, shuffle=True, 35 | num_workers=0) 36 | 37 | validate_dataset = datasets.ImageFolder(root=image_path + "val", 38 | transform=data_transform["val"]) 39 | val_num = len(validate_dataset) 40 | validate_loader = torch.utils.data.DataLoader(validate_dataset, 41 | batch_size=batch_size, shuffle=False, 42 | num_workers=0) 43 | 44 | net = MobileNetV2(num_classes=7) 45 | 46 | # load pretrain weights 47 | model_weight_path = "./mobilenet_v2-b0353104.pth" 48 | pre_weights = torch.load(model_weight_path) 49 | # delete classifier weights 50 | pre_dict = {k: v for k, v in pre_weights.items() if "classifier" not in k} 51 | missing_keys, unexpected_keys = net.load_state_dict(pre_dict, strict=False) 52 | 53 | # freeze features weights 54 | for param in net.features.parameters(): 55 | param.requires_grad = False 56 | 57 | net.to(device) 58 | 59 | loss_function = nn.CrossEntropyLoss() 60 | optimizer = optim.Adam(net.parameters(), lr=0.00001) 61 | 62 | best_acc = 0.0 63 | save_path = './bestmodel.pth' 64 | # save_path = './save/mobilenet_v2_1.4_224_' 65 | for epoch in range(50): 66 | # train 67 | net.train() 68 | running_loss = 0.0 69 | for step, data in enumerate(train_loader, start=0): 70 | images, labels = data 71 | optimizer.zero_grad() 72 | logits = net(images.to(device)) 73 | loss = loss_function(logits, labels.to(device)) 74 | loss.backward() 75 | optimizer.step() 76 | # print statistics 77 | running_loss += loss.item() 78 | # print train process 79 | rate = (step+1)/len(train_loader) 80 | a = "*" * int(rate * 50) 81 | b = "." * int((1 - rate) * 50) 82 | print("\rtrain loss: {:^3.0f}%[{}->{}]{:.4f}".format(int(rate*100), a, b, loss), end="") 83 | print() 84 | 85 | # validate 86 | net.eval() 87 | acc = 0.0 # accumulate accurate number / epoch 88 | with torch.no_grad(): 89 | for val_data in validate_loader: 90 | val_images, val_labels = val_data 91 | outputs = net(val_images.to(device)) # eval model only have last output layer 92 | # loss = loss_function(outputs, test_labels) 93 | predict_y = torch.max(outputs, dim=1)[1] 94 | acc += (predict_y == val_labels.to(device)).sum().item() 95 | val_accurate = acc / val_num 96 | if val_accurate > best_acc: 97 | best_acc = val_accurate 98 | torch.save(net.state_dict(), save_path) 99 | print('[epoch %d] train_loss: %.3f test_accuracy: %.3f' % 100 | (epoch + 1, running_loss / step, val_accurate)) 101 | 102 | print('Finished Training') -------------------------------------------------------------------------------- /video.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import time 3 | camera = cv2.VideoCapture(0) 4 | if camera is None: 5 | print('请先连接摄像头') 6 | exit() 7 | fps = 5 # 帧率 8 | pre_frame = None # 总是取前一帧做为背景（不用考虑环境影响） 9 | play_music = False 10 | i = 0 11 | while True: 12 | start = time.time() 13 | res, cur_frame = camera.read() 14 | if res != True: 15 | break 16 | end = time.time() 17 | seconds = end - start 18 | if seconds < 1.0 / fps: 19 | time.sleep(1.0 / fps - seconds) 20 | cv2.imshow('img', cur_frame) 21 | key = cv2.waitKey(30) & 0xff 22 | if key == 27: 23 | break 24 | gray_img = cv2.cvtColor(cur_frame, cv2.COLOR_BGR2GRAY) 25 | gray_img = cv2.resize(gray_img, (500, 500)) 26 | gray_img = cv2.GaussianBlur(gray_img, (21, 21), 0) 27 | if pre_frame is None: 28 | pre_frame = gray_img 29 | else: 30 | img_delta = cv2.absdiff(pre_frame, gray_img) 31 | thresh = cv2.threshold(img_delta, 25, 255, cv2.THRESH_BINARY)[1] 32 | thresh = cv2.dilate(thresh, None, iterations=2) 33 | image, contours = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) 34 | for c in image: 35 | if cv2.contourArea(c) < 100: # 设置敏感度 36 | continue 37 | else: 38 | # print(cv2.contourArea(c)) 39 | print(i) 40 | i += 1 41 | time.sleep(0.1) 42 | cv2.imwrite("a.jpg", cur_frame) 43 | play_music = True 44 | break 45 | pre_frame = gray_img 46 | camera.release() 47 | cv2.destroyAllWindows() --------------------------------------------------------------------------------