建网站需要多大的宽带,怎么用源码做网站视频,怎么对一个产品进行网络营销,泰州网站制作推广瓶子或者柱面在做字符识别的时候由于变形#xff0c;识别效果是很不好的 或者是检测瓶子表面缺陷的时候效果也没有展平的好 下面介绍两个项目#xff0c;关于曲面#xff08;弧面、柱面#xff09;展平#xff08;拉直#xff09; 项目一#xff1a;通过识别曲面的6个点… 瓶子或者柱面在做字符识别的时候由于变形识别效果是很不好的 或者是检测瓶子表面缺陷的时候效果也没有展平的好 下面介绍两个项目关于曲面弧面、柱面展平拉直 项目一通过识别曲面的6个点展开
图1
如 图1所示通过A、B、C、D、E、F六个点对柱面展成平面对应公式可参考下图图2
用这个项目的前提是需要找到这6个点和通过机器视觉的算法或者是深度学习的算法来实现。 图2
1、 项目一代码
import cv2
import numpy as npBLACK_COLOR (0, 0, 0)
WHITE_COLOR (255, 255, 255)
YELLOW_COLOR (0, 255, 255)
RED_COLOR (0, 0, 255)class Line(object):def __init__(self, point1, point2):For line formula y(x) k * x b, calc k and b paramsIf the line is vertical, set vertical attr to True and save x position of the lineself.point1 point1self.point2 point2self.vertical Falseself.fixed_x Noneself.k Noneself.b None# cached angle propsself.angle Noneself.angle_cos Noneself.angle_sin Noneself.set_line_props(point1, point2)def is_vertical(self):return self.verticaldef set_line_props(self, point1, point2):if point2[0] - point1[0]:self.k float(point2[1] - point1[1]) / (point2[0] - point1[0])self.b point2[1] - self.k * point2[0]k_normal - 1 / self.kelse:self.vertical Trueself.fixed_x point2[0]k_normal 0self.angle np.arctan(k_normal)self.angle_cos np.cos(self.angle)self.angle_sin np.sin(self.angle)def get_x(self, y):if self.is_vertical():return self.fixed_xelse:return int(round(float(y - self.b) / self.k))def get_y(self, x):return self.k * x self.bclass LabelUnwrapper(object):COL_COUNT 30ROW_COUNT 20def __init__(self, src_imageNone, pixel_pointsNone, percent_pointsNone):Point lists are lists of 6 points - [A, B, C, D, E, F]:param pixel_points: List[Tuple] Points, whose coordinates specified as pixels:param percent_points: List[Tuple] Points, whose coordinates specified as fraction of image width/heightIn both cases points represent figure below:| | | || B | A C| / \ | | \ / |A C | B || | | || | OR | || | | |F D F D| \ / | | \ / || E | | E || | | |So, A-B-C-D-E-F-A polygon represent raw wine label on bottleself.src_image src_imageself.width self.src_image.shape[1]self.height src_image.shape[0]self.dst_image Noneself.points pixel_pointsself.percent_points percent_pointsself.point_a None # top leftself.point_b None # top centerself.point_c None # top rightself.point_d None # bottom rightself.point_e None # bottom centerself.point_f None # bottom leftself.center_line Noneself.load_points()def load_points(self):if self.points is None:points []for point in self.percent_points:x int(point[0] * self.width)y int(point[1] * self.height)points.append((x, y))self.points pointsself.points np.array(self.points)(self.point_a, self.point_b, self.point_c,self.point_d, self.point_e, self.point_f) self.pointscenter_top (self.point_a self.point_c) / 2center_bottom (self.point_d self.point_f) / 2self.center_line Line(center_bottom, center_top)if not len(self.points) 6:raise ValueError(Points should be an array of 6 elements)def unwrap(self, interpolateFalse):source_map self.calc_source_map()if interpolate:self.unwrap_label_interpolation(source_map)else:self.unwrap_label_perspective(source_map)return self.dst_imagedef calc_dest_map(self):width, height self.get_label_size()dx float(width) / (self.COL_COUNT - 1)dy float(height) / (self.ROW_COUNT - 1)rows []for row_index in range(self.ROW_COUNT):row []for col_index in range(self.COL_COUNT):row.append([int(dx * col_index),int(dy * row_index)])rows.append(row)return np.array(rows)def unwrap_label_interpolation(self, source_map):Unwrap label using interpolation - more accurate method in terms of qualityfrom scipy.interpolate import griddatawidth, height self.get_label_size()dest_map self.calc_dest_map()grid_x, grid_y np.mgrid[0:width - 1:width * 1j, 0:height - 1:height * 1j]destination dest_map.reshape(dest_map.size // 2, 2)source source_map.reshape(source_map.size // 2, 2)grid_z griddata(destination, source, (grid_x, grid_y), methodcubic)map_x np.append([], [ar[:, 0] for ar in grid_z]).reshape(width, height)map_y np.append([], [ar[:, 1] for ar in grid_z]).reshape(width, height)map_x_32 map_x.astype(float32)map_y_32 map_y.astype(float32)warped cv2.remap(self.src_image, map_x_32, map_y_32, cv2.INTER_CUBIC)self.dst_image cv2.transpose(warped)def unwrap_label_perspective(self, source_map):Unwrap label using transform, unlike unwrap_label_interpolation doesnt require scipywidth, height self.get_label_size()self.dst_image np.zeros((height, width, 3), np.uint8)dx float(width) / (self.COL_COUNT - 1)dy float(height) / (self.ROW_COUNT - 1)dx_int int(np.ceil(dx))dy_int int(np.ceil(dy))for row_index in range(self.ROW_COUNT - 1):for col_index in range(self.COL_COUNT - 1):src_cell (source_map[row_index][col_index],source_map[row_index][col_index 1],source_map[row_index 1][col_index],source_map[row_index 1][col_index 1])dst_cell np.int32([[0, 0], [dx, 0], [0, dy], [dx, dy]])M cv2.getPerspectiveTransform(np.float32(src_cell), np.float32(dst_cell))dst cv2.warpPerspective(self.src_image, M, (dx_int, dy_int))x_offset int(dx * col_index)y_offset int(dy * row_index)self.dst_image[y_offset:y_offset dy_int,x_offset:x_offset dx_int] dstdef get_roi_rect(self, points):max_x min_x points[0][0]max_y min_y points[0][1]for point in points:x, y pointif x max_x:max_x xif x min_x:min_x xif y max_y:max_y yif y min_y:min_y yreturn np.array([[min_x, min_y],[max_x, min_y],[max_x, max_y],[min_x, max_y]])def get_roi(self, image, points):rect self.get_roi_rect(points)return image[np.floor(rect[0][1]):np.ceil(rect[2][1]),np.floor(rect[0][0]):np.ceil(rect[1][0])]def calc_source_map(self):top_points self.calc_ellipse_points(self.point_a, self.point_b, self.point_c,self.COL_COUNT)bottom_points self.calc_ellipse_points(self.point_f, self.point_e, self.point_d,self.COL_COUNT)rows []for row_index in range(self.ROW_COUNT):row []for col_index in range(self.COL_COUNT):top_point top_points[col_index]bottom_point bottom_points[col_index]delta (top_point - bottom_point) / float(self.ROW_COUNT - 1)point top_point - delta * row_indexrow.append(point)rows.append(row)return np.array(rows)def draw_mesh(self, colorRED_COLOR, thickness1):mesh self.calc_source_map()for row in mesh:for x, y in row:point (int(round(x)), int(round(y)))cv2.line(self.src_image, point, point, colorcolor, thicknessthickness)def draw_poly_mask(self, colorWHITE_COLOR):cv2.polylines(self.src_image, np.int32([self.points]), 1, color)def draw_mask(self, colorWHITE_COLOR, thickness1, imgNone):Draw mask, if image not specified - draw to source imageif img is None:img self.src_imagecv2.line(img, tuple(self.point_f.tolist()), tuple(self.point_a.tolist()), color, thickness)cv2.line(img, tuple(self.point_c.tolist()), tuple(self.point_d.tolist()), color, thickness)self.draw_ellipse(img, self.point_a, self.point_b, self.point_c, color, thickness)self.draw_ellipse(img, self.point_d, self.point_e, self.point_f, color, thickness)def get_label_contour(self, colorWHITE_COLOR, thickness1):mask np.zeros(self.src_image.shape)self.draw_mask(color, thickness, mask)return maskdef get_label_mask(self):Generate mask of the label, fully covering itmask np.zeros(self.src_image.shape)pts np.array([[self.point_a, self.point_c, self.point_d, self.point_f]])cv2.fillPoly(mask, pts, WHITE_COLOR)self.draw_filled_ellipse(mask, self.point_a, self.point_b, self.point_c, True)self.draw_filled_ellipse(mask, self.point_f, self.point_e, self.point_d, False)return maskdef draw_ellipse(self, img, left, top, right, colorWHITE_COLOR, thickness1):Draw ellipse using opencv functionis_arc, center_point, axis, angle self.get_ellipse_params(left, top, right)if is_arc:start_angle, end_angle 0, 180else:start_angle, end_angle 180, 360cv2.ellipse(img, center_point, axis, angle, start_angle, end_angle, color, thickness)def draw_filled_ellipse(self, img, left, top, right, is_topFalse):is_arc, center_point, axis, angle self.get_ellipse_params(left, top, right)if is_arc ^ is_top:color WHITE_COLORelse:color BLACK_COLORcv2.ellipse(img, center_point, axis, angle, 0, 360, colorcolor, thickness-1)def get_ellipse_params(self, left, top, right):center (left right) / 2center_point tuple(map(lambda x: int(np.round(x)), center.tolist()))axis (int(np.linalg.norm(left - right) / 2), int(np.linalg.norm(center - top)))x, y left - rightangle np.arctan(float(y) / x) * 57.296is_arc Falseif (top - center)[1] 0:is_arc Truereturn is_arc, center_point, axis, angledef calc_ellipse_points(self, left, top, right, points_count):center (left right) / 2# get ellipse axisa np.linalg.norm(left - right) / 2b np.linalg.norm(center - top)# get start and end anglesif (top - center)[1] 0:delta np.pi / (points_count - 1)else:delta - np.pi / (points_count - 1)cos_rot (right - center)[0] / asin_rot (right - center)[1] / apoints []for i in range(points_count):phi i * deltadx, dy self.get_ellipse_point(a, b, phi)x round(center[0] dx * cos_rot - dy * sin_rot)y round(center[1] dx * sin_rot dy * cos_rot)points.append([x, y])points.reverse()return np.array(points)def get_ellipse_point(self, a, b, phi):Get ellipse radius in polar coordinatesreturn a * np.cos(phi), b * np.sin(phi)def get_label_size(self):top_left self.point_atop_right self.point_cbottom_right self.point_dbottom_left self.point_fwidth1 np.linalg.norm(top_left - top_right)width2 np.linalg.norm(bottom_left - bottom_right)avg_width int((width1 width2) * np.pi / 4)height1 np.linalg.norm(top_left - bottom_left)height2 np.linalg.norm(top_right - bottom_right)avg_height int((height1 height2) / 2)return avg_width, avg_heightif __name__ __main__:shape {tag: label, shape: [{x: 0.012232142857142842, y: 0.2219140625},{x: 0.48655701811449864, y: 0.14404355243445227},{x: 0.9632539682539681, y: 0.2171875},{x: 0.9466567460317459, y: 0.7276953125},{x: 0.48447501824501454, y: 0.7952298867391453},{x: 0.023134920634920626, y: 0.7258984375}]}points []for point in shape[shape]:points.append([point[x], point[y]])imcv cv2.imread(image.jpg, cv2.IMREAD_UNCHANGED)unwrapper LabelUnwrapper(src_imageimcv, percent_pointspoints)dst_image unwrapper.unwrap()for point in unwrapper.points:cv2.line(unwrapper.src_image, tuple(point), tuple(point), colorYELLOW_COLOR, thickness3)# unwrapper.draw_mesh()cv2.imwrite(image_with_mask.png, imcv)cv2.imwrite(unwrapped.jpg, dst_image)2、原始代码github地址库
GitHub - Nepherhotep/unwrap_labels: Algorithm to unwrap labels using edge markers
项目二对项目一的升级版实现深度学习曲面标签位置并进行字符识别
1、通过训练掩膜图像获得模型后自动计算6个点的位置 图3 掩膜图像 图5 深度学习后找到的六个点 2、将弧映射至曲面
自己拿了矿泉水瓶做的实验 图6 网格圆柱投影
3、进行展开 图7 展开后效果图
展开前字符识别效果很不好 图8 未展开直接识别字符 图9 展开后字符全部识别出来 图8和图9用的百度飞桨做的字符识别对中文识别效果很好项目用到的pytesseract字符库识别效果非常不好对于广告体的中文字无法识别。 展开流程图
图10 展开流程图 4、项目二github地址
GitHub - AntoninLeroy/wine_label_reader_toolkit: Computer vision project to read a label on a wine bottle using tensorflow, OpenCV and Tesseract.
项目三依赖的库 Flask2.0.2 Flask_Cors3.0.10 imutils0.5.4 keras2.8.0 numpy1.21.5 opencv_python4.5.5.62 Pillow9.0.1 pytesseract0.3.8 scikit_learn1.0.2 scipy1.7.3 tensorflow2.8.0 tqdm4.62.3 特别说明项目二用了深度学习使用框架依赖gpu无gpu无法运行没有gpu的同学可以看项目三项目三是本人改进的版本 项目三自己通过视觉算法或者ps生成掩膜图像放置对应的位置即可
1、项目三基础代码
import cv2
import numpy as np
from showVision import *
import json
#日期2023年8月10日
#作者大胡子大叔
#托管csdn
#程序功能通过掩膜图像和原始图像对柱形图进行拉伸或者说是展开曲面展成平面图形
#之所以修改是因为cpu电脑无法运行tensorflow和keras的库一直报错所以直接去掉了没有用深度学习对识别位置进行检测
#可以通过传统的二维算法将位置提取出来然后再展开即可
#重要的算法是展开
#倾斜的图像也可以矫正
#原始图像路径
img cv2.imread(./img/train_and_valid/X/400.png)#遮罩图像路径
mask cv2.imread(./img/train_and_valid/Y/400.png, 0)#得到遮罩后的图像
image cv2.bitwise_and(img, img, maskmask)label labelVision()
mesh,unwrappedlabel.readLabels(mask, img)# 最近邻插值法缩放# 缩放到原来的四分之一
image_scale cv2.resize(image, (0, 0), fx0.5, fy0.5, interpolationcv2.INTER_NEAREST)
mesh_scale cv2.resize(mesh, (0, 0), fx0.5, fy0.5, interpolationcv2.INTER_NEAREST)
unwrapped_scale cv2.resize(unwrapped, (0, 0), fx0.5, fy0.5, interpolationcv2.INTER_NEAREST)
# 显示结果图像
#因为图片尺寸过大需要缩放窗口缩放窗口以后显示窗口有时看不见所以需要设置一下显示窗口的位置
cv2.imshow(cut, image_scale)
#设置窗口的位置
cv2.moveWindow(cut,100,100)
cv2.waitKey(0)
cv2.imshow(mesh, mesh_scale)
cv2.moveWindow(mesh,100,100)
cv2.waitKey(0)
cv2.imshow(unwrapped, unwrapped_scale)
cv2.moveWindow(unwrapped,100,100)
cv2.waitKey(0)
cv2.destroyAllWindows() 2、项目三依赖的库 imutils numpy opencv_python 如果不做字符识别可不安装下面的库 pytesseract 曲面弧面、柱面展平拉直需要自己做一个遮罩层 此代码主要是为了方便嫁接使用 自己用视觉算法识别遮罩即可进行嫁接 里面代码去掉了原始程序对gpu的依赖即自动检测遮罩层位置所以需要自己做遮罩层 如果就想用深度学习识别不想去掉的话可以访问原始代码地址 https://github.com/AntoninLeroy/wine_label_reader_toolkit 原始代码包含自动识别、展开、字符识别整个流程 如果只想要柱面展开代码请自行下载 本里面包含两个版本一个版本包含字符识别一个版本不包含字符识别
3、项目三代码下载地址 https://download.csdn.net/download/sunnyrainflower/88228306 扩展一平面变曲面平面向柱面投影 图11
参考链接柱面投影介绍与python实现一_zwx1995zwx的博客-CSDN博客
实现代码
from skimage.io import imread, imshow ,imsave
from skimage.transform import resize
import math
import numpy as npimg imread(img.jpg)
img (resize(img , [1000,800])*255).astype(np.uint8)###圆柱投影
def cylindrical_projection(img , f) :rows img.shape[0]cols img.shape[1]#f cols / (2 * math.tan(np.pi / 8))blank np.zeros_like(img)center_x int(cols / 2)center_y int(rows / 2)for y in range(rows):for x in range(cols):theta math.atan((x- center_x )/ f)point_x int(f * math.tan( (x-center_x) / f) center_x)point_y int( (y-center_y) / math.cos(theta) center_y)if point_x cols or point_x 0 or point_y rows or point_y 0:passelse:blank[y , x, :] img[point_y , point_x ,:]return blankwaved_img cylindrical_projection(img,500)
imshow(waved_img) 拓展二弧面表拉直展平 图12 图 13 用了项目三中的代码将弧形表进行展开发现两侧的刻度过度拉伸直接用的话效果不是很好还需对代码进行调整才能使用不过为弧度表的表盘识别提供了新方向。 ------------------------------------------------------------
声明 | 未经允许禁止转载谢谢合作
作者 | 大胡子大叔
出品 | CSDN
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