# 需要导入模块: import cv2 [as 别名]# 或者: from cv2 importADAPTIVE_THRESH_GAUSSIAN_C[as 别名]defpre_process_image(img, skip_dilate=False):"""Uses a blurring function, adaptive thresholding and dilation to expose the main features of an image."""# Gaussian blur with a kernal size (heig...
Normally, it is positive but may be zero or negative as well. In your example you set C to 2: gaussC = cv2.adaptiveThreshold(imgBlurred, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2) C=2C=8 As you can see, you need to play with blockSize and C parameters...
cv2.ADAPTIVE_THRESH_MEAN_C : threshold value is the mean of neighbourhood area. cv2.ADAPTIVE_THRESH_GAUSSIAN_C : threshold value is the weighted sum of neighbourhood values where weights are a gaussian window.Otsu’s BinarizationIn global thresholding, we used an arbitrary value for threshold ...
When i do adaptive thresholding with : adaptiveThreshold(image, image,255,ADAPTIVE_THRESH_GAUSSIAN_C, CV_THRESH_BINARY,15,-5); i get : Which looks like edge detection and not thresholding. What i expected was black and white areas . So my question is, why does this look like ed...
cv2.ADAPTIVE_THRESH_GAUSSIAN_C:采用高斯加权平均值作为阈值。 thresholdType:阈值类型,有两种可供选择: cv2.THRESH_BINARY:超过阈值部分取maxValue,否则取0。 cv2.THRESH_BINARY_INV:超过阈值部分取0,否则取maxValue。 blockSize:局部块的大小,必须是奇数。 C:减去阈值的常数,防止阈值太小或太大而导致过度二值化...
intthreshold_type=CV_THRESH_BINARY, int block_size=3, double param1=5 ); 参数: src-输入图像. dst-输出图像. max_value-使用 CV_THRESH_BINARY 和CV_THRESH_BINARY_INV 的最大值. adaptive_method-自适应阈值算法使用:CV_ADAPTIVE_THRESH_MEAN_C 或 CV_ADAPTIVE_THRESH_GAUSSIAN_C . ...
adaptiveMethod: 自适应阈值处理的方法,常用cv2.ADAPTIVE_THRESH_MEAN_C和cv2.ADAPTIVE_THRESH_GAUSSIAN_C两种。分别表示计算像素邻域内均值或者高斯加权均值。 thresholdType: 二值化处理的类型,常用cv2.THRESH_BINARY和cv2.THRESH_BINARY_INV两种。分别表示二值化或反转二值化。
threshold_type=CV_THRESH_BINARY_INV: dst(x,y)=0,ifsrc(x,y)>T(x,y) max_value,otherwise 其中TI是为每一个象素点单独计算的阈值 对方法CV_ADAPTIVE_THRESH_MEAN_C，先求出块中的均值，再减掉param1。 对方法CV_ADAPTIVE_THRESH_GAUSSIAN_C，先求出块中的加权和(gaussian)，再减掉param1。 /*...
cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 21, 4) cv2.imshow("Gaussian Adaptive Thresholding", thresh) cv2.waitKey(0) This time we are computing the weighted Gaussian mean over the21×21area, which gives larger weight to pixels closer to the center of the window. We then set...
binarilized =threshold_adaptive(denoised, block_size=13, method='gaussian') feature = binarilized.reshape(1,400)[0] X.append(feature) X = np.array(X)returnX 开发者ID:pigeatshit,项目名称:Kaggle,代码行数:10,代码来源:stacking.py 示例15: image_to_scan_bird_style_view ...