import glob
import os
import pickle
from PIL import Image
from extractor import FeatureExtractor
fe = FeatureExtractor()
for img_path in sorted(glob.glob('static/animal/*.png')):
print(img_path)
img = Image.open(img_path) # PIL image
feature = fe.extract(img)
feature_path = 'static/feature/' + os.path.splitext(
os.path.basename(img_path))[0] + '.pkl'
pickle.dump(feature,
open(feature_path,
'wb')) #array di save ke folder feature dg format .pkl
class Frame:
def __init__(self, img):
self.feature_extractor = FeatureExtractor()
self.display = Display()
self.frames = []
self.kpus = None
self.des = None
self.pts = None
if img is not None:
self.kpus, self.des = self.feature_extractor.extract(img)
self.pts = [None] * len(self.kpus)
@staticmethod
def match_frames(f1, f2):
# Matching previous frame and current frame (used in triangulation
bf = cv2.BFMatcher(cv2.NORM_HAMMING)
try:
matches = bf.knnMatch(f1.des, f2.des, k=2)
except cv2.error:
return False
good_matches = []
idx_of_des_from_f1, idx_of_des_from_f2 = [], []
idx_set1, idx_set2 = set(), set()
# using Lowe's ratio to filter out bad matches
for m, n in matches:
if m.distance < 0.75 * n.distance:
p1 = f1.kpus[m.queryIdx]
p2 = f2.kpus[m.trainIdx]
# ensure distance is within 32
if m.distance < 32:
if m.queryIdx not in idx_set1 and m.trainIdx not in idx_set2:
idx_of_des_from_f1.append(m.queryIdx)
idx_of_des_from_f2.append(m.trainIdx)
idx_set1.add(m.queryIdx)
idx_set2.add(m.trainIdx)
good_matches.append((p1, p2))
good_matches = np.array(good_matches)
idx_of_des_from_f1 = np.array(idx_of_des_from_f1)
idx_of_des_from_f2 = np.array(idx_of_des_from_f2)
# # fit matrix
# model, inliers = ransac((good_matches[:, 0], good_matches[:, 1]), EssentialMatrixTransform, min_samples=8,
# residual_threshold=0.02, max_trials=100)
#
# print(model)
# return idx_of_des_from_f1[inliers], idx_of_des_from_f2[inliers], fundamentalToRt(model.params)
return good_matches, idx_of_des_from_f1, idx_of_des_from_f2
def process_frame(self, img):
img = cv2.resize(img, (self.display.W, self.display.H))
# takes image from cap and turns it into a frame & gets kps/des
frame = Frame(img)
self.frames.append(frame)
if len(self.frames) <= 1:
return
f1 = self.frames[-1]
f2 = self.frames[-2]
# implement some matching frame function that takes the last two frames from self.frames
good_matches, idx1, idx2 = self.match_frames(f1, f2)
proj_points_1 = np.array([
np.array([kp.pt[0] for kp in good_matches[:, 0]]),
np.array([kp.pt[1] for kp in good_matches[:, 0]])
])
proj_points_2 = np.array([
np.array([kp.pt[0] for kp in good_matches[:, 1]]),
np.array([kp.pt[1] for kp in good_matches[:, 1]])
])
# figure out how to get camera matrix 2 (Rt)
points_in_3d = cv2.triangulatePoints(IRt, Rt, proj_points_1,
proj_points_2)
print(points_in_3d)
# for 2D display
kps_frame = self.display.process_kps_to_frame(img, frame.kpus)
self.display.show(img)
self.display.show(kps_frame)
return audioFiles
if __name__ == '__main__':
source_dir = sys.argv[1]
output_dir = "features"
fe = FeatureExtractor("gen")
audioFiles = getAll_audio(source_dir)
print "started feature extraction..."
for af in audioFiles:
filename = os.path.basename(af).split(".")[0] + ".json"
oFile = os.path.join(output_dir, filename)
fe.extract(af, oFile)
print "feature extraction complete"
print "features stored in %s directory" % output_dir
f = Feeder()
dir_path = output_dir
# index creation
print "started index creation..."
f.feedAll(dir_path, iname="audio_index", doc_type="audio")
print "index creation complete"
client = PeragroClient()
client.set_index('audio_index')