def main():
capture = cv.VideoCapture(0)
assert capture.isOpened(), "Cannot connect to camera"
capture.set(cv.CAP_PROP_FPS, 5)
capture.set(cv.CAP_PROP_FRAME_WIDTH, 640)
capture.set(cv.CAP_PROP_FRAME_HEIGHT, 480)
matching = FeatureMatching(train_image='train.png')
for success, frame in iter(capture.read, (False, None)):
cv.imshow("frame", frame)
match_succsess, img_warped, img_flann = matching.match(frame)
if match_succsess:
cv.imshow("res", img_warped)
cv.imshow("flann", img_flann)
if cv.waitKey(1) & 0xff == 27:
break
class FeatureMatchingLayout(BaseLayout):
"""A custom layout for feature matching display
A plain GUI layout for feature matching output.
Each captured frame is passed to the FeatureMatching class, so that an
object of interest can be tracked.
"""
def _init_custom_layout(self):
"""Initializes feature matching class"""
self.matching = FeatureMatching(train_image='salinger.jpg')
def _create_custom_layout(self):
"""Use plain layout"""
pass
def _process_frame(self, frame):
"""Processes each captured frame"""
# if object detected, display new frame, else old one
success, new_frame = self.matching.match(frame)
if success:
return new_frame
else:
return frame
class FeatureMatchingLayout(BaseLayout):
"""A custom layout for feature matching display
A plain GUI layout for feature matching output.
Each captured frame is passed to the FeatureMatching class, so that an
object of interest can be tracked.
"""
def _init_custom_layout(self):
"""Initializes feature matching class"""
self.matching = FeatureMatching(train_image='salinger.jpg')
def _create_custom_layout(self):
"""Use plain layout"""
pass
def _process_frame(self, frame):
"""Processes each captured frame"""
# if object detected, display new frame, else old one
success, new_frame = self.matching.match(frame)
if success:
return new_frame
else:
return frame
def testing():
target = os.path.join(APP_ROOT, 'images/')
print(target)
if not os.path.isdir(target):
os.mkdir(target)
else:
print("Couldn't create upload directory: {}".format(target))
print(request.files.getlist("file"))
for upload in request.files.getlist("file"):
print(upload)
print("{} is the file name".format(upload.filename))
filename = upload.filename
destination = "/".join([target, filename])
print("Accept incoming file:", filename)
print("Save it to:", destination)
upload.save(destination)
start = datetime.now()
start_time_data = time.strftime("%Y/%m/%d %H:%M:%S")
# image_path = 'images/taxi2.jpg'
image_path = 'images/' + filename
img = cv2.imread(image_path)
# try:
# for orientation in ExifTags.TAGS.keys():
# if ExifTags.TAGS[orientation] == 'Orientation':
# break
# exif = dict(img._getexif().items())
#
# if exif[orientation] == 3:
# img = img.rotate(180, expand=True)
# elif exif[orientation] == 6:
# img = img.rotate(270, expand=True)
# elif exif[orientation] == 8:
# img = img.rotate(90, expand=True)
#
# except (AttributeError, KeyError, IndexError):
# # cases: image don't have getexif
# pass
single_template = cv2.imread('static/img/template-img/single.png')
double_template = cv2.imread('static/img/template-img/double.png')
feature_template = cv2.imread('static/img/template-img/feature.png')
is_ev_car = 0
is_general_car = 0
cropped_plate_img = None
valid_crop = 1
with open(image_path, 'rb') as binary_image:
cropped_plate_img = func.crop_plate_img(binary_image.read(), img)
if cropped_plate_img is False:
valid_crop = 0
print('Error while cropping plate img', valid_crop)
return render_template("complete_test.html", status=valid_crop)
#gray_plate_img = cv2.cvtColor(cropped_plate_img, cv2.COLOR_BGR2GRAY)
# Template Matching plate with one small evcar shape image
single_template_matching = TemplateMatching(cropped_plate_img,
single_template)
single_template_matching.find_position()
# Template Matching plate with special EV mark from left part of plate
double_template_matching = TemplateMatching(cropped_plate_img,
double_template)
double_template_matching.find_position()
# check position of 2 logos.
if func.check_position_of_logos(single_template_matching,
double_template_matching) is False:
is_general_car += 1
print('no EV car (logos not matching)')
else:
is_ev_car += 1
print('EV car (logos matched)')
# draw rectangle in Original image
first_img = single_template_matching.draw_rectangle()
second_img = double_template_matching.draw_rectangle(first_img)
# Feature matching by 3 logos template.
feature_template_matching = FeatureMatching(cropped_plate_img,
feature_template)
# if there is no points of feature matching.plus 1 point(is_general_car)
if feature_template_matching.find_points() < 1:
is_general_car += 1
print('no EV car (no feature matching detected)')
else:
is_ev_car += 1 # if there are some points, plus 1 point
print('EV car (feature matched) : {}'.format(
feature_template_matching.find_points()))
# Detect which color of plate
# if it is True : is_ev_car, else is_general_car
color_of_plate = func.detect_plate_color(cropped_plate_img)
if color_of_plate is not True:
is_general_car += 1
else:
is_ev_car += 1
print("is_general_vehicle_points : {}\nis_eletronic_vehicle_points : {}".
format(is_general_car, is_ev_car))
if is_ev_car >= 2:
car_type = 'Electronic Vehicle'
feature_point_img = feature_template_matching.draw_feature_points()
# Set image as max width and same height
combined_h = second_img.shape[0]
combined_w = max(second_img.shape[1], feature_point_img.shape[1])
new_size = (combined_w, combined_h)
# Draw feature matching points on original image
second_img = cv2.resize(second_img, new_size)
feature_point_img = cv2.resize(feature_point_img, new_size)
# Rectangle template image + feature matching image as vertical way
result_plate_img = cv2.vconcat([second_img, feature_point_img])
else:
car_type = 'General Vehicle'
result_plate_img = cropped_plate_img
cv2.imwrite('static/results/saved.jpg', result_plate_img)
processing = datetime.now() - start
end_time_data = time.strftime("%Y/%m/%d %H:%M:%S")
print(processing)
return render_template("complete_test.html",
status=valid_crop,
image_name=filename,
car=car_type,
processing=processing,
result_img=result_plate_img,
start_time_data=start_time_data,
end_time_data=end_time_data)
import cv2
from feature_matching import FeatureMatching
#camera's image
img_train = cv2.imread('./2.jpg')
#goal image
query_image = cv2.imread('./1.jpg')
matching = FeatureMatching(query_image='./1.jpg')
m, flag = matching.match(img_train, query_image)
if (m == False):
print("not match!!!")
def _init_custom_layout(self):
"""Initializes feature matching class"""
self.matching = FeatureMatching(train_image='salinger.jpg')
# -*- coding:utf-8 -*-
__author__ = 'Microcosm'
import cv2
from feature_matching import FeatureMatching
img_train = cv2.imread('train.png')
matching = FeatureMatching(query_image='query.png')
flag = matching.match(img_train)
def _init_custom_layout(self):
"""Initializes feature matching class"""
self.matching = FeatureMatching(train_image="salinger.jpg")
real = 13
# write the frame
# timestr = time.strftime("%Y_%m%d-%H_%M_%S")
# ImageName = "image_" + timestr + "__" + str(times + 1) + ".jpg"
# camera's image
# img_train = cv2.imread('./' + ImageName)
# goal image
ImageName = 'picture' + str(times + 1) + '.jpg'
cv2.imwrite(ImageName, frame)
print("\n save picture" + str(times + 1) + "...\n")
img_train = cv2.imread('picture1.jpg')
print('picture' + str(times + 1) + '.jpg' +
" compare to Goal_1.jpg")
query_image = cv2.imread('./Goal_1.jpg')
matching = FeatureMatching(query_image='./Goal_1.jpg')
elif (GoalNum == 2):
width = 7.1
real = 51.4
img_train = cv2.imread('picture' + str(times + 1) + '.jpg')
print('picture' + str(times + 1) + '.jpg' +
" compare to Goal_2.jpg")
query_image = cv2.imread('./Goal_2.jpg')
matching = FeatureMatching(query_image='./Goal_2.jpg')
elif (GoalNum == 3):
width = 7.1
real = 51.4
img_train = cv2.imread('picture' + str(times + 1) + '.jpg')
print('picture' + str(times + 1) + '.jpg' +
" compare to Goal_3.jpg")
query_image = cv2.imread('./Goal_3.jpg')
import cv2
from feature_matching import FeatureMatching
img_train = cv2.imread('001.jpg')
matching = FeatureMatching(query_image='121.jpg')
flag = matching.match(img_train)
if ret == True:
# write the frame
timestr = time.strftime("%Y_%m%d-%H_%M_%S")
ImageName = "image_" + timestr + "__" + str(times + 1) + ".jpg"
cv2.imwrite(ImageName, frame)
print("\n save picture" + str(times + 1) + "...\n")
f.write("\n save picture" + str(times + 1) + "...\n")
# camera's image
img_train = cv2.imread('./' + ImageName)
# goal image
if (correct < 3):
print(ImageName + " compare to Goal_1.jpg")
f.write("\n " + ImageName + " compare to Goal_1.jpg")
query_image = cv2.imread('./Goal_1.jpg')
matching = FeatureMatching(query_image='./Goal_1.jpg')
else:
print(ImageName + " compare to Goal_2.jpg")
f.write("\n " + ImageName + " compare to Goal_2.jpg")
query_image = cv2.imread('./Goal_2.jpg')
matching = FeatureMatching(query_image='./Goal_2.jpg')
m, x, y = matching.match(img_train, query_image)
break
except:
print(
"This picture cannot be taken any special point, and will be tried again.\n"
)
f.write(
"\n This picture cannot be taken any special point, and will be tried again.\n"
)
break
# Capture frame-by-frame
ret, frame = VideoStream.read()
if ret == True:
# Display the resulting frame
cv2.imshow("camera's streaming video", frame)
# write the frame
timestr = time.strftime("%Y_%m%d-%H_%M_%S")
ImageName = "image_" + timestr + "__" + str(times + 1) + ".jpg"
cv2.imwrite(ImageName, frame)
print("\n save picture" + str(times + 1) + "...\n")
times += 1
# camera's image
img_train = cv2.imread('./' + ImageName)
# goal image
query_image = cv2.imread('./Goal.jpg')
matching = FeatureMatching(query_image='./Goal.jpg')
m = matching.match(img_train, query_image)
if (m == False):
print("not match!!!\n")
else:
break
# When everything done, release the capture
VideoStream.release()
cv2.destroyAllWindows()