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little fixes

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This commit is contained in:
Sagi Frimer
2019-04-20 12:39:24 +03:00
parent 8226179881
commit cef43d9bcd
3 changed files with 74 additions and 71 deletions
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5
backend/app/classes/Exceptions.py Normal file
View File

@@ -0,0 +1,5 @@
class PipelineAlreadyExistsException(Excetion):
def __init__(self, pipe_name):
super(f"Pipeline {pipe_name} already exists")

22
backend/app/classes/SettingsManager.py
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@@ -4,10 +4,12 @@ import cscore
import networktables
from .Singleton import Singleton
from .CamerasHandler import CamerasHandler
from .Exceptions import PipelineAlreadyExistsException
class SettingsManager(metaclass=Singleton):
cams = {}
settings = {}
def __init__(self):
self.settings_path = os.path.join(os.getcwd(), "settings")
@@ -16,19 +18,16 @@ class SettingsManager(metaclass=Singleton):
self._init_cameras()
def _init_general_settings(self):
def init_default_settings():
try:
with open(os.path.join(self.settings_path, 'settings.json')) as setting_file:
self.settings = json.load(setting_file)
except FileNotFoundError:
self.settings = {
"team_number": 1577,
"curr_camera": "cam1",
"curr_pipeline": "pipeline1"
}
try:
with open(os.path.join(self.settings_path, 'settings.json')) as setting_file:
self.settings = json.load(setting_file)
except FileNotFoundError:
init_default_settings()
def _init_cameras(self):
cameras = CamerasHandler.get_cameras()
@@ -72,7 +71,14 @@ class SettingsManager(metaclass=Singleton):
cam_name = self.settings["curr_camera"]
if not pipe_name:
pipe_name = "pipeline" + str(len(self.cams[cam_name]))
suffix = 0
pipe_name = "pipeline" + str(suffix)
while pipe_name not in self.cams[cam_name]["pipelines"]:
suffix += 1
pipe_name = "pipeline" + str(suffix)
elif self.cams[cam_name]["pipelines"][pipe_name]:
raise PipelineAlreadyExistsException(pipe_name)
self.cams[cam_name]["pipelines"][pipe_name] = {
"exposure": 50,

118
backend/app/handlers/VisionHandler.py
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@@ -4,83 +4,75 @@ import cv2
import numpy
from app.classes.SettingsManager import SettingsManager
class VisionHandler:
def __init__(self):
self.kernel = numpy.ones((5, 5), numpy.uint8)
def hsv_threshold(self,hue: list, saturation: list, value: list, img: numpy.ndarray, is_erode: bool, is_dilate: bool):
# img = cv2.medianBlur(img, 1)
# not sure if we need noise reduction now with erode it hurts the precision if val is to high
def _hsv_threshold(self, hue: list, saturation: list, value: list, img: numpy.ndarray, is_erode: bool, is_dilate: bool):
# img = cv2.medianBlur(img, 1)
# not sure if we need noise reduction now with erode it hurts the precision if val is to high
img = cv2.erode(img, kernel=self.kernel, iterations=is_erode)
img = cv2.dilate(img, kernel=self.kernel, iterations=is_dilate)
out = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
return cv2.inRange(out, (hue[0], saturation[0], value[0]), (hue[1], saturation[1], value[1]))
img = cv2.erode(img, kernel=self.kernel, iterations=is_erode)
img = cv2.dilate(img, kernel=self.kernel, iterations=is_dilate)
out = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
return cv2.inRange(out, (hue[0], saturation[0], value[0]), (hue[1], saturation[1], value[1]))
def find_contours(self, binary_img: numpy.ndarray):
_, contours, _ = cv2.findContours(binary_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
return contours
def find_contours(self, binary_img: numpy.ndarray):
_, contours, _ = cv2.findContours(binary_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
return contours
def filter_contours(self, input_contours, camera_area, min_area, max_area, min_ratio, max_ratio, min_extent,
max_extent):
output = []
rectangle = []
for contour in input_contours:
def filter_contours(self, input_contours, camera_area, min_area, max_area, min_ratio, max_ratio, min_extent, max_extent):
output = []
rectangle = []
rect = cv2.minAreaRect(contour)
# center_point = rect[0]
contour_area = cv2.contourArea(contour)
rect_area = rect[1][0] * rect[1][1]
for contour in input_contours:
try:
extent = float(contour_area) / rect_area
ratio = float(rect[1][0]) / rect[1][1]
area = rect_area / camera_area
except:
continue
rect = cv2.minAreaRect(contour)
# center_point = rect[0]
contour_area = cv2.contourArea(contour)
rect_area = rect[1][0] * rect[1][1]
if area < min_area or area > max_area:
continue
if ratio < min_ratio or ratio > max_ratio:
continue
if extent < min_extent or extent > max_extent:
continue
try:
extent = float(contour_area) / rect_area
ratio = float(rect[1][0]) / rect[1][1]
area = rect_area / camera_area
except:
continue
output.append(contour)
rectangle.append(rect)
if area < min_area or area > max_area:
continue
if ratio < min_ratio or ratio > max_ratio:
continue
if extent < min_extent or extent > max_extent:
continue
return [output, rectangle]
output.append(contour)
rectangle.append(rect)
return [output, rectangle]
def draw_image(self, input_image: numpy.ndarray, is_binary: bool, rectangles):
if is_binary:
input_image = cv2.cvtColor(input_image, cv2.COLOR_GRAY2RGB)
for rectangle in rectangles[1]:
box = cv2.boxPoints(rectangle)
box = numpy.int0(box)
cv2.drawContours(input_image, [box], 0, (0, 0, 255), 2)
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 run():
camera_server = cscore.CameraServer.getInstance()
networktables.NetworkTables.startClientTeam(team=SettingsManager.settings["team_number"])
networktables.NetworkTables.initialize()
def camera_process(cv_sink,stream):
image = numpy.zeros(shape=(0,0,3), dtype=numpy.uint8)
table = networktables.NetworkTables.getTable("/Chameleon-Vision/"+"cam name")
while True:
_, image = cv_sink.grabFrame(image)
stream.putFrame(image)
def draw_image(self, input_image: numpy.ndarray, is_binary: bool, rectangles):
if is_binary:
input_image = cv2.cvtColor(input_image, cv2.COLOR_GRAY2RGB)
for rectangle in rectangles[1]:
box = cv2.boxPoints(rectangle)
box = numpy.int0(box)
cv2.drawContours(input_image, [box], 0, (0, 0, 255), 2)
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 run(self):
camera_server = cscore.CameraServer.getInstance()
networktables.NetworkTables.startClientTeam(team=SettingsManager.settings["team_number"])
networktables.NetworkTables.initialize()
def camera_process(cv_sink, stream):
image = numpy.zeros(shape=(0, 0, 3), dtype=numpy.uint8)
table = networktables.NetworkTables.getTable("/Chameleon-Vision/" + "cam name")
while True:
_, image = cv_sink.grabFrame(image)
stream.putFrame(image)