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opencv--基于深度学习的人脸检测器

python opencv 存储

首先，一直以来就在考虑这么牛逼的opencv该换一下里边一些过时的东西了，像：检测器、识别器等等，果不其然，openv的大佬们还是偷偷的换了。

2022-05-09

4370

anaconda + tensorflow +ubuntu

tensorflow python anaconda bash

用过一段时间的caffe后，对caffe有两点感受：1、速度确实快; 2、太不灵活了。

2022-05-09

3530

OpenCV之二值图像联通组件寻找

python代码： import cv2 as cv import numpy as np def connected_components_demo(src): src = cv.GaussianBlur(src, (3, 3), 0) gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY) ret, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)

2021-08-13

3250

OpenCV之图像二化自适应阈值算法

python代码： import cv2 as cv import numpy as np # # THRESH_BINARY = 0 # THRESH_BINARY_INV = 1 # THRESH_TRUNC = 2 # THRESH_TOZERO = 3 # THRESH_TOZERO_INV = 4 # src = cv.imread("./test.png") cv.namedWindow("input", cv.WINDOW_AUTOSIZE) cv.imshow("input", src)

2021-08-13

9420

OpenCV之图像二值寻找算法 – TRIANGLE

python代码： import cv2 as cv import numpy as np #import tensorflow as tf # tf.enable_eager_execution() # # THRESH_BINARY = 0 # THRESH_BINARY_INV = 1 # THRESH_TRUNC = 2 # THRESH_TOZERO = 3 # THRESH_TOZERO_INV = 4 # src = cv.imread("./test.png") cv.namedWindo

2021-08-13

3390

OpenCV之图像二值寻找算法 – OTSU

python代码： import cv2 as cv import numpy as np # # THRESH_BINARY = 0 # THRESH_BINARY_INV = 1 # THRESH_TRUNC = 2 # THRESH_TOZERO = 3 # THRESH_TOZERO_INV = 4 # src = cv.imread("./test.png") cv.namedWindow("input", cv.WINDOW_AUTOSIZE) cv.imshow("input", src) h

2021-08-13

3210

OpenCV之基本阈值操作

python代码： import cv2 as cv import numpy as np # # THRESH_BINARY = 0 # THRESH_BINARY_INV = 1 # THRESH_TRUNC = 2 # THRESH_TOZERO = 3 # THRESH_TOZERO_INV = 4 # src = cv.imread("./test.png") cv.namedWindow("input", cv.WINDOW_AUTOSIZE) cv.imshow("input", src)

2021-08-13

4650

OpenCV之二值图像介绍

python代码： import cv2 as cv import numpy as np src = cv.imread("./test.png") cv.namedWindow("input", cv.WINDOW_AUTOSIZE) cv.imshow("input", src) T = 127 # 转换为灰度图像 gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY) h, w = gray.shape T = cv.mean(gray)[0] print("cu

2021-08-13

2690

OpenCV之图像模板匹配

python代码： import cv2 as cv import numpy as np def template_demo(): src = cv.imread("./test.png") tpl = cv.imread("./test01.png") cv.imshow("input", src) cv.imshow("tpl", tpl) th, tw = tpl.shape[:2] result = cv.matchTemplate(src, t

2021-08-13

4250

OpenCV之拉普拉斯金字塔

python代码： import cv2 as cv import numpy as np def laplaian_demo(pyramid_images): level = len(pyramid_images) for i in range(level-1, -1, -1): if (i-1) < 0: h, w = src.shape[:2] expand = cv.pyrUp(pyramid_images[i],

2021-08-13

2230

OpenCV之图像二值化与去噪

python代码： import cv2 as cv import numpy as np def method_1(image): gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY) t, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY | cv.THRESH_OTSU) return binary def method_2(image): blurred = cv.Ga

2021-08-13

6280

OpenCV之图像金字塔

python代码： import cv2 as cv def pyramid_down(pyramid_images): level = len(pyramid_images) print("level = ",level) for i in range(level-1, -1, -1): expand = cv.pyrUp(pyramid_images[i]) cv.imshow("pyramid_down_"+str(i), expand)

2021-08-05

3840

OpenCV之Canny边缘检测器

python代码： import cv2 as cv import numpy as np src = cv.imread("./test.png") cv.namedWindow("input", cv.WINDOW_AUTOSIZE) cv.imshow("input", src) # t1 = 100, t2 = 3*t1 = 300 edge = cv.Canny(src, 100, 300) cv.imshow("mask image", edge) cv.imwrite("./edge.pn

2021-08-05

3160

OpenCV之USM 锐化增强算法

python代码： import cv2 as cv import numpy as np src = cv.imread("./test.png") cv.namedWindow("input", cv.WINDOW_AUTOSIZE) cv.imshow("input", src) # sigma = 5、15、25 blur_img = cv.GaussianBlur(src, (0, 0), 5) usm = cv.addWeighted(src, 1.5, blur_img, -0.5, 0)

2021-08-05

1K0

OpenCV之图像锐化

python代码： import cv2 as cv import numpy as np src = cv.imread("./test.png") cv.namedWindow("input", cv.WINDOW_AUTOSIZE) cv.imshow("input", src) # sharpen_op = np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]], dtype=np.float32) sharpen_op = np.array([[0

2021-07-27

7440

OpenCV之图像梯度 – 拉普拉斯算子(二阶导数算子)

python代码： import cv2 as cv import numpy as np image = cv.imread("./test.png") cv.namedWindow("input", cv.WINDOW_AUTOSIZE) cv.imshow("input", image) h, w = image.shape[:2] src = cv.GaussianBlur(image, (0, 0), 1) dst = cv.Laplacian(src, cv.CV_32F, ksize=3,

2021-07-27

6380

OpenCV之Sobel算子

python代码： import cv2 as cv import numpy as np src = cv.imread("./test.png") cv.namedWindow("input", cv.WINDOW_AUTOSIZE) cv.imshow("input", src) h, w = src.shape[:2] x_grad = cv.Sobel(src, cv.CV_32F, 1, 0) y_grad = cv.Sobel(src, cv.CV_32F, 0, 1) x_grad =

2021-07-23

2840

OpenCV之快速的图像边缘滤波算法

python代码： import cv2 as cv import numpy as np src = cv.imread("./test.png") cv.namedWindow("input", cv.WINDOW_AUTOSIZE) cv.imshow("input", src) h, w = src.shape[:2] dst = cv.edgePreservingFilter(src, sigma_s=100, sigma_r=0.4, flags=cv.RECURS_FILTER) resu

2021-07-23

3710

OpenCV之图像积分图算法

python代码： import cv2 as cv import numpy as np def get_block_sum(ii, x1, y1, x2, y2, index): tl = ii[y1, x1][index] tr = ii[y2, x1][index] bl = ii[y1, x2][index] br = ii[y2, x2][index] s = (br - bl - tr + tl) return s def blur_de

2021-07-23

2650

OpenCV之边缘保留滤波算法 – 均值迁移模糊(mean-shift blur)

python代码： import cv2 as cv import numpy as np src = cv.imread("./test.png") cv.namedWindow("input", cv.WINDOW_AUTOSIZE) cv.imshow("input", src) h, w = src.shape[:2] dst = cv.pyrMeanShiftFiltering(src, 15, 30, termcrit=(cv.TERM_CRITERIA_MAX_ITER+cv.TERM_C

2021-07-20

5970

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