mirror of
https://github.com/PhotonVision/photonvision
synced 2026-06-21 01:01:41 +00:00
441 lines
19 KiB
Python
441 lines
19 KiB
Python
import asyncio
|
|
import time
|
|
|
|
from networktables import NetworkTables
|
|
import networktables
|
|
import cv2
|
|
import numpy
|
|
from cscore import CameraServer
|
|
from app.classes.SettingsManager import SettingsManager
|
|
from ..classes.Singleton import Singleton
|
|
from multiprocessing import Process
|
|
import threading
|
|
import zmq
|
|
import math
|
|
from enum import Enum, unique
|
|
from ..handlers.SocketHandler import send_all_async
|
|
|
|
|
|
class VisionHandler(metaclass=Singleton):
|
|
def __init__(self):
|
|
self.kernel = numpy.ones((5, 5), numpy.uint8)
|
|
self.cs = CameraServer.getInstance()
|
|
self.settings_manager = SettingsManager()
|
|
|
|
def _hsv_threshold(self, hue: list, saturation: list, value: list, img: numpy.ndarray, is_erode: bool,
|
|
is_dilate: bool):
|
|
blur = cv2.blur(img, (3, 3))
|
|
hsv = cv2.cvtColor(blur, cv2.COLOR_BGR2HSV)
|
|
lower = numpy.array([hue[0], saturation[0], value[0]])
|
|
upper = numpy.array([hue[1], saturation[1], value[1]])
|
|
thresh = cv2.inRange(hsv, lower, upper)
|
|
erode_img = cv2.erode(thresh, kernel=self.kernel, iterations=is_erode)
|
|
dilate_img = cv2.dilate(erode_img, kernel=self.kernel, iterations=is_dilate)
|
|
return dilate_img
|
|
|
|
def find_contours(self, binary_img: numpy.ndarray):
|
|
_, contours, _ = cv2.findContours(binary_img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
return contours
|
|
|
|
class Filter_Contours:
|
|
def __init__(self,center_x, center_y):
|
|
self.sort_mode = self.SortMode(center_x=center_x, center_y=center_y)
|
|
self.center_y = center_y
|
|
self.center_x = center_x
|
|
|
|
class SortMode:
|
|
def __init__(self, center_x, center_y):
|
|
self.center_x = center_x
|
|
self.center_y = center_y
|
|
|
|
@classmethod
|
|
def moment_x(cls,contour):
|
|
M = cv2.moments(contour)
|
|
try:
|
|
x = float(M['m10'] / M['m00'])
|
|
except ZeroDivisionError:
|
|
x = 0
|
|
return x
|
|
|
|
@classmethod
|
|
def moment_y(cls, contour):
|
|
M = cv2.moments(contour)
|
|
try:
|
|
y = float(M['m01'] / M['m00'])
|
|
except ZeroDivisionError:
|
|
y = 0
|
|
return y
|
|
|
|
@classmethod
|
|
def calc_distance(cls,contour, center_x, center_y):
|
|
M = cv2.moments(contour)
|
|
try:
|
|
x = int(M['m10'] / M['m00'])
|
|
except ZeroDivisionError:
|
|
x = 0
|
|
try:
|
|
y = int(M['m01'] / M['m00'])
|
|
except ZeroDivisionError:
|
|
y = 0
|
|
# this function was suggested by my girlfriend maya jugend that i really love
|
|
return math.sqrt((center_x-x)**2 + (center_y-y)**2)
|
|
|
|
def Largest(self, input_contours):
|
|
return sorted(input_contours, key=lambda x: cv2.contourArea(x), reverse=True)
|
|
|
|
def Smallest(self, input_contours):
|
|
return sorted(input_contours, key=lambda x: cv2.contourArea(x))
|
|
|
|
def Highest(self, input_contours):
|
|
return sorted(input_contours, key=lambda x: self.moment_y(x))
|
|
|
|
def Lowest(self, input_contours):
|
|
return sorted(input_contours, key=lambda x: self.moment_y(x),reverse=True)
|
|
|
|
def Rightmost(self, input_contours):
|
|
return sorted(input_contours, key=lambda x: self.moment_x(x), reverse=True)
|
|
|
|
def Leftmost(self, input_contours):
|
|
return sorted(input_contours, key=lambda x: self.moment_x(x))
|
|
|
|
def Closest(self, input_contours):
|
|
return sorted(input_contours, key=lambda x: self.calc_distance(x, center_x=self.center_x,
|
|
center_y=self.center_y), reverse=True)
|
|
|
|
def filter_contours(self, input_contours, cam_area, area, ratio, extent, sort_mode, target_grouping,
|
|
target_intersection):
|
|
class TargetGroup(Enum):
|
|
Single = 1
|
|
Dual = 2
|
|
Triple = 3
|
|
Quadruple = 4
|
|
Quintuple = 6
|
|
|
|
def group_target(i_contours, target_group, intersection_point):
|
|
|
|
def is_intersecting(contour_a, contour_b, intersection_direction):
|
|
|
|
[vx_a, vy_a, x0_a, y0_a] = cv2.fitLine(contour_a, cv2.DIST_L2, 0, 0.01, 0.01)
|
|
[vx_b, vy_b, x0_b, y0_b] = cv2.fitLine(contour_b, cv2.DIST_L2, 0, 0.01, 0.01)
|
|
# getting line data of both contours
|
|
m_a = vy_a / vx_a
|
|
m_b = vy_b / vx_b
|
|
# calculating slope of both lines
|
|
try:
|
|
intersection_x = ((m_a * x0_a) - y0_a - (m_b * x0_b) + y0_b) / (m_a - m_b)
|
|
except ZeroDivisionError:
|
|
if intersection_direction == 'Parallel':
|
|
return True
|
|
else:
|
|
return False
|
|
intersection_y = (m_a * (intersection_x - x0_a)) + y0_a
|
|
# finding intersection point
|
|
if intersection_direction == 'Up':
|
|
if intersection_y < self.center_y:
|
|
return True
|
|
elif intersection_direction == 'Down':
|
|
if intersection_y > self.center_y:
|
|
return True
|
|
elif intersection_direction == 'Left':
|
|
if intersection_x < self.center_x:
|
|
return True
|
|
elif intersection_direction == 'Right':
|
|
if intersection_x > self.center_x:
|
|
return True
|
|
else:
|
|
return False
|
|
if target_group != TargetGroup.Single:
|
|
f_contour_list = []
|
|
for index, g_contour in enumerate(i_contours):
|
|
final_contour = g_contour
|
|
for c in range(target_group.value - 1):
|
|
try:
|
|
first_contour = i_contours[index + c]
|
|
second_contour = i_contours[index + c + 1]
|
|
except IndexError:
|
|
final_contour = []
|
|
break
|
|
if is_intersecting(first_contour, second_contour, intersection_point):
|
|
final_contour = numpy.concatenate((final_contour, second_contour))
|
|
|
|
else:
|
|
final_contour = []
|
|
break
|
|
if final_contour != []:
|
|
f_contour_list.append(final_contour)
|
|
|
|
return f_contour_list
|
|
else:
|
|
return i_contours
|
|
|
|
'''start of the first filtration of contours'''
|
|
filtered_contours = []
|
|
for contour in input_contours:
|
|
try:
|
|
contour_area = cv2.contourArea(contour)
|
|
target_area = float(contour_area / cam_area)*100
|
|
|
|
if target_area >= area[1] or target_area <= area[0]:
|
|
continue
|
|
|
|
rect = cv2.minAreaRect(contour)
|
|
bounding_rect_area = rect[1][0] * rect[1][1]
|
|
try:
|
|
target_fullness = float(contour_area / bounding_rect_area)*100
|
|
except ZeroDivisionError:
|
|
target_fullness = 0
|
|
|
|
if target_fullness <= extent[0] or target_fullness >= extent[1]:
|
|
continue
|
|
try:
|
|
aspect_ratio = float(rect[1][0]/rect[1][1])
|
|
except ZeroDivisionError:
|
|
aspect_ratio = 0
|
|
if aspect_ratio <= ratio[0] or aspect_ratio >= ratio[1]:
|
|
continue
|
|
|
|
filtered_contours.append(contour)
|
|
except Exception as e:
|
|
print(e)
|
|
continue
|
|
#checking for contour grouping before sorting
|
|
grouped_contours = group_target(filtered_contours, TargetGroup[target_grouping], target_intersection)
|
|
try:
|
|
sorted_contours = getattr(self.sort_mode, sort_mode)(grouped_contours)
|
|
except TypeError:
|
|
sorted_contours = []
|
|
return sorted_contours
|
|
|
|
@unique
|
|
class Region(Enum):
|
|
UP_MOST = 0
|
|
RIGHT_MOST = 1
|
|
DOWN_MOST = 2
|
|
LEFT_MOST = 3
|
|
CENTER_MOST = 4
|
|
|
|
def output_contour(self, sorted_contours):
|
|
if len(sorted_contours) > 0:
|
|
selected_contour = sorted_contours[0]
|
|
rect = cv2.minAreaRect(selected_contour)
|
|
else:
|
|
return []
|
|
|
|
# crosshair_calibration function to "put" camera in the middle
|
|
return rect
|
|
|
|
def draw_image(self, input_image, contour):
|
|
if len(input_image.shape)<3:
|
|
input_image = cv2.cvtColor(input_image, cv2.COLOR_GRAY2RGB)
|
|
if contour != []:
|
|
box = cv2.boxPoints(contour)
|
|
box = numpy.int0(box)
|
|
cv2.drawContours(input_image, [box], 0, (0, 0, 255), 3)
|
|
|
|
# center_point = (int(rectangle[0][0]), int(rectangle[0][1]))
|
|
# cv2.circle(input_image, center_point, 0, (0, 255, 0), thickness=3, lineType=8, shift=0)
|
|
return input_image
|
|
|
|
def calculate_pitch(self, pixel_y, center_y, v_focal_length):
|
|
pitch = math.degrees(math.atan((pixel_y - center_y) / v_focal_length))
|
|
# Just stopped working have to do this:
|
|
pitch *= -1
|
|
return pitch
|
|
|
|
def calculate_yaw(self, pixel_x, center_x, h_focal_length):
|
|
yaw = math.degrees(math.atan((pixel_x - center_x) / h_focal_length))
|
|
return yaw
|
|
|
|
def run(self):
|
|
NetworkTables.startClientTeam(team=self.settings_manager.general_settings.get("team_number", 1577))
|
|
# NetworkTables.initialize("localhost")
|
|
|
|
port = 5550
|
|
|
|
for cam_name in self.settings_manager.usb_cameras:
|
|
threading.Thread(target=self.thread_proc, args=(cam_name, port)).start()
|
|
port += 1
|
|
|
|
def thread_proc(self, cam_name, port=5557):
|
|
asyncio.set_event_loop(asyncio.new_event_loop())
|
|
self.settings_manager.cams_curr_pipeline[cam_name] = "pipeline0"
|
|
pipeline = self.settings_manager.cams[cam_name]["pipelines"][self.settings_manager.cams_curr_pipeline[cam_name]]
|
|
FOV = self.settings_manager.cams[cam_name]["FOV"]
|
|
|
|
def change_camera_values(pipline):
|
|
self.settings_manager.usb_cameras[cam_name].setBrightness(pipeline['brightness'])
|
|
self.settings_manager.usb_cameras[cam_name].setExposureManual(pipeline['exposure'])
|
|
self.settings_manager.usb_cameras[cam_name].setWhiteBalanceAuto()
|
|
|
|
def pipeline_listener(table, key, value, is_new):
|
|
asyncio.set_event_loop(asyncio.new_event_loop())
|
|
self.settings_manager.cams_curr_pipeline[cam_name] = value
|
|
change_camera_values(pipeline)
|
|
if cam_name == self.settings_manager.general_settings['curr_camera']:
|
|
self.settings_manager.general_settings['curr_pipeline'] = value
|
|
update_settings = self.settings_manager.get_curr_pipeline()
|
|
update_settings['curr_pipeline'] = self.settings_manager.general_settings["curr_pipeline"]
|
|
send_all_async(update_settings)
|
|
|
|
def mode_listener(table, key, value, is_new):
|
|
change_camera_values({
|
|
'brightness': 25,
|
|
'exposure': 15
|
|
})
|
|
|
|
table = NetworkTables.getTable("/Chameleon-Vision/" + cam_name)
|
|
table.putString('Pipeline', self.settings_manager.cams_curr_pipeline[cam_name])
|
|
table.addEntryListenerEx(pipeline_listener, key="Pipeline",
|
|
flags=networktables.NetworkTablesInstance.NotifyFlags.UPDATE)
|
|
table.addEntryListenerEx(mode_listener, key="Driver_Mode",
|
|
flags=networktables.NetworkTablesInstance.NotifyFlags.UPDATE)
|
|
#gettings video from curent camera
|
|
cv_sink = self.cs.getVideo(camera=self.settings_manager.usb_cameras[cam_name])
|
|
|
|
width = self.settings_manager.cams[cam_name]["video_mode"]["width"]
|
|
height = self.settings_manager.cams[cam_name]["video_mode"]["height"]
|
|
|
|
#setting up a video server for camera
|
|
cv_publish = self.cs.putVideo(name=cam_name, width=width, height=height)
|
|
# saving camera port in cam name dict for usage in client
|
|
self.settings_manager.cams_port[cam_name] = self.cs._sinks['serve_'+cam_name].getPort()
|
|
|
|
#setting up a zmq connection to the opencv subprocess
|
|
context = zmq.Context()
|
|
socket = context.socket(zmq.PAIR)
|
|
socket.bind('tcp://*:%s' % str(port))
|
|
|
|
#starting the process with inital values
|
|
p = Process(target=self.camera_process, args=(cam_name, port, FOV))
|
|
p.start()
|
|
|
|
change_camera_values(pipeline)
|
|
|
|
def _thread():
|
|
global image
|
|
image = numpy.zeros(shape=(width, height, 3), dtype=numpy.uint8)
|
|
while True:
|
|
_, image = cv_sink.grabFrame(image)
|
|
|
|
threading.Thread(target=_thread).start()
|
|
while True:
|
|
pipeline = self.settings_manager.cams[cam_name]["pipelines"][self.settings_manager.cams_curr_pipeline[cam_name]]
|
|
socket.send_json(dict(
|
|
pipeline=pipeline
|
|
), zmq.SNDMORE)
|
|
|
|
socket.send_pyobj(image)
|
|
p_image = socket.recv_pyobj()
|
|
nt_data = socket.recv_json()
|
|
table.putBoolean('valid', nt_data['valid'])
|
|
# check if point is valid
|
|
|
|
# print(nt_data['fps'])
|
|
|
|
if nt_data['valid']:
|
|
#send the point using network tables
|
|
table.putNumber('pitch', nt_data['pitch'])
|
|
table.putNumber('yaw', nt_data['yaw'])
|
|
#if the selected camera in ui is this cam send the point to the ui
|
|
|
|
if self.settings_manager.general_settings['curr_camera'] == cam_name:
|
|
try:
|
|
if nt_data['raw_point'] is not None:
|
|
send_all_async({
|
|
'raw_point': nt_data['raw_point'],
|
|
'point': {
|
|
'pitch': nt_data['pitch'],
|
|
'yaw': nt_data['yaw'],
|
|
'fps': nt_data['fps']
|
|
}
|
|
})
|
|
except Exception as e:
|
|
print(e)
|
|
#send the image to the camera server
|
|
# print(nt_data['fps'])
|
|
cv_publish.putFrame(p_image)
|
|
|
|
def camera_process(self, cam_name, port, FOV):
|
|
from fractions import Fraction
|
|
|
|
diagonalView = math.radians(FOV) #needs to be implemented in client
|
|
|
|
width = self.settings_manager.cams[cam_name]["video_mode"]["width"]
|
|
height = self.settings_manager.cams[cam_name]["video_mode"]["height"]
|
|
centerX = (width / 2) - .5
|
|
centerY = (height / 2) - .5
|
|
cam_area = width * height
|
|
|
|
aspect_fraction = Fraction(width, height)
|
|
horizontal_ratio = aspect_fraction.numerator
|
|
vertical_ratio = aspect_fraction.denominator
|
|
|
|
horizontalView = math.atan(math.tan(diagonalView/2) * (horizontal_ratio / diagonalView)) * 2
|
|
verticalView = math.atan(math.tan(diagonalView/2) * (vertical_ratio / diagonalView)) * 2
|
|
|
|
H_FOCAL_LENGTH = width / (2*math.tan((horizontalView/2)))
|
|
V_FOCAL_LENGTH = height / (2*math.tan((verticalView/2)))
|
|
|
|
context = zmq.Context()
|
|
socket = context.socket(zmq.PAIR)
|
|
socket.connect('tcp://localhost:%s' % str(port))
|
|
filter_contours = self.Filter_Contours(center_x=centerX, center_y=centerY)
|
|
x = 1
|
|
counter = 0
|
|
start_time = time.time()
|
|
fps = 0
|
|
while True:
|
|
obj = socket.recv_json()
|
|
image = socket.recv_pyobj()
|
|
curr_pipeline = obj["pipeline"]
|
|
if curr_pipeline['orientation'] == "Inverted":
|
|
M = cv2.getRotationMatrix2D((width / 2, height / 2), 180, 1)
|
|
image = cv2.warpAffine(image, M, (width, height))
|
|
hsv_image = self._hsv_threshold(curr_pipeline["hue"],
|
|
curr_pipeline["saturation"], curr_pipeline["value"],
|
|
image, curr_pipeline["erode"], curr_pipeline["dilate"])
|
|
# if table.getBoolean("Driver_Mode", False):
|
|
contours = self.find_contours(hsv_image)
|
|
filtered_contours = filter_contours.filter_contours(input_contours=contours, area=curr_pipeline['area'],
|
|
ratio=curr_pipeline['ratio'],
|
|
extent=curr_pipeline['extent'],
|
|
sort_mode=curr_pipeline['sort_mode'], cam_area=cam_area,
|
|
target_grouping=curr_pipeline['target_group'],
|
|
target_intersection=
|
|
curr_pipeline['target_intersection'])
|
|
final_contour = self.output_contour(filtered_contours)
|
|
try:
|
|
center = final_contour[0]
|
|
center_x = (center[1] - curr_pipeline['B']) / curr_pipeline["M"]
|
|
center_y = (center[0] * curr_pipeline["M"]) + curr_pipeline["B"]
|
|
pitch = self.calculate_pitch(pixel_y=center[1], center_y=center_y, v_focal_length=V_FOCAL_LENGTH)
|
|
yaw = self.calculate_yaw(pixel_x=center[0], center_x=center_x, h_focal_length=H_FOCAL_LENGTH)
|
|
valid = True
|
|
except IndexError:
|
|
center = None
|
|
pitch = None
|
|
yaw = None
|
|
valid = False
|
|
|
|
if curr_pipeline['is_binary']:
|
|
draw_image = hsv_image
|
|
else:
|
|
draw_image = image
|
|
res = self.draw_image(input_image=draw_image, contour=final_contour)
|
|
socket.send_pyobj(res)
|
|
socket.send_json(dict(
|
|
pitch=pitch,
|
|
yaw=yaw,
|
|
valid=valid,
|
|
raw_point=center,
|
|
fps=fps
|
|
))
|
|
counter += 1
|
|
if (time.time() - start_time) > x:
|
|
fps = (counter / (time.time() - start_time))
|
|
counter = 0
|
|
start_time = time.time()
|
|
|
|
|
|
|