def search():
# Dữ liệu đầu vào
file = request.files['file']
file.save('./' + file.filename)
features = mongo.db.features
# Đọc ảnh input
image = cv2.imread('./' + file.filename)
color_feature = Descriptor.color_history(image, 8)
lpb_feature = Descriptor.lpb_history(image, 24, 8)
hog_feature = Descriptor.hog(image, 10, 2, 9)
feature = np.hstack([color_feature, lpb_feature, hog_feature])
db_features = features.find()
results_euclidean = {}
results_cosine = {}
for db_feature in db_features:
dist_euclidean = euclidean(np.asarray(db_feature['feature']), feature)
results_euclidean[db_feature['path']] = dist_euclidean
dist_cosine = cosine(np.asarray(db_feature['feature']), feature)
results_cosine[db_feature['path']] = dist_cosine
results_euclidean = sorted([(d, n) for n, d in results_euclidean.items()])
results_cosine = sorted([(d, n) for n, d in results_cosine.items()])
return jsonify({
'result_euclidean': results_euclidean,
'result_cosine': results_cosine
})
def process():
dirs = os.listdir(path)
features = mongo.db.features
for file in dirs:
# đọc từng file
image = cv2.imread(path + '/' + file)
# Vẽ sơ đồ histogram của 3 đặc trưng :
# đặc trưng màu sắc,
color_feature = Descriptor.color_history(image, 8)
# đặc trưng kết cấu ảnh(lpb),
lpb_feature = Descriptor.lpb_history(image, 24, 8)
# đặc trưng HOG
hog_feature = Descriptor.hog(image, 10, 2, 9)
feature = list(np.hstack([color_feature, lpb_feature, hog_feature]))
id = features.insert_one({
'path': path + '/' + file,
'feature': feature
})
return 'done'
