Fruits 360 基于CNN实现果蔬识别系统

先看最终效果：

项目主要分为三部分：

1、数据集解析处理
2、模型训练
3、推理计算与GUI可视化

本文使用的数据集是开源的果蔬数据集Fruits 360，下载后截图如下所示：

其中：

Test 表示测试数据集目录

Training 表示训练数据集目录

接下来各自看下对应的数据集情况。

Training 目录截图如下所示：

Test目录截图如下所示：

可以看到：训练集和测试集都是提前划分好的，每个目录下面都有131个子目录，也就是该数据集中一共是131个类别，接下来我们详细统计下数据详情，代码如下：

def lookDataset():'''查看数据集情况'''dataDir = "data/train/"train_dict = {}train_num = 0for one_label in os.listdir(dataDir):oneDir = dataDir + one_label + "/"one_num = len(os.listdir(oneDir))train_dict[one_label] = one_numtrain_num += one_numprint("Total Train Number: ", train_num)with open("train_num.json", "w") as f:f.write(json.dumps(train_dict))dataDir = "data/test/"test_dict = {}test_num = 0for one_label in os.listdir(dataDir):oneDir = dataDir + one_label + "/"one_num = len(os.listdir(oneDir))test_dict[one_label] = one_numtest_num += one_numprint("Total Test Number: ", test_num)with open("test_num.json", "w") as f:f.write(json.dumps(test_dict))

结果输出如下：

训练集-测试集数据量如下：

Total Train Number:  67692
Total Test Number:  22688

其中，我对其各个类别数据量也进行了统计，测试集详情如下所示：

{"Apple Braeburn": 164,"Apple Crimson Snow": 148,"Apple Golden 1": 160,"Apple Golden 2": 164,"Apple Golden 3": 161,"Apple Granny Smith": 164,"Apple Pink Lady": 152,"Apple Red 1": 164,"Apple Red 2": 164,"Apple Red 3": 144,"Apple Red Delicious": 166,"Apple Red Yellow 1": 164,"Apple Red Yellow 2": 219,"Apricot": 164,"Avocado": 143,"Avocado ripe": 166,"Banana": 166,"Banana Lady Finger": 152,"Banana Red": 166,"Beetroot": 150,"Blueberry": 154,"Cactus fruit": 166,"Cantaloupe 1": 164,"Cantaloupe 2": 164,"Carambula": 166,"Cauliflower": 234,"Cherry 1": 164,"Cherry 2": 246,"Cherry Rainier": 246,"Cherry Wax Black": 164,"Cherry Wax Red": 164,"Cherry Wax Yellow": 164,"Chestnut": 153,"Clementine": 166,"Cocos": 166,"Corn": 150,"Corn Husk": 154,"Cucumber Ripe": 130,"Cucumber Ripe 2": 156,"Dates": 166,"Eggplant": 156,"Fig": 234,"Ginger Root": 99,"Granadilla": 166,"Grape Blue": 328,"Grape Pink": 164,"Grape White": 166,"Grape White 2": 166,"Grape White 3": 164,"Grape White 4": 158,"Grapefruit Pink": 166,"Grapefruit White": 164,"Guava": 166,"Hazelnut": 157,"Huckleberry": 166,"Kaki": 166,"Kiwi": 156,"Kohlrabi": 157,"Kumquats": 166,"Lemon": 164,"Lemon Meyer": 166,"Limes": 166,"Lychee": 166,"Mandarine": 166,"Mango": 166,"Mango Red": 142,"Mangostan": 102,"Maracuja": 166,"Melon Piel de Sapo": 246,"Mulberry": 164,"Nectarine": 164,"Nectarine Flat": 160,"Nut Forest": 218,"Nut Pecan": 178,"Onion Red": 150,"Onion Red Peeled": 155,"Onion White": 146,"Orange": 160,"Papaya": 164,"Passion Fruit": 166,"Peach": 164,"Peach 2": 246,"Peach Flat": 164,"Pear": 164,"Pear 2": 232,"Pear Abate": 166,"Pear Forelle": 234,"Pear Kaiser": 102,"Pear Monster": 166,"Pear Red": 222,"Pear Stone": 237,"Pear Williams": 166,"Pepino": 166,"Pepper Green": 148,"Pepper Orange": 234,"Pepper Red": 222,"Pepper Yellow": 222,"Physalis": 164,"Physalis with Husk": 164,"Pineapple": 166,"Pineapple Mini": 163,"Pitahaya Red": 166,"Plum": 151,"Plum 2": 142,"Plum 3": 304,"Pomegranate": 164,"Pomelo Sweetie": 153,"Potato Red": 150,"Potato Red Washed": 151,"Potato Sweet": 150,"Potato White": 150,"Quince": 166,"Rambutan": 164,"Raspberry": 166,"Redcurrant": 164,"Salak": 162,"Strawberry": 164,"Strawberry Wedge": 246,"Tamarillo": 166,"Tangelo": 166,"Tomato 1": 246,"Tomato 2": 225,"Tomato 3": 246,"Tomato 4": 160,"Tomato Cherry Red": 164,"Tomato Heart": 228,"Tomato Maroon": 127,"Tomato not Ripened": 158,"Tomato Yellow": 153,"Walnut": 249,"Watermelon": 157
}

训练集详情如下所示：

{"Apple Braeburn": 492,"Apple Crimson Snow": 444,"Apple Golden 1": 480,"Apple Golden 2": 492,"Apple Golden 3": 481,"Apple Granny Smith": 492,"Apple Pink Lady": 456,"Apple Red 1": 492,"Apple Red 2": 492,"Apple Red 3": 429,"Apple Red Delicious": 490,"Apple Red Yellow 1": 492,"Apple Red Yellow 2": 672,"Apricot": 492,"Avocado": 427,"Avocado ripe": 491,"Banana": 490,"Banana Lady Finger": 450,"Banana Red": 490,"Beetroot": 450,"Blueberry": 462,"Cactus fruit": 490,"Cantaloupe 1": 492,"Cantaloupe 2": 492,"Carambula": 490,"Cauliflower": 702,"Cherry 1": 492,"Cherry 2": 738,"Cherry Rainier": 738,"Cherry Wax Black": 492,"Cherry Wax Red": 492,"Cherry Wax Yellow": 492,"Chestnut": 450,"Clementine": 490,"Cocos": 490,"Corn": 450,"Corn Husk": 462,"Cucumber Ripe": 392,"Cucumber Ripe 2": 468,"Dates": 490,"Eggplant": 468,"Fig": 702,"Ginger Root": 297,"Granadilla": 490,"Grape Blue": 984,"Grape Pink": 492,"Grape White": 490,"Grape White 2": 490,"Grape White 3": 492,"Grape White 4": 471,"Grapefruit Pink": 490,"Grapefruit White": 492,"Guava": 490,"Hazelnut": 464,"Huckleberry": 490,"Kaki": 490,"Kiwi": 466,"Kohlrabi": 471,"Kumquats": 490,"Lemon": 492,"Lemon Meyer": 490,"Limes": 490,"Lychee": 490,"Mandarine": 490,"Mango": 490,"Mango Red": 426,"Mangostan": 300,"Maracuja": 490,"Melon Piel de Sapo": 738,"Mulberry": 492,"Nectarine": 492,"Nectarine Flat": 480,"Nut Forest": 654,"Nut Pecan": 534,"Onion Red": 450,"Onion Red Peeled": 445,"Onion White": 438,"Orange": 479,"Papaya": 492,"Passion Fruit": 490,"Peach": 492,"Peach 2": 738,"Peach Flat": 492,"Pear": 492,"Pear 2": 696,"Pear Abate": 490,"Pear Forelle": 702,"Pear Kaiser": 300,"Pear Monster": 490,"Pear Red": 666,"Pear Stone": 711,"Pear Williams": 490,"Pepino": 490,"Pepper Green": 444,"Pepper Orange": 702,"Pepper Red": 666,"Pepper Yellow": 666,"Physalis": 492,"Physalis with Husk": 492,"Pineapple": 490,"Pineapple Mini": 493,"Pitahaya Red": 490,"Plum": 447,"Plum 2": 420,"Plum 3": 900,"Pomegranate": 492,"Pomelo Sweetie": 450,"Potato Red": 450,"Potato Red Washed": 453,"Potato Sweet": 450,"Potato White": 450,"Quince": 490,"Rambutan": 492,"Raspberry": 490,"Redcurrant": 492,"Salak": 490,"Strawberry": 492,"Strawberry Wedge": 738,"Tamarillo": 490,"Tangelo": 490,"Tomato 1": 738,"Tomato 2": 672,"Tomato 3": 738,"Tomato 4": 479,"Tomato Cherry Red": 492,"Tomato Heart": 684,"Tomato Maroon": 367,"Tomato not Ripened": 474,"Tomato Yellow": 459,"Walnut": 735,"Watermelon": 475
}

整体来看，划分得还是比较均衡的，基本维持在3:1的状况。

当然了，如果想要自己对数据集划分，也是可以的，这里我也同样实现了数据集随机比例划分功能，如下所示：

def random2Dataset(dataDir='data/original/',ratio=0.3):'''对原始数据集进行划分，得到：训练集和测试集'''label_list=os.listdir(dataDir)for one_label in label_list:oneDir=dataDir+one_label+'/'pic_list=os.listdir(oneDir)testNum=int(len(pic_list)*ratio)oneTrainDir='data/train/'+one_label+'/'oneTestDir='data/test/'+one_label+'/'if not os.path.exists(oneTrainDir):os.makedirs(oneTrainDir)if not os.path.exists(oneTestDir):os.makedirs(oneTestDir)#创建测试集for i in range(testNum):one_path=oneDir+random.choice(os.listdir(oneDir))name=str(len(os.listdir(oneTestDir))+1)new_path=oneTestDir+one_label+'_'+name+'.jpg'shutil.move(one_path,new_path)#创建训练集for one_pic in os.listdir(oneDir):one_path=oneDir+one_picname=str(len(os.listdir(oneTrainDir))+1)new_path=oneTrainDir+one_label+'_'+name+'.jpg'shutil.move(one_path,new_path)

可以根据自己的实际情况进行选择。

模型层面我基于VGG主干网络进行改造，设计新的网络模型，如下所示：

可以看到：参数量缩减了很多。

我默认设置了1000次的迭代，实际观察发现：不到10次就足够了，我们可以看下训练可视化的曲线：

准确度曲线：

损失曲线：

我在模型训练结束的时候在测试集总数据集上面进行了测试，准确率达到了96.55%，如下所示：

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