def start_cameras():
left_camera = CSI_Camera()
left_camera.create_gstreamer_pipeline(
sensor_id=0,
sensor_mode=SENSOR_MODE_720,
framerate=30,
flip_method=0,
display_height=DISPLAY_HEIGHT,
display_width=DISPLAY_WIDTH,
)
left_camera.open(left_camera.gstreamer_pipeline)
left_camera.start()
right_camera = CSI_Camera()
right_camera.create_gstreamer_pipeline(
sensor_id=1,
sensor_mode=SENSOR_MODE_720,
framerate=30,
flip_method=0,
display_height=DISPLAY_HEIGHT,
display_width=DISPLAY_WIDTH,
)
right_camera.open(right_camera.gstreamer_pipeline)
right_camera.start()
cv2.namedWindow("CSI Cameras", cv2.WINDOW_AUTOSIZE)
if (not left_camera.video_capture.isOpened()
or not right_camera.video_capture.isOpened()):
# Cameras did not open, or no camera attached
print("Unable to open any cameras")
# TODO: Proper Cleanup
SystemExit(0)
try:
# Start counting the number of frames read and displayed
left_camera.start_counting_fps()
right_camera.start_counting_fps()
while cv2.getWindowProperty("CSI Cameras", 0) >= 0:
left_image = read_camera(left_camera, show_fps)
right_image = read_camera(right_camera, show_fps)
# We place both images side by side to show in the window
camera_images = np.hstack((left_image, right_image))
cv2.imshow("CSI Cameras", camera_images)
left_camera.frames_displayed += 1
right_camera.frames_displayed += 1
# This also acts as a frame limiter
# Stop the program on the ESC key
if (cv2.waitKey(20) & 0xFF) == 27:
break
finally:
left_camera.stop()
left_camera.release()
right_camera.stop()
right_camera.release()
cv2.destroyAllWindows()
def getCapture(cap): # 반복적으로 화면 캡쳐를 얻는 함수
# 로컬에 화면 캡쳐 이미지를 저장함
camera = CSI_Camera()
camera.create_gstreamer_pipeline(sensor_id=0,
sensor_mode=2,
framerate=30,
flip_method=0,
display_height=720,
display_width=1280)
camera.open(camera.gstreamer_pipeline)
camera.start()
cv2.namedWindow("Sticker Solution", cv2.WINDOW_AUTOSIZE)
if not camera.video_capture.isOpened():
print("Unable to open camera")
SystemExit(0)
try:
camera.start_counting_fps()
while cv2.getWindowProperty("Sticker Solution", 0) >= 0:
_, img = camera.read()
cv2.imwrite("images/" + str(cap) + ".jpg", img)
cv2.imshow("Sticker Solution", img)
camera.frames_displayed += 1
cap = cap + 1
if (cv2.waitKey(5) & 0xFF) == 27: break
finally:
camera.stop()
camera.release()
cv2.destroyAllWindows()
def face_detect():
face_cascade = cv2.CascadeClassifier(
"/usr/share/opencv4/haarcascades/haarcascade_frontalface_default.xml")
eye_cascade = cv2.CascadeClassifier(
"/usr/share/opencv4/haarcascades/haarcascade_eye.xml")
left_camera = CSI_Camera()
left_camera.create_gstreamer_pipeline(
sensor_id=0,
sensor_mode=SENSOR_MODE_720,
framerate=30,
flip_method=0,
display_height=DISPLAY_HEIGHT,
display_width=DISPLAY_WIDTH,
)
left_camera.open(left_camera.gstreamer_pipeline)
left_camera.start()
cv2.namedWindow("Face Detect", cv2.WINDOW_AUTOSIZE)
if (not left_camera.video_capture.isOpened()):
# Cameras did not open, or no camera attached
print("Unable to open any cameras")
# TODO: Proper Cleanup
SystemExit(0)
try:
# Start counting the number of frames read and displayed
left_camera.start_counting_fps()
while cv2.getWindowProperty("Face Detect", 0) >= 0:
img = read_camera(left_camera, False)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
roi_gray = gray[y:y + h, x:x + w]
roi_color = img[y:y + h, x:x + w]
eyes = eye_cascade.detectMultiScale(roi_gray)
for (ex, ey, ew, eh) in eyes:
cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh),
(0, 255, 0), 2)
if show_fps:
draw_label(
img, "Frames Displayed (PS): " +
str(left_camera.last_frames_displayed), (10, 20))
draw_label(
img,
"Frames Read (PS): " + str(left_camera.last_frames_read),
(10, 40))
cv2.imshow("Face Detect", img)
left_camera.frames_displayed += 1
keyCode = cv2.waitKey(5) & 0xFF
# Stop the program on the ESC key
if keyCode == 27:
break
finally:
left_camera.stop()
left_camera.release()
cv2.destroyAllWindows()
def lighter_detect():
camera = CSI_Camera()
camera.create_gstreamer_pipeline(
sensor_id = 0,
sensor_mode = SENSOR_MODE,
framerate = 30,
flip_method = 0,
display_height = DISPLAY_HEIGHT,
display_width = DISPLAY_WIDTH,
)
camera.open(camera.gstreamer_pipeline)
camera.start()
cv2.namedWindow("Lighter Test FPS", cv2.WINDOW_AUTOSIZE)
if not camera.video_capture.isOpened():
print("Unable to open camera")
SystemExit(0)
try:
camera.start_counting_fps()
while cv2.getWindowProperty("Lighter Test FPS", 0) >= 0:
img = read_camera(camera)
blob = cv2.dnn.blobFromImage(img, 0.00392, (446, 446), (0, 0, 0), True, crop=False)
net.setInput(blob)
outs = net.forward(output_layers)
confidences = []
boxes = []
for out in outs:
for detection in out:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.5 :
center_x = int(detection[0] * DISPLAY_WIDTH)
center_y = int(detection[1] * DISPLAY_HEIGHT)
w = int(detection[2] * DISPLAY_WIDTH)
h = int(detection[3] * DISPLAY_HEIGHT)
x = int(center_x - w / 2)
y = int(center_y - h / 2)
if class_id == 0: cv2.rectangle(img, (x, y), (x+w, y+h), (0, 0, 255), 2, cv2.LINE_AA)
elif class_id == 1: cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2, cv2.LINE_AA)
else: cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2, cv2.LINE_AA)
cv2.putText(img, classes[class_id], (x, y+30), cv2.FONT_HERSHEY_PLAIN, 2, (0, 0, 255), 2)
boxes.append([x, y, w, h])
confidences.append(float(confidence))
cv2.imshow("Lighter Test FPS", img)
camera.frames_displayed += 1
if (cv2.waitKey(5) & 0xFF) == 27: break # ESC key Stops program
finally:
camera.stop()
camera.release()
cv2.destroyAllWindows()
def getCapture():
camera = CSI_Camera()
camera.create_gstreamer_pipeline(sensor_id=0,
sensor_mode=0,
framerate=30,
flip_method=0,
display_height=DISPLAY_HEIGHT,
display_width=DISPLAY_WIDTH)
camera.open(camera.gstreamer_pipeline)
camera.start()
cv2.namedWindow("Gas Test Display", cv2.WINDOW_AUTOSIZE)
if not camera.video_capture.isOpened():
print("Unable to open camera")
SystemExit(0)
try:
camera.start_counting_fps()
image_num = 1
gpu_frame = cv2.cuda_GpuMat()
while cv2.getWindowProperty("Gas Test Display", 0) >= 0:
_, original_img = camera.read()
gpu_frame.upload(original_img)
# Apply grayscale and sobel filter
#temp = cv2.cvtColor(original_img, cv2.COLOR_RGB2GRAY)
#img = np.zeros([DISPLAY_HEIGHT, DISPLAY_WIDTH, 3])
#for i in range(3) : img[:, :, i] = temp[:, :]
#img = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=5, scale=0.15)
#img = cv2.convertScaleAbs(img)
# Apply Sharpnening filter
#img = getSharpening(img, 2)
gpu_frame.download(original_img)
cv2.imshow("Gas Test Display", original_img)
camera.frames_displayed += 1
if (cv2.waitKey(25) & 0xFF) == 27:
break # Quit if you pressed ESC.
#if (cv2.waitKey(25) & 0xFF) == 13: # Capture if you pressed ENTER.
# cv2.imwrite('/home/jyjun/test' + str(image_num) + '.png', img)
# print("<Image captured successfully!>")
# image_num = image_num + 1
finally:
camera.stop()
camera.release()
cv2.destroyAllWindows()
def getCapture(cap): # 실시간으로 화면을 캡쳐 후 로컬저장함
camera = CSI_Camera()
camera.create_gstreamer_pipeline(
sensor_id=0,
sensor_mode=
2, # [TODO] Sensor_mode로 설정하지 말고 해상도로 설정하도록 해야할 것 같은데? 4032x3040 (Ratio: 4:3)
framerate=30,
flip_method=0,
display_height=720, # [TODO] 이것도 4:3으로 맞춰주는게 가장 best인데...
display_width=1280)
camera.open(camera.gstreamer_pipeline)
camera.start()
cv2.namedWindow("Gas Solution", cv2.WINDOW_AUTOSIZE)
if not camera.video_capture.isOpened():
print("Unable to open camera")
SystemExit(0)
try:
camera.start_counting_fps()
while cv2.getWindowProperty("Gas Solution", 0) >= 0:
_, img_ori = camera.read()
temp = cv2.cvtColor(img_ori, cv2.COLOR_RGB2GRAY)
img = np.zeros([720, 720, 3])
for i in range(3):
img[:, :, i] = temp[:, 280:1000]
img = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=5, scale=0.15)
img = cv2.convertScaleAbs(img)
cv2.imwrite(
"images/" + str(cap) + ".jpg",
img) # [중요!] 이미지를 로컬에 저장함! TODO. 이거 저장하지말고 바로 판단하도록 바꿔야함
#cv2.imshow("Gas Solution", img) # [TODO] 이걸 굳이 띄울 필요가 있나?
time.sleep(0.33) # 의도적으로 3프레임으로 만들려고 0.33초 sleep
camera.frames_displayed += 1
cap = cap + 1
if (cv2.waitKey(5) & 0xFF) == 27: break
finally:
camera.stop()
camera.release()
cv2.destroyAllWindows()
def getCapture(): # 반복적으로 화면 캡쳐를 얻는 함수
# 로컬에 화면 캡쳐 이미지를 저장함
camera = CSI_Camera()
camera.create_gstreamer_pipeline(sensor_id=0,
sensor_mode=1,
framerate=30,
flip_method=2,
display_height=720,
display_width=1280)
camera.open(camera.gstreamer_pipeline)
camera.start()
cv2.namedWindow("Sticker Solution", cv2.WINDOW_AUTOSIZE)
if not camera.video_capture.isOpened():
print("Unable to open camera")
SystemExit(0)
try:
# camera.start_counting_fps()
# while cv2.getWindowProperty("Sticker Solution", 0) >= 0:
cap = 0
while True:
# 0.3 second per once
# time.sleep(0.3)
_, img = camera.read()
img = img[:, 280:1000, :]
cv2.imshow("img", img)
# 외부 통신 삽입 자리
cap += 1
# cv2.imwrite("NewFile1/"+str(cap)+".jpg", img)
# time.sleep(0.1) #시작 전 여기 수정
# camera.frames_displayed += 1
if (cv2.waitKey(5) & 0xFF) == 27:
break
camera.stop()
camera.release()
cv2.destroyAllWindows()
except:
print("?")
def getCapture(): # 실시간으로 화면을 캡쳐 후 로컬저장함
global cap
camera = CSI_Camera()
camera.create_gstreamer_pipeline(sensor_id=0,
sensor_mode=2,
framerate=15,
flip_method=0,
display_height=DISPLAY_HEIGHT,
display_width=DISPLAY_WIDTH)
camera.open(camera.gstreamer_pipeline)
camera.start()
cv2.namedWindow("Gas Solution", cv2.WINDOW_AUTOSIZE)
if not camera.video_capture.isOpened():
print("Unable to open camera")
SystemExit(0)
try:
camera.start_counting_fps()
while cv2.getWindowProperty("Gas Solution", 0) >= 0:
_, img_ori = camera.read()
temp = cv2.cvtColor(img_ori, cv2.COLOR_RGB2GRAY)
img = np.zeros([DISPLAY_HEIGHT, DISPLAY_HEIGHT, 3])
diff_half = (DISPLAY_WIDTH - DISPLAY_HEIGHT) // 2
for i in range(3):
img[:, :, i] = temp[:, diff_half:DISPLAY_WIDTH - diff_half]
img = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=5, scale=0.15)
img = cv2.convertScaleAbs(img)
cv2.imwrite("images/" + str(cap) + ".jpg", img)
cap = cap + 1
cv2.imshow("Gas Solution", img)
camera.frames_displayed += 1
if (cv2.waitKey(5) & 0xFF) == 27: break
finally:
camera.stop()
camera.release()
cv2.destroyAllWindows()
def __init__(self):
self.camera = CSI_Camera()
self.net = cv2.dnn_DetectionModel("model.cfg", "model.weights")
self.gpu_template_img = cv2.cuda_GpuMat(TEMPLATE_IMAGE_ACE)
self.gpu_target_img = cv2.cuda_GpuMat()
###### Lighter information #######
self.head_width = 0
self.body_height = 0
self.upper_sticker_bound = 0
self.lower_sticker_bound = 0
self.sticker_poses = [] # Lighter's sticker position for each lighter
self.error_sticker_images = []
# Manual camera setting variables, initialize with default size.
self.manual_box_x = DISPLAY_WIDTH // 2
self.manual_box_y = DISPLAY_HEIGHT // 2
self.manual_box_width = 150
self.manual_box_height = 300
###### Image Information ######
self.display_contrast = 30 # Default contrast 110%
self.display_brightness = 5 # Default brightness 105%
self.initialize_camera()
self.initialize_yolo()
def checkHeadRatio(raw, stick) : # 라이터 헤드를 찾기 위한 좌표 추정 함수
return int((stick-raw) * (5/105)) #헤드 간 간격/몸통 길이
def checkStickRatio(raw) :
return int(raw * (490/260))
def checkStickerRatio(raw, stick) :
return int((stick-raw)*(45/216)), int((stick-raw)*(133/216))
def checkStickerRatio2(raw, stick) :
return int((stick-raw)*(134/216)), int((stick-raw)*(175/216))
# 로컬에 화면 캡쳐 이미지를 저장함
camera = CSI_Camera()
camera.create_gstreamer_pipeline(
sensor_id = 0,
sensor_mode = 1,
framerate = 30,
flip_method = 2,
display_height = 720,
display_width = 1280
)
camera.open(camera.gstreamer_pipeline)
camera.start()
# cv2.namedWindow("Sticker Solution", cv2.WINDOW_AUTOSIZE)
def getStart():
def __init__(self):
# Pi-Camera for jetson nano (CSI_Camera)
self.camera = CSI_Camera()
self.gpu_frame = cv2.cuda_GpuMat()
# 4:3 Resolution (Capture Resolution: 4032 x 3040)
self.display_width = 1280 # Display width (not captured width)
self.display_height = 960 # Display height (not captured height)
self.upper_not_roi = self.display_height // 4 # Default value of ROI
self.lower_not_roi = 3 * self.display_height // 4 # Default value of ROI
# Load YOLOv4
self.net = cv2.dnn_DetectionModel("yolov4-tiny.cfg",
"yolov4-tiny-final.weights")
self.net.setInputSize(448, 448) # It can be (416, 416) either
self.net.setInputScale(1.0 / 255) # Scaled by 1byte [0, 255]
self.net.setInputSwapRB(True) # Swap BGR order to RGB
self.net.setPreferableTarget(
cv2.dnn.DNN_TARGET_CUDA) # For using CUDA GPU
self.net.setPreferableBackend(
cv2.dnn.DNN_BACKEND_CUDA) # For using CUDA GPU
# Standard Lines
self.upper_std_line = 0 # Lighter's standard line which is used for getting height
self.lower_std_line = 0 # Lighter's standard line which is used for getting height
self.upper_normal_line = 0 # Normal bound line to decide whether lighter has defect or not
self.lower_normal_line = 0 # Normal bound line to decide whether lighter has defect or not
# QSlider bars
# ROI which can be controlled by user
self.roi_upper = 0
self.roi_lower = self.display_height
self.roi_col = 0
# Lighter's standard line which can be controlled by user
self.std_upper = 0
self.std_lower = self.display_height
# Lighter Informations
self.lighter_width = 0 # Thickness of one lighter
self.lighter_height = 0 # Height of lighter
# Image filters
# Sobel filter is used to extract the outline of an image.
self.sobel_filter = cv2.cuda.createSobelFilter(cv2.CV_8U,
cv2.CV_8U,
0,
1,
ksize=5,
scale=0.25)
# Hough segment filter is used to extract standard line of lighter.
# You can decrease first two arguments if you want higher precision.
# 3rd arg: minimum length of line
# 4th arg: minimum gap of each lines
self.hough_seg_filter = cv2.cuda.createHoughSegmentDetector(
0.1, np.pi / 180, 80, 1)
signal.signal(
signal.SIGINT,
self.sigint_handler) # Allocate Ctrl + C (SIGINT)'s handler.
self.initialize_camera()
self.load_lighter_info()
def face_detect(sensor_mode=S_MODE_3_1280_720_60,
dispW=DISP_W_M3_M4_one_half,
dispH=DISP_H_M3_M4_one_half):
face_cascade = cv2.CascadeClassifier(
"/usr/share/opencv4/haarcascades/haarcascade_frontalface_default.xml")
eye_cascade = cv2.CascadeClassifier(
"/usr/share/opencv4/haarcascades/haarcascade_eye.xml")
left_camera = CSI_Camera()
# WS mod: IMPORTANT use lowest fps = 21 as default, otherwise modes 0 or 1 crash: reboot required
left_camera.create_gstreamer_pipeline(
sensor_id=0,
sensor_mode=sensor_mode,
framerate=21,
flip_method=0,
display_height=dispH,
display_width=dispW,
)
left_camera.open(left_camera.gstreamer_pipeline)
left_camera.start()
txt = "Face Detect: Sensor Mode {}, Display {} x {}".format(
sensor_mode, dispW, dispH) # WS mod
cv2.namedWindow(txt, cv2.WINDOW_AUTOSIZE)
if (not left_camera.video_capture.isOpened()):
# Cameras did not open, or no camera attached
print("Unable to open any cameras")
# TODO: Proper Cleanup
SystemExit(0)
try:
# Start counting the number of frames read and displayed
left_camera.start_counting_fps()
while cv2.getWindowProperty(txt, 0) >= 0:
img = read_camera(left_camera, False)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
roi_gray = gray[y:y + h, x:x + w]
roi_color = img[y:y + h, x:x + w]
eyes = eye_cascade.detectMultiScale(roi_gray)
for (ex, ey, ew, eh) in eyes:
cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh),
(0, 255, 0), 2)
if show_fps:
draw_label(
img, "Frames Displayed (PS): " +
str(left_camera.last_frames_displayed), (10, 20))
draw_label(
img,
"Frames Read (PS): " + str(left_camera.last_frames_read),
(10, 40))
cv2.imshow(txt, img)
left_camera.frames_displayed += 1
keyCode = cv2.waitKey(5) & 0xFF
# Stop the program on the ESC key
if keyCode == 27:
break
finally:
left_camera.stop()
left_camera.release()
cv2.destroyAllWindows()
def getCapture(): # 반복적으로 화면 캡쳐를 얻는 함수
# 로컬에 화면 캡쳐 이미지를 저장함
camera = CSI_Camera()
camera.create_gstreamer_pipeline(sensor_id=0,
sensor_mode=0,
framerate=30,
flip_method=0,
display_height=720,
display_width=1280)
camera.open(camera.gstreamer_pipeline)
camera.start()
try:
# camera.start_counting_fps()
while 1:
_, img_ori = camera.read()
temp = cv2.cvtColor(img_ori, cv2.COLOR_RGB2GRAY)
temp = cv2.cuda.cvtColor(img_ori, cv.COLOR_RGB2GRAY)
img = np.zeros([720, 720, 3])
for i in range(3):
img[:, :, i] = temp[:, 280:1000]
img = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=5, scale=0.15)
img = cv2.cuda.createSobelFilter(cv.CV_8UC1, -1, 1, 1)
img = cv2.convertScaleAbs(img)
sub_img = copy.deepcopy(img)
print("1")
raw = 0
try:
print("2")
temp, stick = findRaw(img) # 라이터 위치를 특정하기 위한 받침대 위치 확인
print(temp, stick)
if temp is not None and temp > 0:
# if 0.9*raw < temp < 1.1*raw : print("카메라가 위치를 벗어남") # 이전 프레임과 비교하여 받침대 위치가 벗어나면 카메라가 움직인 것
raw = temp
print("3")
# 상한~하한선 찾기
print(stick, raw)
h_line, m_line, l_line = checkLineRatio(stick, raw)
print(h_line, m_line, l_line)
cv2.line(sub_img, (0, raw), (720, raw), (255, 255, 0), 1)
cv2.line(sub_img, (0, stick), (720, stick), (255, 255, 0), 1)
cv2.line(sub_img, (0, h_line), (720, h_line), (0, 255, 0), 1)
cv2.line(sub_img, (0, m_line), (720, m_line), (0, 255, 0), 1)
cv2.line(sub_img, (0, l_line), (720, l_line), (0, 255, 0), 1)
cv2.imwrite("images/" + str(10) + ".jpg", sub_img)
except:
pass
cv2.imshow("Gas Solution", sub_img)
time.sleep(10)
if (cv2.waitKey(5) & 0xFF) == 27: break
finally:
camera.stop()
camera.release()
cv2.destroyAllWindows()