step = 0
for imagePath in glob.glob(datasetDir + "/*.*g"):
photoName = imagePath[imagePath.rfind("/") + 1:]
image = cv2.imread(imagePath)
size = image.shape[:2]
coefficient = size[0] / size[1]
# Resize image
width = INIT_SIZE
height = int(INIT_SIZE * coefficient)
image = cv2.resize(image, (width, height))
# Crop center
image = image[height // 8:(7 * height) // 8, width // 8:(7 * width) // 8]
# image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
features = desc.describe(image)
index[photoName] = features
if step % 20 == 0:
print("progress: %d" % step)
step += 1
f = open(args["index"], "wb")
f.write(pickle.dumps(index))
f.close()
print("Done. Indexed %d images" % (len(index)))
list_images = glob.glob("../data/flowers/*.jpg")
# initialize our image descriptor -- a 3D RGB histogram with
# 8 bins per channel
index = {}
i = 0
# use list_images to grab the image paths and loop over them
for imagePath in list_images:
i+=1
# extract our unique image ID (i.e. the filename)
k = imagePath[imagePath.rfind("/") + 1:]
# load the image, describe it using our RGB histogram
# descriptor, and update the index
img = cv2.imread(imagePath)
features = desc.describe(img)
index[k] = [round(digit) for digit in features * 10e2]
# index[k] = features
# print(k, ":", index[k])
# if (i >= 10):
# break
# tao 1 feature-values dua vao descList
desc_list = {}
# for i, feature in enumerate(index.values()): #load het cac features cua tat ca anh
# for fi,value in enumerate(feature): # voi moi anh, duyet het 512 features,, tao inverted index
# tao 1 dict
# check xem fi (feature_value Fi) co ton tai, if not init, if yes add
# sub_desc == Vij cua Features_value, sub_desc[gia tri tai Fi] = index cua tam anh
from RGBHistogram import RGBHistogram
from ZernikeMoments import ZernikeMoments
import cv2
from matplotlib import pyplot as plt
import glob
import pickle
from Searcher import Searcher
# desc = RGBHistogram([8 8, 8])
desc = RGBHistogram([4, 4, 4])
test_image_path = '../data/flowers/image_0030.jpg'
query_image = cv2.imread(test_image_path)
query_feature = desc.describe(query_image)
# print(query_feature)
# quit()
# load the index and initialize our searcher
index = pickle.load(open("Histogram_only_index_4bins.cpickle", "rb"))
print(index.items())
searcher = Searcher(index)
results = searcher.search(query_feature)
print(results)
for i in range(0, 5):
ret_path = '../data/' + results[i][1].replace('\\','/')
img = cv2.imread(ret_path)
cv2.imshow(str(i), img)
cv2.waitKey()
exit()
imagePaths = sorted(glob.glob("dataset/images/*.png"))
maskPaths = sorted(glob.glob("dataset/masks/*.png"))
imagePath = "TestImg/picTac.png"
data = []
target = []
desc = RGBHistogram([8, 8, 8])
counter = 0
for imagePath, maskPath in zip(imagePaths, maskPaths):
image = cv2.imread(imagePath)
mask = cv2.imread(maskPath)
mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
features = desc.describe(image, mask)
data.append(features)
target.append(imagePath.split('_')[-2])
#print target, '\n'
targetNames = np.unique(target)
le = LabelEncoder()
target = le.fit_transform(target)
(trainData, testData, trainTarget, testTarget) = train_test_split(data, target,
test_size = 0.3, random_state = 42)
model = RandomForestClassifier(n_estimators=25, random_state=84)
model.fit(trainData, trainTarget)
