init

.gitignore

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+#scrapyPlus/spiders
+#scrapyPlus/scrapyPlus/items
+#scrapyPlus/scrapyPlus/pipelines
+#scrapyPlus/scrapyPlus/items.py
+#scrapyPlus/scrapyPlus/middlewares.py
+#scrapyPlus/scrapyPlus/pipelines.py
+*.log
+scrapyPlus/log/*
+scrapyPlus/test/*
+
+# Python:
+*/__pycache__
+/.vagrant
+/scrapy.iml
+*.pyc
+_trial_temp*
+dropin.cache
+docs/build
+*egg-info
+.tox
+venv
+build
+dist
+.idea
+htmlcov/
+.coverage
+.pytest_cache/
+.coverage.*
+.cache/
+.mypy_cache/
+/tests/keys/localhost.crt
+/tests/keys/localhost.key
+key.txt
+key.txt.pub
+
+# Windows
+Thumbs.db
+ehthumbs.db
+Desktop.ini

main.py

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+import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+
+# 读取图片
+image = cv2.imread('p41.png')
+
+# 将图片转换为RGB格式
+image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+# 增强饱和度
+hsv = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2HSV)
+hsv[..., 1] = hsv[..., 1] * 6  # 增强饱和度
+enhanced_image_rgb = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB)
+
+# 进行Prewitt边缘检测
+gray_image = cv2.cvtColor(enhanced_image_rgb, cv2.COLOR_RGB2GRAY)
+prewittx = cv2.Sobel(gray_image, cv2.CV_64F, 1, 0, ksize=3)
+prewitty = cv2.Sobel(gray_image, cv2.CV_64F, 0, 1, ksize=3)
+prewitt_edge = cv2.magnitude(prewittx, prewitty)
+
+_, thresholded_image = cv2.threshold(prewitt_edge, 50, 255, cv2.THRESH_BINARY)
+
+# 寻找轮廓
+contours, hierarchy = cv2.findContours(thresholded_image.astype(np.uint8), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
+
+# 过滤面积小于阈值的轮廓
+filtered_contours = []
+# 计算和显示每个轮廓的最小斜率
+for contour in contours:
+    rect = cv2.minAreaRect(contour)  # 获取最小外接矩形
+    box = cv2.boxPoints(rect)  # 计算矩形的四个角点
+    box = np.intp(box)  # 角点坐标整数化  # 角点坐标整数化
+
+    # 求边长
+    widths = [np.linalg.norm(box[i] - box[(i + 1) % 4]) for i in range(4)]
+    sorted_widths = sorted(widths)
+
+    # 宽和高
+    width, height = sorted_widths[0], sorted_widths[1]
+
+    # 角度调整
+    angle = rect[2]
+    # if width < height:
+    #     angle += 90
+    area = width * height
+    # 计算斜率
+    slope = np.tan(np.radians(angle))
+    if angle < 45 and area > 5000:
+        filtered_contours.append(contour)
+
+# 将二值图像转换为RGB格式，以便能在其上绘制彩色轮廓
+contoured_image = cv2.cvtColor(thresholded_image.astype(np.uint8), cv2.COLOR_GRAY2RGB)
+
+# 使用红色来绘制轮廓
+cv2.drawContours(contoured_image, filtered_contours, -1, (255, 0, 0), 2)
+
+# 使用matplotlib显示图像
+plt.figure(figsize=(8, 8))
+plt.imshow(contoured_image)
+plt.title('Filtered Image with Red Contours and Slope Info')
+plt.axis('off')
+plt.show()

test.py

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+import easyocr
+
+reader = easyocr.Reader(['ar'])  # this needs to run only once to load the model into memory
+result = reader.readtext('00.jpg')
+print(result)