def ranking_with_classifier(train_corpus,
test_corpus,
train_rels,
topics,
p,
ix,
alpha1=0.5,
alpha2=0.5):
lst = []
for topic_id in topics:
results = ranking(topic_id, p, ix, "TF-IDF")
results = [(el[0], el[1] / results[0][1]) for el in results]
new_corpus = []
for id1 in (el[0] for el in results):
for el in test_corpus:
if el[0] == id1:
new_corpus.append((id1, el[1]))
topic = process_topic(topic_id, topic_directory)
model = training(topic,
train_corpus,
train_rels,
model=KNeighborsClassifier(n_neighbors=25,
metric="euclidean"))
classes = [
classify(new_corpus[i][1], topic, model)
for i in range(len(new_corpus))
]
results = [(el[0], el[1] * alpha1 + classes[i] * alpha2)
for i, el in enumerate(results)]
results.sort(reverse=True, key=lambda x: x[1])
lst.append(results)
return lst
def main(args):
#Clean previous image
clean_images()
#Training phase
model = training()
vidcap = cv2.VideoCapture(args.file_name)
fps = vidcap.get(cv2.CAP_PROP_FPS)
width = vidcap.get(3) # float
height = vidcap.get(4) # float
# Define the codec and create VideoWriter object
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('output.avi', fourcc, fps, (640, 480))
# initialize the termination criteria for cam shift, indicating
# a maximum of ten iterations or movement by a least one pixel
# along with the bounding box of the ROI
termination = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1)
roiBox = None
roiHist = None
success = True
similitary_contour_with_circle = 0.65 # parameter
count = 0
current_sign = None
current_text = ""
current_size = 0
sign_count = 0
coordinates = []
position = []
file = open("Output.txt", "w")
while True:
success, frame = vidcap.read()
if not success:
print("FINISHED")
break
width = frame.shape[1]
height = frame.shape[0]
#frame = cv2.resize(frame, (640,int(height/(width/640))))
frame = cv2.resize(frame, (640, 480))
print("Frame:{}".format(count))
#image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
coordinate, image, sign_type, text = localization(
frame, args.min_size_components,
args.similitary_contour_with_circle, model, count, current_sign)
if coordinate is not None:
cv2.rectangle(image, coordinate[0], coordinate[1], (255, 255, 255),
1)
print("Sign:{}".format(sign_type))
if sign_type > 0 and (not current_sign or sign_type != current_sign):
current_sign = sign_type
current_text = text
top = int(coordinate[0][1] * 1.05)
left = int(coordinate[0][0] * 1.05)
bottom = int(coordinate[1][1] * 0.95)
right = int(coordinate[1][0] * 0.95)
position = [
count, sign_type if sign_type <= 8 else 8, coordinate[0][0],
coordinate[0][1], coordinate[1][0], coordinate[1][1]
]
cv2.rectangle(image, coordinate[0], coordinate[1], (0, 255, 0), 1)
font = cv2.FONT_HERSHEY_PLAIN
cv2.putText(image, text, (coordinate[0][0], coordinate[0][1] - 15),
font, 1, (0, 0, 255), 2, cv2.LINE_4)
tl = [left, top]
br = [right, bottom]
print(tl, br)
current_size = math.sqrt(
math.pow((tl[0] - br[0]), 2) + math.pow((tl[1] - br[1]), 2))
# grab the ROI for the bounding box and convert it
# to the HSV color space
roi = frame[tl[1]:br[1], tl[0]:br[0]]
roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
#roi = cv2.cvtColor(roi, cv2.COLOR_BGR2LAB)
# compute a HSV histogram for the ROI and store the
# bounding box
roiHist = cv2.calcHist([roi], [0], None, [16], [0, 180])
roiHist = cv2.normalize(roiHist, roiHist, 0, 255, cv2.NORM_MINMAX)
roiBox = (tl[0], tl[1], br[0], br[1])
elif current_sign:
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
backProj = cv2.calcBackProject([hsv], [0], roiHist, [0, 180], 1)
# apply cam shift to the back projection, convert the
# points to a bounding box, and then draw them
(r, roiBox) = cv2.CamShift(backProj, roiBox, termination)
pts = np.int0(cv2.boxPoints(r))
s = pts.sum(axis=1)
tl = pts[np.argmin(s)]
br = pts[np.argmax(s)]
size = math.sqrt(pow((tl[0] - br[0]), 2) + pow((tl[1] - br[1]), 2))
print(size)
if current_size < 1 or size < 1 or size / current_size > 30 or math.fabs(
(tl[0] - br[0]) / (tl[1] - br[1])) > 2 or math.fabs(
(tl[0] - br[0]) / (tl[1] - br[1])) < 0.5:
current_sign = None
print("Stop tracking")
else:
current_size = size
if sign_type > 0:
top = int(coordinate[0][1])
left = int(coordinate[0][0])
bottom = int(coordinate[1][1])
right = int(coordinate[1][0])
position = [
count, sign_type if sign_type <= 8 else 8, left, top,
right, bottom
]
cv2.rectangle(image, coordinate[0], coordinate[1], (0, 255, 0),
1)
font = cv2.FONT_HERSHEY_PLAIN
cv2.putText(image, text,
(coordinate[0][0], coordinate[0][1] - 15), font, 1,
(0, 0, 255), 2, cv2.LINE_4)
elif current_sign:
position = [
count, sign_type if sign_type <= 8 else 8, tl[0], tl[1],
br[0], br[1]
]
cv2.rectangle(image, (tl[0], tl[1]), (br[0], br[1]),
(0, 255, 0), 1)
font = cv2.FONT_HERSHEY_PLAIN
cv2.putText(image, current_text, (tl[0], tl[1] - 15), font, 1,
(0, 0, 255), 2, cv2.LINE_4)
if current_sign:
sign_count += 1
coordinates.append(position)
cv2.imshow('Result', image)
count = count + 1
#Write to video
out.write(image)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
file.write("{}".format(sign_count))
for pos in coordinates:
file.write("\n{} {} {} {} {} {}".format(pos[0], pos[1], pos[2], pos[3],
pos[4], pos[5]))
print("Finish {} frames".format(count))
file.close()
return
# from sign_detection import extract_roi
from detect_circles import extract_roi
from classification import training, getLabel, SVM
import cv2
import numpy as np
textLables = ["not defined", "50", "80", "70", "15", "5", "60", "40", "30"]
# video_cap = cv2.VideoCapture(0)
video_cap = cv2.VideoCapture("jarab.avi")
model = training()
# model = SVM()
# model.load('data_svm.dat')
while True:
if not video_cap.isOpened():
print("error: video capture is not opened")
break
ret, frame = video_cap.read()
if not ret:
print("error: failed to read frame")
break
frame = cv2.resize(frame, (640, 480))
ret = extract_roi(frame)
if ret is None:
