def search(Image):
idealBins = (8, 12, 3) # 设定HSV的bins
kps = 100
# 打开图片，由于图名可能为中文，不能用imread
queryImage = cv2.imdecode(np.fromfile(Image, dtype=np.uint8), -1)
# 初始化颜色特征提取器和结构特征提取器
colorDescriptor = color_descriptor.ColorDescriptor(idealBins)
structureDescriptor = structure_descriptor.StructureDescriptor(kps)
colorIndexPath = "colorindex.csv"
structureIndexPath = "structureindex.csv"
#获得目标图片的颜色特征和结构特征
queryFeatures = colorDescriptor.describe(queryImage)
queryStructures = structureDescriptor.describe(queryImage)
# 初始化搜索器并进行搜索
imageSearcher = searcher.Searcher(colorIndexPath, structureIndexPath)
searchResults = imageSearcher.search(queryFeatures, queryStructures)
result = []
for imageName, score in searchResults:
#queryResult = cv2.imread(resultPath + "/" + imageName)
result.append(imageName)
return result
def index():
if request.method == 'POST':
file = request.files['query_img']
img = Image.open(file.stream) # PIL image
uploaded_img_path = "static/query/" + file.filename
img.save(uploaded_img_path)
idealBins = (8, 12, 3)
idealDimension = (16, 16)
colorDescriptor = color_descriptor.ColorDescriptor(idealBins)
structureDescriptor = structure_descriptor.StructureDescriptor(
idealDimension)
queryImage = cv2.imread(uploaded_img_path)
colorIndexPath = "static/color_index.csv"
structureIndexPath = "static/structure_index.csv"
resultPath = "static/dataset"
queryFeatures = colorDescriptor.describe(queryImage)
queryStructures = structureDescriptor.describe(queryImage)
imageSearcher = searcher.Searcher(colorIndexPath, structureIndexPath)
searchResults = imageSearcher.search(queryFeatures, queryStructures)
return render_template('index.html',
query_path=uploaded_img_path,
scores=searchResults)
else:
return render_template('index.html')
def find_name(queryImage):
idealBins = (8, 12, 3)
idealDimension = (16, 16)
colorDescriptor = color_descriptor.ColorDescriptor(idealBins) #掩模处理后的直方图
structureDescriptor = structure_descriptor.StructureDescriptor(
idealDimension) #HSV色彩空间的矩阵
#queryImage = cv2.imread("query.jpg")
colorIndexPath = os.path.abspath("colorindex")
structureIndexPath = os.path.abspath("structureindex")
queryFeatures = colorDescriptor.describe(queryImage) #掩模处理后的直方图
queryStructures = structureDescriptor.describe(queryImage) #HSV色彩空间的矩阵
imageSearcher = searcher.Searcher(colorIndexPath, structureIndexPath)
searchResults = imageSearcher.search(queryFeatures, queryStructures)
result = [] #图片名称所在的列表
for imageName, score in searchResults:
result.append(imageName)
return result
def searchEngine(colorIndexPath, structureIndexPath, query):
"""
colorIndexPath: Path to where the computed color index will be stored
structureIndexPath: Path to where the computed structure index will be stored
query: 可以是一个图片的路径，也可以是一个图片的url
当传入图片的url时，会将图片下载到当前工作目录中，形成query.jpg
"""
idealBins = (8, 12, 3)
idealDimension = (16, 16)
colorDescriptor = color_descriptor.ColorDescriptor(idealBins)
structureDescriptor = structure_descriptor.StructureDescriptor(
idealDimension)
if not os.path.exists(query):
try:
downloadpath = os.path.join(os.getcwd(), 'query.jpg')
urllib.urlretrieve(query, downloadpath)
queryImage = cv2.imread('query.jpg')
except Exception, e:
print str(e)
return None
import cv2
import glob
import csv
import re
import numpy
import structure_descriptor
idealDimension = (16, 16)
structureDescriptor = structure_descriptor.StructureDescriptor(idealDimension)
testImage = cv2.imread("query/forest.jpg")
rawQueryStructures = structureDescriptor.describe(testImage)
# index
output = open("structureIndex.csv", "w")
for imagePath in glob.glob("dataset" + "/*.jpg"):
imageName = imagePath[imagePath.rfind("/") + 1:]
image = cv2.imread(imagePath)
structures = structureDescriptor.describe(image)
# write structures to file
structures = [str(structure).replace("\n", "") for structure in structures]
output.write("%s,%s\n" % (imageName, ",".join(structures)))
# close index file
output.close()
# searcher
def solveStructureDistance(self, structures, queryStructures, eps=1e-5):
distance = 0
colorDesriptor = color_descriptor.ColorDescriptor((8, 12, 3))
output = open("H:/CBIR_python2/colorindex.csv", "w")
for imagePath in glob.glob("H:/CBIR_python2/nikeset/*.png"):
#文件名字
imageName = imagePath[24:29]
image = cv2.imread(imagePath)
features = colorDesriptor.describe(image)
#读入特征
features = [str(feature).replace("\n", "") for feature in features]
output.write("%s,%s\n" % (imageName, ",".join(features)))
output.close()
#空间构图特征
structureDescriptor = structure_descriptor.StructureDescriptor((64, 64))
output = open("H:/CBIR_python2/structindex.csv", "w")
for imagePath in glob.glob("H:/CBIR_python2/nikeset/*.png"):
#读取文件名字
imageName = imagePath[24:29]
image = cv2.imread(imagePath)
structures = structureDescriptor.describe(image)
structures = [str(structure).replace("\n", "") for structure in structures]
output.write("%s,%s\n" % (imageName, ",".join(structures)))
output.close()
output = open("H:/CBIR_python2/cannyindex.csv", "w")
for imagePath in glob.glob("H:/CBIR_python2/canny/*.png"):
idealBins = (8, 12, 3)
colorDesriptor = color_descriptor.ColorDescriptor(idealBins)
output = open("colorindex.csv", "w") #arguments["colorindex"], "w")
for imagePath in glob.glob("D:/Photos/airplanes_side" +
"/*.jpg"): #arguments["dataset"] + "/*.jpg"):
imageName = imagePath[imagePath.rfind("/") + 1:]
image = cv2.imread(imagePath)
features = colorDesriptor.describe(image)
# write features to file
features = [str(feature).replace("\n", "") for feature in features]
output.write("%s,%s\n" % (imageName, ",".join(features)))
# close index file
output.close()
kps = 100
structureDescriptor = structure_descriptor.StructureDescriptor(kps)
output = open("structureindex.csv", "w") #arguments["structureindex"], "w")
for imagePath in glob.glob("D:/Photos/airplanes_side" + "/*.jpg"):
imageName = imagePath[imagePath.rfind("/") + 1:]
image = cv2.imread(imagePath)
structures = structureDescriptor.describe(image)
# write structures to file
structures = [str(structure).replace("\n", "") for structure in structures]
output.write("%s,%s\n" % (imageName, ",".join(structures)))
# close index file
output.close()
