Python 中使用 GRIP 处理图像的分段错误
Segmentation Fault using GRIP to process images in Python
我正在使用 Python 通过以太网连接从 IP 摄像机获取图像，然后处理它们以寻找特定目标。我正在使用 GRIP 生成代码来寻找特定的目标区域。 （对于那些不熟悉 GRIP 的人：它基本上为您提供了一个 GUI 桌面界面，您可以在其中查看实时视频源并更改参数，直到获得所需的输出。然后您可以自动生成一段代码——我的代码在 Python—这将对您在代码中输入的任何图像执行该处理 'pipeline'）。
在对我的连接代码进行了大量调试之后，我终于获得了一个成功的工作连接，它从 IP 摄像机获取图像并将其发送到 GRIP 管道中。但是，图像处理失败，它返回一个分段错误，没有指示行号。这是管道代码（自动生成）：
import cv2
import numpy
import math
from enum import Enum
class GripPipeline:
"""
An OpenCV pipeline generated by GRIP.
"""
def __init__(self):
"""initializes all values to presets or None if need to be set
"""
self.__blur_type = BlurType.Median_Filter
self.__blur_radius = 19.81981981981982
self.blur_output = None
self.__hsv_threshold_input = self.blur_output
self.__hsv_threshold_hue = [72.84172661870504, 86.31399317406144]
self.__hsv_threshold_saturation = [199.50539568345323, 255.0]
self.__hsv_threshold_value = [89.43345323741006, 255.0]
self.hsv_threshold_output = None
self.__find_contours_input = self.hsv_threshold_output
self.__find_contours_external_only = False
self.find_contours_output = None
self.__filter_contours_contours = self.find_contours_output
self.__filter_contours_min_area = 500.0
self.__filter_contours_min_perimeter = 0.0
self.__filter_contours_min_width = 0.0
self.__filter_contours_max_width = 1000.0
self.__filter_contours_min_height = 0.0
self.__filter_contours_max_height = 1000.0
self.__filter_contours_solidity = [0, 100]
self.__filter_contours_max_vertices = 1000000.0
self.__filter_contours_min_vertices = 0.0
self.__filter_contours_min_ratio = 0.0
self.__filter_contours_max_ratio = 1000.0
self.filter_contours_output = None
def process(self, source0):
"""
Runs the pipeline and sets all outputs to new values.
"""
# Step Blur0:
self.__blur_input = source0
(self.blur_output) = self.__blur(self.__blur_input, self.__blur_type, self.__blur_radius)
# Step HSV_Threshold0:
self.__hsv_threshold_input = self.blur_output
(self.hsv_threshold_output) = self.__hsv_threshold(self.__hsv_threshold_input, self.__hsv_threshold_hue, self.__hsv_threshold_saturation, self.__hsv_threshold_value)
# Step Find_Contours0:
self.__find_contours_input = self.hsv_threshold_output
(self.find_contours_output) = self.__find_contours(self.__find_contours_input, self.__find_contours_external_only)
# Step Filter_Contours0:
self.__filter_contours_contours = self.find_contours_output
(self.filter_contours_output) = self.__filter_contours(self.__filter_contours_contours, self.__filter_contours_min_area, self.__filter_contours_min_perimeter, self.__filter_contours_min_width, self.__filter_contours_max_width, self.__filter_contours_min_height, self.__filter_contours_max_height, self.__filter_contours_solidity, self.__filter_contours_max_vertices, self.__filter_contours_min_vertices, self.__filter_contours_min_ratio, self.__filter_contours_max_ratio)
@staticmethod
def __blur(src, type, radius):
"""Softens an image using one of several filters.
Args:
src: The source mat (numpy.ndarray).
type: The blurType to perform represented as an int.
radius: The radius for the blur as a float.
Returns:
A numpy.ndarray that has been blurred.
"""
if(type is BlurType.Box_Blur):
ksize = int(2 * round(radius) + 1)
return cv2.blur(src, (ksize, ksize))
elif(type is BlurType.Gaussian_Blur):
ksize = int(6 * round(radius) + 1)
return cv2.GaussianBlur(src, (ksize, ksize), round(radius))
elif(type is BlurType.Median_Filter):
ksize = int(2 * round(radius) + 1)
return cv2.medianBlur(src, ksize)
else:
return cv2.bilateralFilter(src, -1, round(radius), round(radius))
@staticmethod
def __hsv_threshold(input, hue, sat, val):
"""Segment an image based on hue, saturation, and value ranges.
Args:
input: A BGR numpy.ndarray.
hue: A list of two numbers the are the min and max hue.
sat: A list of two numbers the are the min and max saturation.
lum: A list of two numbers the are the min and max value.
Returns:
A black and white numpy.ndarray.
"""
out = cv2.cvtColor(input, cv2.COLOR_BGR2HSV)
return cv2.inRange(out, (hue[0], sat[0], val[0]), (hue[1], sat[1], val[1]))
@staticmethod
def __find_contours(input, external_only):
"""Sets the values of pixels in a binary image to their distance to the nearest black pixel.
Args:
input: A numpy.ndarray.
external_only: A boolean. If true only external contours are found.
Return:
A list of numpy.ndarray where each one represents a contour.
"""
if(external_only):
mode = cv2.RETR_EXTERNAL
else:
mode = cv2.RETR_LIST
method = cv2.CHAIN_APPROX_SIMPLE
im2, contours, hierarchy =cv2.findContours(input, mode=mode, method=method)
return contours
@staticmethod
def __filter_contours(input_contours, min_area, min_perimeter, min_width, max_width,
min_height, max_height, solidity, max_vertex_count, min_vertex_count,
min_ratio, max_ratio):
"""Filters out contours that do not meet certain criteria.
Args:
input_contours: Contours as a list of numpy.ndarray.
min_area: The minimum area of a contour that will be kept.
min_perimeter: The minimum perimeter of a contour that will be kept.
min_width: Minimum width of a contour.
max_width: MaxWidth maximum width.
min_height: Minimum height.
max_height: Maximimum height.
solidity: The minimum and maximum solidity of a contour.
min_vertex_count: Minimum vertex Count of the contours.
max_vertex_count: Maximum vertex Count.
min_ratio: Minimum ratio of width to height.
max_ratio: Maximum ratio of width to height.
Returns:
Contours as a list of numpy.ndarray.
"""
output = []
for contour in input_contours:
x,y,w,h = cv2.boundingRect(contour)
if (w < min_width or w > max_width):
continue
if (h < min_height or h > max_height):
continue
area = cv2.contourArea(contour)
if (area < min_area):
continue
if (cv2.arcLength(contour, True) < min_perimeter):
continue
hull = cv2.convexHull(contour)
solid = 100 * area / cv2.contourArea(hull)
if (solid < solidity[0] or solid > solidity[1]):
continue
if (len(contour) < min_vertex_count or len(contour) > max_vertex_count):
continue
ratio = (float)(w) / h
if (ratio < min_ratio or ratio > max_ratio):
continue
output.append(contour)
return output
BlurType = Enum('BlurType', 'Box_Blur Gaussian_Blur Median_Filter Bilateral_Filter')
我知道这很长，但是我对 Python 的熟悉不如其他语言，所以我想提供所有这些，以防 Python 经验更多的人可能能够发现其中的一些错误。
这是我为获取图像并将其输入管道而编写的代码：
import numpy
import math
import cv2
import urllib.request
from enum import Enum
from GripPipeline import GripPipeline
from networktables import NetworkTable
frame = cv2.VideoCapture('https://10.17.11.1')
pipeline = GripPipeline()
def get_image()
img_array = numpy.asarray(bytearray(frame.grab()))
return img_array
while True:
img = get_image()
pipeline.process(img) #where the Segmentation Fault occurs
有没有人知道可能导致此问题的原因或解决方法？
编辑：事实证明错误来自 process 方法第二行中的某些内容，但我仍然不知道是什么。如果有人发现那里的调用有任何缺陷，请告诉我。
尝试按照 tutorial 的建议获取帧。注意将 frame 重命名为 cap:
cap = cv2.VideoCapture('https://10.17.11.1')
pipeline = GripPipeline()
while True:
ret, img = cap.read()
pipeline.process(img)
我正在使用 Python 通过以太网连接从 IP 摄像机获取图像，然后处理它们以寻找特定目标。我正在使用 GRIP 生成代码来寻找特定的目标区域。 （对于那些不熟悉 GRIP 的人：它基本上为您提供了一个 GUI 桌面界面，您可以在其中查看实时视频源并更改参数，直到获得所需的输出。然后您可以自动生成一段代码——我的代码在 Python—这将对您在代码中输入的任何图像执行该处理 'pipeline'）。
在对我的连接代码进行了大量调试之后，我终于获得了一个成功的工作连接，它从 IP 摄像机获取图像并将其发送到 GRIP 管道中。但是，图像处理失败，它返回一个分段错误，没有指示行号。这是管道代码（自动生成）：
import cv2
import numpy
import math
from enum import Enum
class GripPipeline:
"""
An OpenCV pipeline generated by GRIP.
"""
def __init__(self):
"""initializes all values to presets or None if need to be set
"""
self.__blur_type = BlurType.Median_Filter
self.__blur_radius = 19.81981981981982
self.blur_output = None
self.__hsv_threshold_input = self.blur_output
self.__hsv_threshold_hue = [72.84172661870504, 86.31399317406144]
self.__hsv_threshold_saturation = [199.50539568345323, 255.0]
self.__hsv_threshold_value = [89.43345323741006, 255.0]
self.hsv_threshold_output = None
self.__find_contours_input = self.hsv_threshold_output
self.__find_contours_external_only = False
self.find_contours_output = None
self.__filter_contours_contours = self.find_contours_output
self.__filter_contours_min_area = 500.0
self.__filter_contours_min_perimeter = 0.0
self.__filter_contours_min_width = 0.0
self.__filter_contours_max_width = 1000.0
self.__filter_contours_min_height = 0.0
self.__filter_contours_max_height = 1000.0
self.__filter_contours_solidity = [0, 100]
self.__filter_contours_max_vertices = 1000000.0
self.__filter_contours_min_vertices = 0.0
self.__filter_contours_min_ratio = 0.0
self.__filter_contours_max_ratio = 1000.0
self.filter_contours_output = None
def process(self, source0):
"""
Runs the pipeline and sets all outputs to new values.
"""
# Step Blur0:
self.__blur_input = source0
(self.blur_output) = self.__blur(self.__blur_input, self.__blur_type, self.__blur_radius)
# Step HSV_Threshold0:
self.__hsv_threshold_input = self.blur_output
(self.hsv_threshold_output) = self.__hsv_threshold(self.__hsv_threshold_input, self.__hsv_threshold_hue, self.__hsv_threshold_saturation, self.__hsv_threshold_value)
# Step Find_Contours0:
self.__find_contours_input = self.hsv_threshold_output
(self.find_contours_output) = self.__find_contours(self.__find_contours_input, self.__find_contours_external_only)
# Step Filter_Contours0:
self.__filter_contours_contours = self.find_contours_output
(self.filter_contours_output) = self.__filter_contours(self.__filter_contours_contours, self.__filter_contours_min_area, self.__filter_contours_min_perimeter, self.__filter_contours_min_width, self.__filter_contours_max_width, self.__filter_contours_min_height, self.__filter_contours_max_height, self.__filter_contours_solidity, self.__filter_contours_max_vertices, self.__filter_contours_min_vertices, self.__filter_contours_min_ratio, self.__filter_contours_max_ratio)
@staticmethod
def __blur(src, type, radius):
"""Softens an image using one of several filters.
Args:
src: The source mat (numpy.ndarray).
type: The blurType to perform represented as an int.
radius: The radius for the blur as a float.
Returns:
A numpy.ndarray that has been blurred.
"""
if(type is BlurType.Box_Blur):
ksize = int(2 * round(radius) + 1)
return cv2.blur(src, (ksize, ksize))
elif(type is BlurType.Gaussian_Blur):
ksize = int(6 * round(radius) + 1)
return cv2.GaussianBlur(src, (ksize, ksize), round(radius))
elif(type is BlurType.Median_Filter):
ksize = int(2 * round(radius) + 1)
return cv2.medianBlur(src, ksize)
else:
return cv2.bilateralFilter(src, -1, round(radius), round(radius))
@staticmethod
def __hsv_threshold(input, hue, sat, val):
"""Segment an image based on hue, saturation, and value ranges.
Args:
input: A BGR numpy.ndarray.
hue: A list of two numbers the are the min and max hue.
sat: A list of two numbers the are the min and max saturation.
lum: A list of two numbers the are the min and max value.
Returns:
A black and white numpy.ndarray.
"""
out = cv2.cvtColor(input, cv2.COLOR_BGR2HSV)
return cv2.inRange(out, (hue[0], sat[0], val[0]), (hue[1], sat[1], val[1]))
@staticmethod
def __find_contours(input, external_only):
"""Sets the values of pixels in a binary image to their distance to the nearest black pixel.
Args:
input: A numpy.ndarray.
external_only: A boolean. If true only external contours are found.
Return:
A list of numpy.ndarray where each one represents a contour.
"""
if(external_only):
mode = cv2.RETR_EXTERNAL
else:
mode = cv2.RETR_LIST
method = cv2.CHAIN_APPROX_SIMPLE
im2, contours, hierarchy =cv2.findContours(input, mode=mode, method=method)
return contours
@staticmethod
def __filter_contours(input_contours, min_area, min_perimeter, min_width, max_width,
min_height, max_height, solidity, max_vertex_count, min_vertex_count,
min_ratio, max_ratio):
"""Filters out contours that do not meet certain criteria.
Args:
input_contours: Contours as a list of numpy.ndarray.
min_area: The minimum area of a contour that will be kept.
min_perimeter: The minimum perimeter of a contour that will be kept.
min_width: Minimum width of a contour.
max_width: MaxWidth maximum width.
min_height: Minimum height.
max_height: Maximimum height.
solidity: The minimum and maximum solidity of a contour.
min_vertex_count: Minimum vertex Count of the contours.
max_vertex_count: Maximum vertex Count.
min_ratio: Minimum ratio of width to height.
max_ratio: Maximum ratio of width to height.
Returns:
Contours as a list of numpy.ndarray.
"""
output = []
for contour in input_contours:
x,y,w,h = cv2.boundingRect(contour)
if (w < min_width or w > max_width):
continue
if (h < min_height or h > max_height):
continue
area = cv2.contourArea(contour)
if (area < min_area):
continue
if (cv2.arcLength(contour, True) < min_perimeter):
continue
hull = cv2.convexHull(contour)
solid = 100 * area / cv2.contourArea(hull)
if (solid < solidity[0] or solid > solidity[1]):
continue
if (len(contour) < min_vertex_count or len(contour) > max_vertex_count):
continue
ratio = (float)(w) / h
if (ratio < min_ratio or ratio > max_ratio):
continue
output.append(contour)
return output
BlurType = Enum('BlurType', 'Box_Blur Gaussian_Blur Median_Filter Bilateral_Filter')
我知道这很长，但是我对 Python 的熟悉不如其他语言，所以我想提供所有这些，以防 Python 经验更多的人可能能够发现其中的一些错误。
这是我为获取图像并将其输入管道而编写的代码：
import numpy
import math
import cv2
import urllib.request
from enum import Enum
from GripPipeline import GripPipeline
from networktables import NetworkTable
frame = cv2.VideoCapture('https://10.17.11.1')
pipeline = GripPipeline()
def get_image()
img_array = numpy.asarray(bytearray(frame.grab()))
return img_array
while True:
img = get_image()
pipeline.process(img) #where the Segmentation Fault occurs
有没有人知道可能导致此问题的原因或解决方法？
编辑：事实证明错误来自 process 方法第二行中的某些内容，但我仍然不知道是什么。如果有人发现那里的调用有任何缺陷，请告诉我。
尝试按照 tutorial 的建议获取帧。注意将 frame 重命名为 cap:
cap = cv2.VideoCapture('https://10.17.11.1')
pipeline = GripPipeline()
while True:
ret, img = cap.read()
pipeline.process(img)