diff --git a/scan.py b/scan.py
new file mode 100644
index 0000000..a58ff11
--- /dev/null
+++ b/scan.py
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+# 导入工具包
+import numpy as np
+import argparse
+import cv2
+import pytesseract
+import os
+from PIL import Image
+
+# 设置参数
+ap = argparse.ArgumentParser()
+ap.add_argument("-i", "--image", required = True,
+	help = "Path to the image to be scanned") 
+args = vars(ap.parse_args())
+
+def order_points(pts):
+	# 一共4个坐标点
+	rect = np.zeros((4, 2), dtype = "float32")
+
+	# 按顺序找到对应坐标0123分别是 左上，右上，右下，左下
+	# 计算左上，右下
+	s = pts.sum(axis = 1)
+	rect[0] = pts[np.argmin(s)]
+	rect[2] = pts[np.argmax(s)]
+
+	# 计算右上和左下
+	diff = np.diff(pts, axis = 1)
+	rect[1] = pts[np.argmin(diff)]
+	rect[3] = pts[np.argmax(diff)]
+
+	return rect
+
+def four_point_transform(image, pts):
+	# 获取输入坐标点
+	rect = order_points(pts)
+	(tl, tr, br, bl) = rect
+
+	# 计算输入的w和h值
+	widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
+	widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
+	maxWidth = max(int(widthA), int(widthB))
+
+	heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
+	heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
+	maxHeight = max(int(heightA), int(heightB))
+
+	# 变换后对应坐标位置
+	dst = np.array([
+		[0, 0],
+		[maxWidth - 1, 0],
+		[maxWidth - 1, maxHeight - 1],
+		[0, maxHeight - 1]], dtype = "float32")
+
+	# 计算变换矩阵
+	M = cv2.getPerspectiveTransform(rect, dst)
+	warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
+
+	# 返回变换后结果
+	return warped
+
+def resize(image, width=None, height=None, inter=cv2.INTER_AREA):
+	dim = None
+	(h, w) = image.shape[:2]
+	if width is None and height is None:
+		return image
+	if width is None:
+		r = height / float(h)
+		dim = (int(w * r), height)
+	else:
+		r = width / float(w)
+		dim = (width, int(h * r))
+	resized = cv2.resize(image, dim, interpolation=inter)
+	return resized
+
+# 读取输入
+image = cv2.imread(args["image"])
+#坐标也会相同变化
+ratio = image.shape[0] / 500.0
+orig = image.copy()
+
+
+image = resize(orig, height = 500)
+
+# 预处理
+gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+gray = cv2.GaussianBlur(gray, (5, 5), 0)
+edged = cv2.Canny(gray, 75, 200)
+
+# 展示预处理结果
+print("STEP 1: 边缘检测")
+cv2.imshow("Image", image)
+cv2.imshow("Edged", edged)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+
+# 轮廓检测
+cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[0]
+cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:5]
+
+# 遍历轮廓
+for c in cnts:
+	# 计算轮廓近似
+	peri = cv2.arcLength(c, True)
+	# C表示输入的点集
+	# epsilon表示从原始轮廓到近似轮廓的最大距离，它是一个准确度参数
+	# True表示封闭的
+	approx = cv2.approxPolyDP(c, 0.05 * peri, True)
+
+	# 4个点的时候就拿出来
+	if len(approx) == 4:
+		screenCnt = approx
+		break
+
+# 展示结果
+print("STEP 2: 获取轮廓")
+cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)
+cv2.imshow("Outline", image)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+
+# 透视变换
+warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)
+
+# 二值处理
+warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
+ref = cv2.threshold(warped, 100, 255, cv2.THRESH_BINARY)[1]
+cv2.imwrite('scan.jpg', ref)
+# 展示结果
+print("STEP 3: 变换")
+cv2.imshow("Original", resize(orig, height = 650))
+cv2.imshow("Scanned", resize(ref, height = 650))
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+
+'''
+OCR扫描
+'''
+preprocess = "blur"
+
+if preprocess == "thresh":
+	gray = cv2.threshold(ref, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
+if preprocess == "blur":
+	gray = cv2.medianBlur(ref, 3)
+cv2.imshow("Detect", gray)
+
+filename = "{}.png".format(os.getpid())
+cv2.imwrite(filename, gray)
+text = pytesseract.image_to_string(Image.open(filename))
+os.remove(filename)
+
+encodings = ['utf-8', 'latin1', 'iso-8859-1', 'cp1252', 'gbk', 'big5']
+for encoding in encodings:
+	try:
+		with open("out.txt", 'w', encoding=encoding, errors="replace") as file:
+			file.write(text)
+			break
+	except UnicodeDecodeError:
+		continue
+file.close()
+print("text is written to out.txt")
+
+# Wait for pressing any key
+cv2.waitKey(0)
+cv2.destroyAllWindows()
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