python opencv卡尺测量边缘距离
【摘要】
opencv 卡尺法 测量边缘距离 参考来源 ：https://github.com/crackwitz/metrology-demo
前言 一、测量方法 二、测量步骤 1.获取直线的像素 2.高斯滤波平滑曲线 3.计算跳变幅度值 4.计算距离值 5.显示和保存图片 总结 前言 halcon中有按照直线找边缘测量距离的工具，但是...
opencv 卡尺法 测量边缘距离
参考来源 ：https://github.com/crackwitz/metrology-demo
前言
一、测量方法
二、测量步骤
1.获取直线的像素
2.高斯滤波平滑曲线
3.计算跳变幅度值
4.计算距离值
5.显示和保存图片
总结
前言
halcon中有按照直线找边缘测量距离的工具，但是opencv中没有类似的工具可以直接实现该测量方式，参考网上的实现方式，可以实现。
测量的效果贴图
一、测量方法
根据测量线的两个端点，获取直线的像素，然后进行滤波过滤噪点，计算跳变幅度，获取最大最小值的位置，计算距离
二、测量步骤
1.获取直线的像素
将直线的通过仿射变换转成1D的图像，可以采用亚像素的算法，先获取仿射变换的矩阵
代码如下（示例）：
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def build_transform(p0, p1, stride=None, nsamples=None):
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"builds an affine transform with x+ along defined line"
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# use one of stride (in pixels) or nsamples (absolute value)
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(x0, y0) = p0
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(x1, y1) = p1
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dx = x1 - x0
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dy = y1 - y0
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length = np.hypot(dx, dy)
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if nsamples is not None:
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#stride = length / nsamples
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factor = 1 / nsamples
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else:
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if stride is None:
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stride = 1.0
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factor = stride / length
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nsamples = int(round(length / stride))
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# map: src <- dst (use WARP_INVERSE_MAP flag for warpAffine)
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H = np.eye(3, dtype=np.float64) # homography
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H[0:2, 0] = (dx, dy) # x unit vector
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H[0:2, 1] = (-dy, dx) # y unit vector is x rotated by 90 degrees
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x=H[0:2, 0:2]
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H[0:2, 0:2] *= factor
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H[0:2, 2] = (x0, y0) # translate onto starting point
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# take affine part of homography
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assert np.isclose(a=H[2], b=(0,0,1)).all() # we didn't touch those but let's better check
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A = H[0:2, :]
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return (nsamples, A)
然后再采用变换的方法获取图像的像素值
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def sample_opencv(im, M, nsamples):
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# use transform to get samples
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# available: INTER_{NEAREST,LINEAR,AREA,CUBIC,LANCOS4)
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samples = cv.warpAffine(im, M=M, dsize=(nsamples, 1), flags=cv.WARP_INVERSE_MAP | cv.INTER_CUBIC )
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# flatten row vector
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samples.shape = (-1,)
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# INTER_CUBIC seems to break down beyond 1/32 sampling (discretizes).
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# there might be fixed point algorithms at work
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return samples
2.高斯滤波平滑曲线
samples = scipy.ndimage.gaussian_filter1d(samples, sigma=args.sigma / args.stride)
1
3.计算跳变幅度值
# off-by-half in position because for values [0,1,1,0] this returns [+1,0,-1]
gradient = np.diff(samples) / args.stride
4.计算距离值
i_falling = np.argmin(gradient) # in samples
i_rising = np.argmax(gradient) # in samples
distance = np.abs(i_rising - i_falling) * args.stride # in pixels
完整代码：
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#!/usr/bin/env python3
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import sys
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import argparse
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import numpy as np
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import cv2
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import scipy.ndimage
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### "business logic" ###################################################
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def build_transform(p0, p1, stride=None, nsamples=None):
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"builds an affine transform with x+ along defined line"
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# use one of stride (in pixels) or nsamples (absolute value)
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(x0, y0) = p0
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(x1, y1) = p1
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dx = x1 - x0
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dy = y1 - y0
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length = np.hypot(dx, dy)
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if nsamples is not None:
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# stride = length / nsamples
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factor = 1 / nsamples
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else:
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if stride is None:
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stride = 1.0
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factor = stride / length
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nsamples = int(round(length / stride))
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# map: src <- dst (use WARP_INVERSE_MAP flag for warpAffine)
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H = np.eye(3, dtype=np.float64) # homography
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H[0:2, 0] = (dx, dy) # x unit vector
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H[0:2, 1] = (-dy, dx) # y unit vector is x rotated by 90 degrees
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H[0:2, 0:2] *= factor
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H[0:2, 2] = (x0, y0) # translate onto starting point
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# take affine part of homography
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assert np.isclose(a=H[2], b=(0, 0, 1)).all() # we didn't touch those but let's better check
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A = H[0:2, :]
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return (nsamples, A)
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def sample_opencv(im, M, nsamples):
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# use transform to get samples
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# available: INTER_{NEAREST,LINEAR,AREA,CUBIC,LANCOS4)
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samples = cv2.warpAffine(im, M=M, dsize=(nsamples, 1), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC)
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# flatten row vector
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samples.shape = (-1,)
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# INTER_CUBIC seems to break down beyond 1/32 sampling (discretizes).
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# there might be fixed point algorithms at work
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return samples
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def sample_scipy(im, M, nsamples):
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# coordinates to this function are (i,j) = (y,x)
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# I could permute first and second rows+columns of M, or transpose input+output
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Mp = M.copy()
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Mp[(0, 1), :] = Mp[(1, 0), :] # permute rows
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Mp[:, (0, 1)] = Mp[:, (1, 0)] # permute columns
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samples = scipy.ndimage.interpolation.affine_transform(input=im, matrix=Mp, output_shape=(1, nsamples), order=2,
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# 1: linear (C0, f' is piecewise constant), 2: C1 (f' is piecewise linear), 3: C2... https://en.wikipedia.org/wiki/Smoothness
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mode='nearest' # border handling
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)
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# flatten row vector
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samples.shape = (-1,)
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return samples
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### command line parsing utility functions #############################
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def parse_linestr(arg):
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pieces = arg.split(",")
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pieces = [float(el) for el in pieces]
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x0, y0, x1, y1 = pieces
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return ((x0, y0), (x1, y1))
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def parse_bool(arg):
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if isinstance(arg, bool):
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return arg
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if arg.lower() in ('yes', 'true', 't', 'y', '1'):
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return True
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elif arg.lower() in ('no', 'false', 'f', 'n', '0'):
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return False
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else:
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raise argparse.ArgumentTypeError(f'Boolean value expected, got {arg!r} instead')
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def parse_float(arg):
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import ast
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if '/' in arg:
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num, denom = arg.split('/', 1)
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num = ast.literal_eval(num)
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denom = ast.literal_eval(denom)
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result = num / denom
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else:
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result = ast.literal_eval(arg)
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return result
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### main... ############################################################
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if __name__ == '__main__':
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# command line argument parsing
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# change defaults here
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parser = argparse.ArgumentParser()
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parser.add_argument("--picture", dest="fname", metavar="PATH", type=str, default="dish-1.jpg", help="path to picture file")
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parser.add_argument("--invert", type=parse_bool, default=True, metavar="BOOL", help="invert picture (cosmetic; distance between gradient extrema is absolute)")
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parser.add_argument("--line", type=parse_linestr, default=((1320, 2500), (1320, 2100)), metavar="X0,Y0,X1,Y1", help="line to sample on")
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parser.add_argument("--stride", type=parse_float, default=1 / 4, metavar="PX", help="stride in pixels to sample along line, fractions supported")
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parser.add_argument("--method", type=lambda s: s.lower(), default="opencv", help="sampling methods: SciPy (slower, smoother, default), OpenCV (faster, less smooth)")
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parser.add_argument("--sigma", type=float, default=2.0, metavar="PX", help="sigma for gaussian lowpass on sampled signal, before gradient is calculated")
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parser.add_argument("--verbose", type=parse_bool, default=True, metavar="BOOL", help="chatty or not")
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parser.add_argument("--display", type=parse_bool, default=True, metavar="BOOL", help="draw some plots")
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parser.add_argument("--saveplot", type=str, default="plot.png", metavar="PATH", help="save a picture (use '--saveplot=' to disable)")
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args = parser.parse_args()
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########## here be dragons ##########
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if args.stride > 1:
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print(f"WARNING: stride should be <= 1, is {args.stride}")
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stride_decimals = max(0, int(np.ceil(-np.log10(args.stride))))
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if args.verbose: print("loading picture...", end=" ", flush=True)
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im = cv2.imread(args.fname, cv2.IMREAD_GRAYSCALE)
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imh, imw = im.shape[:2]
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if args.invert:
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im = 255 - im # invert
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im = im.astype(np.float32) # * np.float32(1/255)
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if args.verbose: print("done")
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# build transform
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p0, p1 = args.line
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nsamples, M = build_transform(p0, p1, stride=args.stride)
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if args.verbose: print(f"taking {nsamples} samples along line {p0} -> {p1}...", end=" ", flush=True)
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# pick one
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if args.method == 'opencv':
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samples = sample_opencv(im, M, nsamples) # does "normal" cubic (4x4 support points, continuous first derivative)
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elif args.method == 'scipy':
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samples = sample_scipy(im, M, nsamples) # does some fancy "cubic" with continuous higher derivatives
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else:
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assert False, "method needs to be opencv or scipy"
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if args.verbose: print("sampling done")
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# smoothing to remove noise
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if args.sigma > 0:
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if args.verbose: print(f"lowpass filtering with sigma = {args.sigma} px...", end=" ", flush=True)
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samples = scipy.ndimage.gaussian_filter1d(samples, sigma=args.sigma / args.stride)
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if args.verbose: print("done")
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# off-by-half in position because for values [0,1,1,0] this returns [+1,0,-1]
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gradient = np.diff(samples) / args.stride
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i_falling = np.argmin(gradient) # in samples
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i_rising = np.argmax(gradient) # in samples
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distance = np.abs(i_rising - i_falling) * args.stride # in pixels
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if args.verbose:
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print(f"distance: {distance:.{stride_decimals}f} pixels")
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else:
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print(distance)
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# this was the result. algorithm is done.
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# now follows displaying code
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if args.display:
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gradient *= 255 / np.abs(gradient).max()
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# plot signal
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plot = cv2.plot.Plot2d_create(np.arange(nsamples, dtype=np.float64), samples.astype(np.float64))
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plot.setMinY(256 + 32)
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plot.setMaxY(-32)
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plot.setMinX(0)
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plot.setMaxX(nsamples)
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plot.setGridLinesNumber(5)
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plot.setShowText(False) # callout for specific point, setPointIdxToPrint(index)
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plot.setPlotGridColor((64,) * 3)
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canvas1 = plot.render()
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# plot gradient
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plot = cv2.plot.Plot2d_create(np.arange(nsamples - 1) + 0.5, gradient.astype(np.float64))
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plot.setMinY(256 + 64)
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plot.setMaxY(-256 - 64)
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plot.setMinX(0)
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plot.setMaxX(nsamples)
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plot.setGridLinesNumber(5)
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plot.setShowText(False) # callout for specific point, setPointIdxToPrint(index)
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plot.setPlotGridColor((64,) * 3)
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canvas2 = plot.render()
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# arrange vertically
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canvas = np.vstack([canvas1, canvas2]) # 600 wide, 800 tall
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# draw lines at edges (largest gradients)
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# plots are 600x400 pixels... and there's no way to plot multiple or plot lines in "plot space"
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px_falling = int(600 * (i_falling + 0.5) / nsamples)
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px_rising = int(600 * (i_rising + 0.5) / nsamples)
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cv2.line(canvas, (px_falling, 0), (px_falling, 400 * 2), color=(255, 0, 0))
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cv2.line(canvas, (px_rising, 0), (px_rising, 400 * 2), color=(255, 0, 0))
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# some text to describe the picture
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cv2.putText(canvas, f"{nsamples * args.stride:.0f} px, {p0} -> {p1}", (10, 350), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (255, 255, 255), thickness=1, lineType=cv2.LINE_AA)
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cv2.putText(canvas, f"stride {args.stride} px, {nsamples} samples, sigma {args.sigma}", (10, 350 + 35), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (255, 255, 255), thickness=1, lineType=cv2.LINE_AA)
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cv2.putText(canvas, f"distance: {distance:.{stride_decimals}f} px", (10, 350 + 70), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (255, 255, 255), thickness=1, lineType=cv2.LINE_AA)
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# save for posterity
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if args.saveplot:
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cv2.imwrite(args.saveplot, canvas)
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if args.display:
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cv2.imshow("plot", canvas)
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if args.verbose:
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print("press Ctrl+C in the terminal, or press any key while the imshow() window is focused")
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while True:
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keycode = cv2.waitKey(100)
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if keycode == -1:
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continue
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# some key...
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if args.verbose:
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print(f"keycode: {keycode}")
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cv2.destroyAllWindows()
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break
总结
提示：显示的程序包含了opencv pilo，这个需要引入opencv-contrib-python模块：
原文链接：https://blog.csdn.net/hong3731/article/details/119649418
使用过程报错：
module 'cv2' has no attribute 'plot'
文章来源: blog.csdn.net，作者：AI视觉网奇，版权归原作者所有，如需转载，请联系作者。
原文链接：blog.csdn.net/jacke121/article/details/122118816
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