期末作业制作网站,网站建设存在问题整改报告,网上商城建设方案,如何 套用模板做网站问题描述
实际工程中#xff0c;我们经常需要对图片进行预处理#xff0c;比如#xff1a;
1、图片是倾斜的
2、图片背景需要处理掉
3、图片的公章需要剔除
4、图片过暗#xff0c;过亮
5、图片表格检测
6、图片表格版面分析
。。。。。。等等各种情况。
结果展示…问题描述
实际工程中我们经常需要对图片进行预处理比如
1、图片是倾斜的
2、图片背景需要处理掉
3、图片的公章需要剔除
4、图片过暗过亮
5、图片表格检测
6、图片表格版面分析
。。。。。。等等各种情况。
结果展示
本文以表格图片为例介绍如何进行矫正、表格检测及裁剪保存图片。
原始图片 矫正之后 表格检测 裁剪之后 代码详解
图片矫正
通过多次旋转计算最佳旋转角度并应用旋转矩阵矫正图片
#codingutf-8
import cv2
import numpy as np
def rotate_image(image, angle):(h, w) image.shape[: 2]center (w // 2, h // 2)M cv2.getRotationMatrix2D(center, angle, 1.0)corrected cv2.warpAffine(image, M, (w, h), flags cv2.INTER_CUBIC, \borderMode cv2.BORDER_REPLICATE)return correcteddef determine_score(arr):histogram np.sum(arr, axis 2, dtype float)score np.sum((histogram[..., 1 :] - histogram[..., : -1]) ** 2, \axis 1, dtype float)return scoredef correct_skew(image, delta 0.05, limit 10):thresh cv2.threshold(image, 0, 255, cv2.THRESH_BINARY_INV \cv2.THRESH_OTSU)[1]angles np.arange(-limit, limit delta, delta)img_stack np.stack([rotate_image(thresh, angle) for angle \in angles], axis 0)scores determine_score(img_stack)best_angle angles[np.argmax(scores)]corrected rotate_image(image, best_angle)return best_angle, corrected
if __name__ __main__:batch_folder rD:\temp\picsout_folder rD:\temp\picsout/for root, dirs, files in os.walk(batch_folder):for file in files:file_path os.path.join(root, file)file_path file_path.replace(\\, /)img cv2.imread(file_path, 0)angle, corrected correct_skew(img)print(angle,file_path)cv2.imwrite(out_folder file_path.split(/)[-1], corrected)
表格识别
通过微软的table-transformer-detection进行表格该模型可在Hugging Face 官网下载。
图片裁剪
通过PIL里的Image的crop方法对指定的let_top,right_bottom进行裁剪。
相关代码见下
from PIL import Image
import matplotlib.pyplot as plt
file_path rD:\temp\pics\efb.jpg
image Image.open(file_path).convert(RGB)
width, height image.size
image.resize((int(width * 0.5), int(height * 0.5)))
from transformers import DetrFeatureExtractorfeature_extractor DetrFeatureExtractor()
encoding feature_extractor(image, return_tensorspt)
encoding.keys()
from transformers import TableTransformerForObjectDetection
model TableTransformerForObjectDetection.from_pretrained(rD:\Modles\table-transformer-detection/)
import torchwith torch.no_grad():outputs model(**encoding)
COLORS [[0.000, 0.447, 0.741], [0.850, 0.325, 0.098], [0.929, 0.694, 0.125],[0.494, 0.184, 0.556], [0.466, 0.674, 0.188], [0.301, 0.745, 0.933]]def plot_results(pil_img, scores, labels, boxes):plt.figure(figsize(16, 10))plt.imshow(pil_img)ax plt.gca()colors COLORS * 100for score, label, (xmin, ymin, xmax, ymax), c in zip(scores.tolist(), labels.tolist(), boxes.tolist(), colors):ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin,fillFalse, colorc, linewidth3))text f{model.config.id2label[label]}: {score:0.2f}ax.text(xmin, ymin, text, fontsize15,bboxdict(facecoloryellow, alpha0.5))plt.axis(off)plt.show()if __name__ __main__:width, height image.sizeresults feature_extractor.post_process_object_detection(outputs, threshold0.2, target_sizes[(height, width)])[0]plot_results(image, results[scores], results[labels], results[boxes])print(results[scores])print(results[labels])print(results[boxes])print(results[boxes][0][0],type((results[boxes][0][0])))x0int(results[boxes][0][0].item())-50y0int(results[boxes][0][1].item())-50x1int(results[boxes][0][2].item())50y1int(results[boxes][0][3].item())50img2 image.crop((x0,y0,x1,y1))img2.save(rD:\\efb.jpg)
