def capture_samples(frame):
global tick
tick2 = cv2.getTickCount()
t = (tick2 - tick) / freq * 1000
if t > capture_threshold:
tick = tick2
capture(frame)
def main():
print('Creating video capture')
cap = cv2.VideoCapture(1)
while cap.isOpened():
timer = cv2.getTickCount()
have_frame, showFrame = cap.read()
if have_frame:
pipeline.process(showFrame)
extra_processing(showFrame)
cv2.putText(showFrame, "FPS : " + str(int(cv2.getTickFrequency() / (cv2.getTickCount() - timer))), (100, 50),
cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2)
cv2.imshow("match?", showFrame)
cv2.waitKey(1)
print('Capture closed')
def get(self):
current_tick = cv2.getTickCount()
different_time = (current_tick - self._start_tick) * self._freq
self._start_tick = current_tick
self._difftimes.append(different_time)
fps = 1000.0 / (sum(self._difftimes) / len(self._difftimes))
fps_rounded = round(fps, 2)
return fps_rounded
import numpy as np
def access_pixels(image):
print(image.shape)
height = image.shape[0]
width = image.shape[1]
channels = image.shape[2]
print("width:%s,height:%s,channels:%s" % (width, height, channels))
for row in range(height):
for col in range(width):
for c in range(channels):
pv = image[row, col, c]
image[row, col, c] = 255 - pv
cv.imshow("pixels_demo", image)
def creat_image():
img = np.zeros([400, 400, 3], np.uint8)
img[:, :, 0] = np.ones([400, 400]) * 255
cv.imshow("new image", img)
src = cv.imread("C:/Users/32936/Desktop/2/1.jpg")
t1 = cv.getTickCount()
creat_image()
t2 = cv.getTickCount()
print("time%s" % ((t2 - t1) / cv.getTickFrequency()))
cv.waitKey(0)
cv.destroyAllWindows()
def clock():
return cv.getTickCount() / cv.getTickFrequency()
def videoCap():
# pytesseract.pytesseract.tesseract_cmd = "C:/Program Files/Tesseract-OCR/tesseract"
cap = cv2.VideoCapture(
'C:/Users/jeawa/Desktop/project/LPR_Project/project/video/test_car.mp4'
)
count = 0
before_chars = ""
while True:
el = cv2.getTickCount()
fps = cap.get(5)
ret, img_ori = cap.read()
height, width, channel = img_ori.shape # 높이, 너비, 채널 확보
gray = cv2.cvtColor(img_ori, cv2.COLOR_BGR2GRAY)
# img_ori = cv2.imread('LPR_Project/project/image/3.jpg') # 이미지 불러오기
# gray = cv2.cvtColor(img_ori, cv2.COLOR_BGR2GRAY)
img_blurred = cv2.GaussianBlur(gray, ksize=(3, 3), sigmaX=0) # 노이즈 제거
img_thresh = cv2.adaptiveThreshold(
img_blurred,
maxValue=255.0,
adaptiveMethod=cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
thresholdType=cv2.THRESH_BINARY_INV,
blockSize=19, # 12 통과 15,20
C=9)
contours, _ = cv2.findContours( # 윤곽선을 찾기
img_thresh,
mode=cv2.RETR_LIST, #
method=cv2.CHAIN_APPROX_TC89_KCOS #
)
temp_result = np.zeros((height, width, channel), dtype=np.uint8)
cv2.drawContours(
temp_result, # 원본이미지
contours=contours, # contours 정보
contourIdx=-1, # -1 : 전체
color=(255, 255, 255),
thickness=1)
contours_dict = []
for contour in contours:
x, y, w, h = cv2.boundingRect(contour) # 컴투어를 감싸는 사각형
# cv2.rectangle(
# temp_result,
# pt1=(x, y),
# pt2=(x+w, y+h),
# color=(255, 255, 255),
# thickness=1
# )
contours_dict.append({
'contour': contour,
'x': x,
'y': y,
'w': w,
'h': h,
'cx': x + (w / 2), # 중심 좌표
'cy': y + (h / 2)
})
MIN_AREA, MAX_AREA = 100, 1000 # boundingRect(사각형)의 최소넓이
MIN_WIDTH, MIN_HEIGHT = 2, 8 # boundingRect의 최소 넓이, 높이 2, 8
MIN_RATIO, MAX_RATIO = 0.3, 0.9 # boundingRect의 가로 세로 비율
possible_contours = [] # 위의 조건의 만족하는 사각형
cnt = 0
for d in contours_dict:
area = d['w'] * d['h'] # 가로 * 세로 = 면적
ratio = d['w'] / d['h'] # 가로 / 세로 = 비율
if MIN_AREA < area < MAX_AREA and d['w'] > MIN_WIDTH and d[
'h'] > MIN_HEIGHT and MIN_RATIO < ratio < MAX_RATIO:
d['idx'] = cnt # 조건의 맞는 값을 idx에 저장한다.
cnt += 1
possible_contours.append(d) # possible_contour를 업데이트한다.
# temp_result = np.zeros((height, width, channel), dtype=np.uint8)
for d in possible_contours:
# cv2.drawContours(temp_result, d['contour'], -1, (255, 255, 255))
cv2.rectangle(temp_result,
pt1=(d['x'], d['y']),
pt2=(d['x'] + d['w'], d['y'] + d['h']),
color=(0, 0, 255),
thickness=1)
MAX_DIAG_MULTIPLYER = 4 # 대각선의 5배 안에 있어야함
MAX_ANGLE_DIFF = 12.0 # 12.0 #세타의 최대값 12, 0.7, 0.5, 0.6, 3
MAX_AREA_DIFF = 0.3 # 0.5 #면적의 차이
MAX_WIDTH_DIFF = 0.5 # 너비차이
MAX_HEIGHT_DIFF = 0.6 # 높이차이
MIN_N_MATCHED = 4 # 3 #위의 조건이 3개 미만이면 뺀다
def find_chars(contour_list): #
matched_result_idx = [] # idx 값 저장
for d1 in contour_list:
matched_contours_idx = []
for d2 in contour_list:
if d1['idx'] == d2['idx']: # d1 과 d2가 같으면 컨티뉴
continue
dx = abs(d1['cx'] - d2['cx'])
dy = abs(d1['cy'] - d2['cy'])
diagonal_length1 = np.sqrt(d1['w']**2 + d1['h']**2)
distance = np.linalg.norm(
np.array([d1['cx'], d1['cy']]) -
np.array([d2['cx'], d2['cy']])) # 대각선 길이
if dx == 0:
angle_diff = 90 # 0일 때 각도 90도 (예외처리)
else:
angle_diff = np.degrees(np.arctan(dy / dx)) # 세타 구하기
area_diff = abs(d1['w'] * d1['h'] - d2['w'] * d2['h']) / (
d1['w'] * d1['h']) # 면적 비율
width_diff = abs(d1['w'] - d2['w']) / d1['w'] # 너비비율
height_diff = abs(d1['h'] - d2['h']) / d1['h'] # 높이비율
# 조건들
if distance < diagonal_length1 * MAX_DIAG_MULTIPLYER \
and angle_diff < MAX_ANGLE_DIFF and area_diff < MAX_AREA_DIFF \
and width_diff < MAX_WIDTH_DIFF and height_diff < MAX_HEIGHT_DIFF:
# d2만 넣었기 때문에 마지막으로 d1을 넣은다
matched_contours_idx.append(d2['idx'])
# append this contour
matched_contours_idx.append(d1['idx'])
if len(matched_contours_idx) < MIN_N_MATCHED: # 3개 이하이면 번호판 x
continue
matched_result_idx.append(matched_contours_idx) # 최종 후보군
unmatched_contour_idx = [] # 최종후보군이 아닌 애들
for d4 in contour_list:
if d4['idx'] not in matched_contours_idx:
unmatched_contour_idx.append(d4['idx'])
unmatched_contour = np.take(possible_contours,
unmatched_contour_idx)
# recursive
recursive_contour_list = find_chars(unmatched_contour)
for idx in recursive_contour_list:
matched_result_idx.append(idx) # 번호판 이외의 값을 재정의
break
return matched_result_idx
result_idx = find_chars(possible_contours)
matched_result = []
for idx_list in result_idx:
matched_result.append(np.take(possible_contours, idx_list))
# visualize possible contours
temp_result = np.zeros((height, width, channel), dtype=np.uint8)
for r in matched_result:
for d in r:
cv2.rectangle(temp_result,
pt1=(d['x'], d['y']),
pt2=(d['x'] + d['w'], d['y'] + d['h']),
color=(0, 0, 255),
thickness=1)
PLATE_WIDTH_PADDING = 1.2 # 1.3
PLATE_HEIGHT_PADDING = 1.5 # 1.5
MIN_PLATE_RATIO = 3
MAX_PLATE_RATIO = 7 #10
plate_imgs = []
plate_infos = []
for i, matched_chars in enumerate(matched_result):
sorted_chars = sorted(matched_chars,
key=lambda x: x['cx']) # x방향으로 순차적으로 정렬
plate_cx = (sorted_chars[0]['cx'] +
sorted_chars[-1]['cx']) / 2 # 센터 좌표 구하기
plate_cy = (sorted_chars[0]['cy'] + sorted_chars[-1]['cy']) / 2
plate_width = (sorted_chars[-1]['x'] + sorted_chars[-1]['w'] -
sorted_chars[0]['x']) * PLATE_WIDTH_PADDING # 너비
# sum_height = 0
# for d in sorted_chars:
# sum_height += d['h']
# plate_height = int(sum_height / len(sorted_chars)
# * PLATE_HEIGHT_PADDING) # 높이
plate_height = (sorted_chars[-1]['y'] - sorted_chars[0]['y'] +
sorted_chars[-1]['h']) * PLATE_HEIGHT_PADDING
#
triangle_height = sorted_chars[-1]['cy'] - sorted_chars[0]['cy']
triangle_hypotenus = np.linalg.norm(
np.array([sorted_chars[0]['cx'], sorted_chars[0]['cy']]) -
np.array([sorted_chars[-1]['cx'], sorted_chars[-1]['cy']]))
angle = np.degrees(np.arcsin(triangle_height / triangle_hypotenus))
rotation_matrix = cv2.getRotationMatrix2D(center=(plate_cx,
plate_cy),
angle=angle,
scale=1.0)
# 회전
img_rotated = cv2.warpAffine(img_thresh,
M=rotation_matrix,
dsize=(width, height))
img_cropped = cv2.getRectSubPix(img_rotated,
patchSize=(int(plate_width),
int(plate_height)),
center=(int(plate_cx),
int(plate_cy)))
if img_cropped.shape[1] / img_cropped.shape[
0] < MIN_PLATE_RATIO or img_cropped.shape[
1] / img_cropped.shape[
0] < MIN_PLATE_RATIO > MAX_PLATE_RATIO:
continue
plate_imgs.append(img_cropped)
plate_infos.append({
'x': int(plate_cx - plate_width / 2),
'y': int(plate_cy - plate_height / 2),
'w': int(plate_width),
'h': int(plate_height)
})
for i, plate_img in enumerate(plate_imgs):
plate_img = cv2.resize(plate_img, dsize=(0, 0), fx=1.6, fy=1.6)
_, plate_img = cv2.threshold(plate_img,
thresh=0.0,
maxval=255.0,
type=cv2.THRESH_BINARY
| cv2.THRESH_OTSU)
# plt.imshow(plate_img, cmap='gray')
# plt.show()
# find contours again (same as above)
contours, _ = cv2.findContours(plate_img,
mode=cv2.RETR_LIST,
method=cv2.CHAIN_APPROX_SIMPLE)
plate_min_x, plate_min_y = plate_img.shape[1], plate_img.shape[
0]
plate_max_x, plate_max_y = 0, 0
for contour in contours:
x, y, w, h = cv2.boundingRect(contour)
area = w * h
ratio = w / h
if area > MIN_AREA \
and w > MIN_WIDTH and h > MIN_HEIGHT \
and MIN_RATIO < ratio < MAX_RATIO:
if x < plate_min_x:
plate_min_x = x
if y < plate_min_y:
plate_min_y = y
if x + w > plate_max_x:
plate_max_x = x + w
if y + h > plate_max_y:
plate_max_y = y + h
img_result = plate_img[plate_min_y:plate_max_y,
plate_min_x:plate_max_x]
img_result = cv2.GaussianBlur(img_result, ksize=(3, 3), sigmaX=0)
_, img_result = cv2.threshold(img_result,
thresh=0.0,
maxval=255.0,
type=cv2.THRESH_BINARY
| cv2.THRESH_OTSU)
img_result = cv2.copyMakeBorder(img_result,
top=10,
bottom=10,
left=20,
right=10,
borderType=cv2.BORDER_CONSTANT,
value=(0, 0, 0))
chars = pytesseract.image_to_string(img_result,
lang='kor',
config='--psm 7 --oem 0')
result_chars = ''
has_digit = False
# <= ord('힣')
for c in chars:
if \
+ ord('가') == ord(c) or \
+ ord('나') == ord(c) or \
+ ord('다') == ord(c) or \
+ ord('라') == ord(c) or \
+ ord('마') == ord(c) or \
+ ord('거') == ord(c) or \
+ ord('너') == ord(c) or \
+ ord('더') == ord(c) or \
+ ord('러') == ord(c) or \
+ ord('머') == ord(c) or \
+ ord('버') == ord(c) or \
+ ord('서') == ord(c) or \
+ ord('어') == ord(c) or \
+ ord('저') == ord(c) or \
+ ord('고') == ord(c) or \
+ ord('노') == ord(c) or \
+ ord('도') == ord(c) or \
+ ord('로') == ord(c) or \
+ ord('모') == ord(c) or \
+ ord('보') == ord(c) or \
+ ord('소') == ord(c) or \
+ ord('오') == ord(c) or \
+ ord('조') == ord(c) or \
+ ord('구') == ord(c) or \
+ ord('누') == ord(c) or \
+ ord('두') == ord(c) or \
+ ord('루') == ord(c) or \
+ ord('무') == ord(c) or \
+ ord('부') == ord(c) or \
+ ord('수') == ord(c) or \
+ ord('우') == ord(c) or \
+ ord('주') == ord(c) or \
+ ord('아') == ord(c) or \
+ ord('바') == ord(c) or \
+ ord('사') == ord(c) or \
+ ord('자') == ord(c) or \
+ ord('배') == ord(c) or \
+ ord('하') == ord(c) or \
+ ord('허') == ord(c) or \
+ ord('호') == ord(c) or c.isdigit():
if c.isdigit():
has_digit = True
result_chars += c
# print(result_chars)
# print(len(result_chars))
if result_chars == "":
pass
else:
if before_chars == result_chars and 6 < len(result_chars) < 9:
count += 1
else:
before_chars = result_chars
count = 0
if count == 1:
print(result_chars)
count = 0
before_chars = ""
# print(fps + " : 프레임 영상입니다.")
print(fps)
a = np.hstack((img_ori, temp_result))
cv2.imshow("Go", a)
if cv2.waitKey(42) == ord('q'):
break
e2 = cv2.getTickCount()
time = (e2 - el) / cv2.getTickFrequency()
print(time)
# print("1프레임 : 1초당 처리속도는 " + time + "입니다.")
cap.release()
cv2.destroyAllWindows()
def main():
global drone
prediction_class = ObjectDetectionPredict(model_name=MODEL_NAME)
try:
drone.subscribe(drone.EVENT_FLIGHT_DATA, handler)
drone.connect()
drone.wait_for_connection(60.0)
container = av.open(drone.get_video_stream())
# skip first 300 frames
frame_skip = 1000
bbox = (287, 23, 86, 320)
c = 0
# drone.takeoff()
# sleep(5)
th = threading.Thread(target=droneControl)
th.start()
landflag = False
while True:
print("******************\nNEW WHILE\n******************** ")
for frame in container.decode(video=0):
if 0 < frame_skip:
frame_skip = frame_skip - 1
continue
if c < 2:
c += 1
continue
else:
c = 0
pil_im = Image.fromarray(np.array(frame.to_image()))
print("pil_im :", pil_im)
timer = cv2.getTickCount()
scores, classes, img, boxes = prediction_class.predict_single_image(
pil_im)
opencvImage = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
# Calculate Frames per second (FPS)
fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer)
print('FPS : ' + str(float(fps)))
cv2.putText(opencvImage, "FPS : " + str(int(fps)), (50, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2)
cv2.imshow('opencvImage', opencvImage)
if cv2.waitKey(1) & 0xFF == ord('q'):
landflag = True
cv2.destroyAllWindows()
break
if landflag:
print('down')
drone.down(50)
sleep(3)
drone.land()
sleep(1)
break
# prediction_class.sess.close()
print('down again')
drone.land()
sleep(1)
except Exception as ex:
exc_type, exc_value, exc_traceback = sys.exc_info()
traceback.print_exception(exc_type, exc_value, exc_traceback)
print(ex)
finally:
drone.quit()
cv2.destroyAllWindows()
# Initialize video stream
videostream = VideoStream(resolution=(imW, imH)).start()
time.sleep(1)
def get_color(name):
if name == 'car':
return 10, 255, 0
elif name == 'person':
return 0, 10, 255
elif name == 'cycle':
return 255, 10, 0
return 0, 127, 255
tick = cv2.getTickCount()
out_file = open("out/%s.csv" % datetime.now().strftime('%Y%m%d-%H%M%S'), 'a')
def capture_samples(frame):
global tick
tick2 = cv2.getTickCount()
t = (tick2 - tick) / freq * 1000
if t > capture_threshold:
tick = tick2
capture(frame)
def capture(frame):
filename = "samples/frame-%s.jpg" % datetime.now().strftime('%Y%m%d-%H%M%S')
from cv2 import cv2
import numpy as np
'''
#模板
e1 = cv2.getTickCount()
# 你的代码
e2 = cv2.getTickCount()
time = (e2 - e1)/ cv2.getTickFrequency()
print(time)
'''
img1 = cv2.imread('img/bird.jpg')
e1 = cv2.getTickCount()
for i in range(5, 49, 2):
img1 = cv2.medianBlur(img1, i)
e2 = cv2.getTickCount()
t = (e2 - e1) / cv2.getTickFrequency()
print(t)
def __init__(self, buffer_len=1):
self._start_tick = cv2.getTickCount()
self._freq = 1000.0 / cv2.getTickFrequency()
# deque = collections.deque
self._difftimes = deque(maxlen=buffer_len)
bbox = (287, 23, 86, 320)
# Uncomment the line below to select a different bounding box
bbox = cv2.selectROI(frame, False)
# Initialize tracker with first frame and bounding box
ok = tracker.init(frame, bbox)
while True:
# Read a new frame
ok, frame = video.read()
if not ok:
break
# Start timer
timer = cv2.getTickCount()
# Update tracker
ok, bbox = tracker.update(frame)
# Calculate Frames per second (FPS)
fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer)
# Draw bounding box
if ok:
# Tracking success
p1 = (int(bbox[0]), int(bbox[1]))
p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))
cv2.rectangle(frame, p1, p2, (255, 0, 0), 2, 1)
else:
# Tracking failure
from cv2 import cv2 as cv
import numpy as np
import time
img = cv.imread("star.jpg")
e1 = cv.getTickCount() # 也可用python的time模块计数
# 估量代码执行效率
for i in range(5, 49, 2):
img = cv.medianBlur(img, i)
e2 = cv.getTickCount()
time = (e2 - e1) / cv.getTickFrequency()
print(time)
# 查看是否开启优化
ret = cv.useOptimized()
print(ret)
# 魔法命令%time（使用ipython）