数据集找不见了，可以自己拍点。。。。。。代码是对的

物体识别

1. 读取每个图片文件, 加载每个文件的特征值描述矩阵, 整理训练集, 与某个类别名绑定在一起.
2. 基于隐马模型, 对三个类别的特征值描述矩阵训练集进行训练, 得到3个隐马模型, 分别用于识别三个类别.
3. 对测试集分别进行测试, 取得分高的为最终预测类别.

"""
物体识别
"""
import os 
import numpy as np
import hmmlearn.hmm as hl
import cv2 as cv

def search_files(directory):
	directory = os.path.normpath(directory)

	# {'apple':[dir,dir,dir], 'banana':[dir..]}
	objects = {}
	#当前目录, 当前目录子目录, 文件列表
	for curdir,subdirs,files in \
					os.walk(directory):
		for file in files:
			if file.endswith('.jpg'):
				label = curdir.split(os.path.sep)[-1]
				if label not in objects:
					objects[label] = []
				path = os.path.join(curdir, file)
				objects[label].append(path)
	return objects


train_samples = \
	search_files('../ml_data/objects/training')

# 整理训练集, 把每一个类别中的图片的desc
# 摞在一起, 基于隐马模型开始训练.

train_x, train_y = [], []
for label, filenames in train_samples.items():
	descs = np.array([])
	for filename in filenames:
		# 获取当前图片的desc描述矩阵加入descs
		img = cv.imread(filename)
		gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
		# 对图片进行范围缩放
		h, w = gray.shape[:2]
		f = 200 / min(h, w)
		# fx: x轴伸缩比例  fy: y轴伸缩比例
		gray = cv.resize(gray, None, fx=f, fy=f)
		sift = cv.xfeatures2d.SIFT_create()
		keypoints = sift.detect(gray)
		_,desc = sift.compute(gray, keypoints)
		print(label, desc.shape)
		if len(descs) == 0:
			descs = desc
		else:
			descs = np.append(descs, desc, axis=0)
	train_x.append(descs)
	train_y.append(label)

# 基于隐马模型进行训练, 把所有类别的模型都存起来
# 一共3个类别循环3次
models = {}
for descs, label in zip(train_x, train_y):
	model = hl.GaussianHMM(n_components=4, 
		covariance_type='diag', n_iter=200)
	models[label] = model.fit(descs)

# 读取测试集中的文件, 使用每个模型对文件进行
# 评分, 取分值大的模型对应的label作为预测类别
test_samples = \
	search_files('../ml_data/objects/testing')

# 整理测试集, 提取每一个文件的mfcc
test_x, test_y = [], []
for label, filenames in test_samples.items():
	descs = np.array([])
	for filename in filenames:
		# 获取当前图片的desc描述矩阵加入descs
		img = cv.imread(filename)
		gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
		# 对图片进行范围缩放
		h, w = gray.shape[:2]
		f = 200 / min(h, w)
		# fx: x轴伸缩比例  fy: y轴伸缩比例
		gray = cv.resize(gray, None, fx=f, fy=f)
		sift = cv.xfeatures2d.SIFT_create()
		keypoints = sift.detect(gray)
		_,desc = sift.compute(gray, keypoints)
		print(label, desc.shape)
		if len(descs) == 0:
			descs = desc
		else:
			descs = np.append(descs, desc, axis=0)
	test_x.append(descs)
	test_y.append(label)

# 使用3个模型, 对每一个文件进行预测得分.
pred_test_y = []
# test_x一共3个样本, 遍历3次, 每次验证1个文件
for descs in test_x:
	best_score, best_label = None, None
	for label, model in models.items():
		score = model.score(descs)
		if (best_score is None) or \
					(best_score < score):
			best_score, best_label=score,label
	pred_test_y.append(best_label)

print(test_y)
print(pred_test_y)