From 27764bfed78ff1a456a2749ca31978906dd2b79c Mon Sep 17 00:00:00 2001
From: jackyu1127 <jack-yu-business@foxmail.com>
Date: Sun, 8 Dec 2019 02:57:59 +0800
Subject: [PATCH] v2.0

---
 hyperlpr_pip_pkg/.idea/hyperlpr_python_pkg.iml     |    11 +
 hyperlpr_pip_pkg/.idea/misc.xml                    |     7 +
 hyperlpr_pip_pkg/.idea/modules.xml                 |     8 +
 hyperlpr_pip_pkg/.idea/workspace.xml               |   533 +
 hyperlpr_pip_pkg/demo.py                           |     4 +
 hyperlpr_pip_pkg/hyperlpr/LICENSE                  |    19 +
 hyperlpr_pip_pkg/hyperlpr/__init__.py              |     8 +
 hyperlpr_pip_pkg/hyperlpr/hyperlpr.py              |   317 +
 .../hyperlpr/models/cascade/char/char_single.xml   | 10408 +++++++++++++++++++
 .../models/cascade/detector/detector_ch.xml        |  1242 +++
 hyperlpr_pip_pkg/hyperlpr/table_chs.py             |     5 +
 hyperlpr_pip_pkg/setup.py                          |    33 +
 12 files changed, 12595 insertions(+)
 create mode 100644 hyperlpr_pip_pkg/.idea/hyperlpr_python_pkg.iml
 create mode 100644 hyperlpr_pip_pkg/.idea/misc.xml
 create mode 100644 hyperlpr_pip_pkg/.idea/modules.xml
 create mode 100644 hyperlpr_pip_pkg/.idea/workspace.xml
 create mode 100644 hyperlpr_pip_pkg/demo.py
 create mode 100644 hyperlpr_pip_pkg/hyperlpr/LICENSE
 create mode 100644 hyperlpr_pip_pkg/hyperlpr/__init__.py
 create mode 100644 hyperlpr_pip_pkg/hyperlpr/hyperlpr.py
 create mode 100755 hyperlpr_pip_pkg/hyperlpr/models/cascade/char/char_single.xml
 create mode 100644 hyperlpr_pip_pkg/hyperlpr/models/cascade/detector/detector_ch.xml
 create mode 100644 hyperlpr_pip_pkg/hyperlpr/table_chs.py
 create mode 100644 hyperlpr_pip_pkg/setup.py

diff --git a/hyperlpr_pip_pkg/.idea/hyperlpr_python_pkg.iml b/hyperlpr_pip_pkg/.idea/hyperlpr_python_pkg.iml
new file mode 100644
index 0000000..8a24a28
--- /dev/null
+++ b/hyperlpr_pip_pkg/.idea/hyperlpr_python_pkg.iml
@@ -0,0 +1,11 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="Python 3.6 (2)" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+  <component name="TestRunnerService">
+    <option name="PROJECT_TEST_RUNNER" value="Unittests" />
+  </component>
+</module>
\ No newline at end of file
diff --git a/hyperlpr_pip_pkg/.idea/misc.xml b/hyperlpr_pip_pkg/.idea/misc.xml
new file mode 100644
index 0000000..b48de80
--- /dev/null
+++ b/hyperlpr_pip_pkg/.idea/misc.xml
@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
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+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.6 (2)" project-jdk-type="Python SDK" />
+</project>
\ No newline at end of file
diff --git a/hyperlpr_pip_pkg/.idea/modules.xml b/hyperlpr_pip_pkg/.idea/modules.xml
new file mode 100644
index 0000000..b08d264
--- /dev/null
+++ b/hyperlpr_pip_pkg/.idea/modules.xml
@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/hyperlpr_python_pkg.iml" filepath="$PROJECT_DIR$/.idea/hyperlpr_python_pkg.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file
diff --git a/hyperlpr_pip_pkg/.idea/workspace.xml b/hyperlpr_pip_pkg/.idea/workspace.xml
new file mode 100644
index 0000000..c1ac3e2
--- /dev/null
+++ b/hyperlpr_pip_pkg/.idea/workspace.xml
@@ -0,0 +1,533 @@
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diff --git a/hyperlpr_pip_pkg/demo.py b/hyperlpr_pip_pkg/demo.py
new file mode 100644
index 0000000..3b5fd94
--- /dev/null
+++ b/hyperlpr_pip_pkg/demo.py
@@ -0,0 +1,4 @@
+from hyperlpr import *
+import cv2
+image = cv2.imread("test.png")
+print(HyperLPR_plate_recognition(image,16,charSelectionDeskew=False))
diff --git a/hyperlpr_pip_pkg/hyperlpr/LICENSE b/hyperlpr_pip_pkg/hyperlpr/LICENSE
new file mode 100644
index 0000000..96f1555
--- /dev/null
+++ b/hyperlpr_pip_pkg/hyperlpr/LICENSE
@@ -0,0 +1,19 @@
+Copyright (c) 2018 The Python Packaging Authority
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/hyperlpr_pip_pkg/hyperlpr/__init__.py b/hyperlpr_pip_pkg/hyperlpr/__init__.py
new file mode 100644
index 0000000..0b68157
--- /dev/null
+++ b/hyperlpr_pip_pkg/hyperlpr/__init__.py
@@ -0,0 +1,8 @@
+name = "hyperlpr_python_pkg"
+import sys
+from .hyperlpr import LPR
+import os
+
+PR = LPR(os.path.join(os.path.split(os.path.realpath(__file__))[0],"models"))
+def HyperLPR_plate_recognition(Input_BGR,minSize=30,charSelectionDeskew=True , region = "CH"):
+    return PR.plate_recognition(Input_BGR,minSize,charSelectionDeskew)
diff --git a/hyperlpr_pip_pkg/hyperlpr/hyperlpr.py b/hyperlpr_pip_pkg/hyperlpr/hyperlpr.py
new file mode 100644
index 0000000..69421aa
--- /dev/null
+++ b/hyperlpr_pip_pkg/hyperlpr/hyperlpr.py
@@ -0,0 +1,317 @@
+#coding=utf-8
+import cv2
+import numpy as np
+import os
+from .table_chs import chars
+
+
+
+
+class LPR():
+    def __init__(self,folder):
+        """
+        Init the recognition instance.
+
+        :param model_detection:    opencv cascade model which detecting license plate.
+        :param model_finemapping:    finemapping model which deskew the license plate
+        :param model_rec:   CNN based sequence recognition model trained with CTC loss.
+        """
+
+        charLocPath= os.path.join(folder,"cascade/char/char_single.xml")
+        detectorPath = os.path.join(folder,"cascade/detector/detector_ch.xml")
+        modelRecognitionPath = [os.path.join(folder,"dnn/SegmenationFree-Inception.prototxt"),os.path.join(folder,"dnn/SegmenationFree-Inception.caffemodel")]
+        modelFineMappingPath= [os.path.join(folder,"dnn/HorizonalFinemapping.prototxt"),os.path.join(folder,"dnn/HorizonalFinemapping.caffemodel")]
+        mini_ssd_path= [os.path.join(folder,"dnn/mininet_ssd_v1.prototxt"),os.path.join(folder,"dnn/mininet_ssd_v1.caffemodel")]
+        refine_net_path = [os.path.join(folder,"dnn/refinenet.prototxt"),os.path.join(folder,"dnn/refinenet.caffemodel")]
+
+        self.detector = cv2.CascadeClassifier(detectorPath)
+        self.charLoc = cv2.CascadeClassifier(charLocPath)
+        self.modelRecognition = cv2.dnn.readNetFromCaffe(*modelRecognitionPath)
+        self.ssd_detection = cv2.dnn.readNetFromCaffe(*mini_ssd_path)
+        self.refine_net  =  cv2.dnn.readNetFromCaffe(*refine_net_path)
+
+    def detect_ssd(self,im):
+        """
+        Detect the approximate location of plate via single shot detector based on modified mobilenet.
+        :param im:  input image (BGR) .
+        :return:  [[cropped,x1,y2,x2,y2] ,... ]
+        """
+        _im = im.copy()
+        pixel_means = [0.406, 0.456, 0.485]
+        pixel_stds = [0.225, 0.224, 0.229]
+        pixel_scale = 255.0
+        rows, cols, c = im.shape
+        im_tensor = np.zeros((1, 3, im.shape[0], im.shape[1]))
+        im = im.astype(np.float32)
+        for i in range(3):
+            im_tensor[0, i, :, :] = (im[:, :, 2 - i] / pixel_scale - pixel_means[2 - i]) / pixel_stds[2 - i]
+        self.ssd_detection.setInput(im_tensor)
+        print(im_tensor.shape)
+        cropped_images = []
+        cvOut = self.ssd_detection.forward()
+        for detection in cvOut[0, 0, :, :]:
+            score = float(detection[2])
+            if score > 0.5:
+                x1 = int(detection[3] * cols)
+                y1 = int(detection[4] * rows)
+                x2 = int(detection[5] * cols)
+                y2 = int(detection[6] * rows)
+                x1 = max(x1,0)
+                y1 = max(y1,0)
+                x2 = min(x2,im.shape[1]-1)
+                y2 = min(y2,im.shape[0]-1)
+                cropped = _im[y1:y2,x1:x2]
+                cropped_images.append([cropped ,[x1,y1,x2,y2]])
+            return cropped_images
+
+    def detect_traditional(self,image_gray,resize_h = 720,en_scale =1.1,minSize = 30):
+        """
+        Detect the approximate location of plate via opencv build-in cascade detection.
+        :param image_gray:  input single channel image (gray) .
+        :param resize_h:  adjust input image size to a fixed size.
+        :param en_scale:  the ratio of image between every scale of images in cascade detection.
+        :param minSize:  minSize of plate increase this parameter can increase the speed of detection.
+        :return:   the results.
+        """
+        watches = self.detector.detectMultiScale(image_gray, en_scale, 3, minSize=(minSize*4, minSize))
+        cropped_images = []
+        for (x, y, w, h) in watches:
+            x -= w * 0.14
+            w += w * 0.28
+            y -= h * 0.15
+            h += h * 0.35
+            x1 = int(x)
+            y1 = int(y)
+            x2 = int(x+w)
+            y2 = int(y+h)
+            x1 = max(x1,0)
+            y1 = max(y1,0)
+            x2 = min(x2,image_gray.shape[1]-1)
+            y2 = min(y2,image_gray.shape[0]-1)
+            cropped = image_gray[y1:y2,x1:x2]
+            cropped_images.append([cropped ,[x1,y1,x2,y2]])
+        return cropped_images
+
+
+    def loose_crop(self,image, box, aspect_ratio, padding_ratio=1.7):
+        """
+        Crop the image with an extend rectangle.
+        :param image:  input image (BGR).
+        :param box:  origin bounding box.
+        :param aspect_ratio:  the aspect ratio that need to keep.
+        :param padding_ratio:  padding ratio of origin rectangle.
+        :return:   the cropped image
+        """
+        x1, y1, x2, y2 = box
+        cx, cy = ((x2 + x1) // 2, (y2 + y1) // 2)
+        if (x2 - x1) / (y2 - y1) > aspect_ratio:
+            _w = int((x2 - x1) * padding_ratio)
+            _h = int(_w / aspect_ratio)
+        else:
+            _h = int((y2 - y1) * padding_ratio)
+            _w = int(_h * aspect_ratio)
+        x1, y1, x2, y2 = cx - _w // 2, cy - _h // 2, cx + _w // 2, cy + _h // 2
+        x1 = int(max(x1, 0))
+        y1 = int(max(y1, 0))
+        cropped = image[y1:y2, x1:x2]
+        return cropped
+
+
+    def decode_ctc(self,y_pred):
+        """
+        Decode  the results from the last layer of recognition model.
+        :param y_pred:  the feature map output last feature map.
+        :return: decode results.
+        """
+        results = ""
+        confidence = 0.0
+        y_pred = y_pred.T
+        table_pred = y_pred
+        res = table_pred.argmax(axis=1)
+        for i,one in enumerate(res):
+            if one<len(chars) and (i==0 or (one!=res[i-1])):
+                results+= chars[one]
+                confidence+=table_pred[i][one]
+        confidence/= len(results)
+        return results,confidence
+
+    def fit_ransac(self,pts, zero_add=0):
+        """
+        fit a line and use RANSAC algorithm to reject outlier.
+        :param pts: input pts
+        :return: The line border around the image on the location of the point
+        """
+        if len(pts) >= 2:
+            [vx, vy, x, y] = cv2.fitLine(pts, cv2.DIST_HUBER, 0, 0.01, 0.01)
+            lefty = int((-x * vy / vx) + y)
+            righty = int(((136 - x) * vy / vx) + y)
+            return lefty + 30 + zero_add, righty + 30 + zero_add
+        return 0, 0
+
+    def fine_mapping(self,image_rgb):
+        """
+        fit plate upper and lower with multi-threshold method to segment single character.
+        :param image_rgb:
+        :return: fined image.
+        """
+
+        line_upper = [];
+        line_lower = [];
+        line_experiment = []
+        if image_rgb.ndim == 3:
+            gray_image = cv2.cvtColor(image_rgb, cv2.COLOR_BGR2GRAY)
+        else:
+            gray_image = image_rgb
+
+        for k in np.linspace(-50, 0, 16):
+            binary_niblack = cv2.adaptiveThreshold(gray_image, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 17,k)
+            if cv2.__version__[0] == "4":
+                contours, hierarchy = cv2.findContours(binary_niblack.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
+            else:
+                imagex, contours, hierarchy = cv2.findContours(binary_niblack.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
+            for contour in contours:
+                bdbox = cv2.boundingRect(contour)
+                if ((bdbox[3] / float(bdbox[2]) > 0.7 and bdbox[3] * bdbox[2] > 100 and bdbox[3] * bdbox[2] < 1200) or (
+                        bdbox[3] / float(bdbox[2]) > 3 and bdbox[3] * bdbox[2] < 100)):
+                    line_upper.append([bdbox[0], bdbox[1]])
+                    line_lower.append([bdbox[0] + bdbox[2], bdbox[1] + bdbox[3]])
+                    line_experiment.append([bdbox[0], bdbox[1]])
+                    line_experiment.append([bdbox[0] + bdbox[2], bdbox[1] + bdbox[3]])
+        rgb = cv2.copyMakeBorder(image_rgb, 30, 30, 0, 0, cv2.BORDER_REPLICATE)
+        leftyA, rightyA = self.fit_ransac(np.array(line_lower), 3)
+        leftyB, rightyB = self.fit_ransac(np.array(line_upper), -3)
+        rows, cols = rgb.shape[:2]
+        pts_map1 = np.float32([[cols - 1, rightyA], [0, leftyA], [cols - 1, rightyB], [0, leftyB]])
+        pts_map2 = np.float32([[136, 36], [0, 36], [136, 0], [0, 0]])
+        mat = cv2.getPerspectiveTransform(pts_map1, pts_map2)
+        image = cv2.warpPerspective(rgb, mat, (136, 36), flags=cv2.INTER_CUBIC)
+        return image
+
+
+    def fine_mapping_by_selecting(self,image_rgb,line_upper,line_lower ):
+        """
+        fit plate upper and lower with detecting character boundingbox
+        :param image_rgb: input image
+        :param line_upper: padding of upper
+        :param line_lower: padding of lower
+        :return: fined image.
+        """
+
+
+        rgb = cv2.copyMakeBorder(image_rgb, 30, 30, 0, 0, cv2.BORDER_REPLICATE)
+        leftyA, rightyA = self.fit_ransac(np.array(line_lower), 3)
+        leftyB, rightyB = self.fit_ransac(np.array(line_upper), -3)
+        rows, cols = rgb.shape[:2]
+        pts_map1 = np.float32([[cols - 1, rightyA], [0, leftyA], [cols - 1, rightyB], [0, leftyB]])
+        pts_map2 = np.float32([[136, 36], [0, 36], [136, 0], [0, 0]])
+        mat = cv2.getPerspectiveTransform(pts_map1, pts_map2)
+        image = cv2.warpPerspective(rgb, mat, (136, 36), flags=cv2.INTER_CUBIC)
+        return image
+
+    def to_refine(self,image, pts, scale=3.0):
+        """
+        refine the image by input points.
+        :param image_rgb: input image
+        :param pts: points
+        """
+        x1, y1, x2, y2, x3, y3, x4, y4 = pts.ravel()
+        cx, cy = int(128 // 2), int(48 // 2)
+        cw = 64
+        ch = 24
+        tx1 = cx - cw // 2
+        ty1 = cy - ch // 2
+        tx2 = cx + cw // 2
+        ty2 = cy - ch // 2
+        tx3 = cx + cw // 2
+        ty3 = cy + ch // 2
+        tx4 = cx - cw // 2
+        ty4 = cy + ch // 2
+        target_pts = np.array([[tx1, ty1], [tx2, ty2], [tx3, ty3], [tx4, ty4]]).astype(np.float32) * scale
+        org_pts = np.array([[x1, y1], [x2, y2], [x3, y3], [x4, y4]]).astype(np.float32)
+        mat_ = cv2.estimateRigidTransform(org_pts, target_pts, True)
+        dsize = (int(120 * scale), int(48 * scale))
+        warped = cv2.warpAffine(image, mat_, dsize)
+        return warped
+
+    def affine_crop(self, image, pts):
+        """
+        crop a image by affine transform.
+        :param image_rgb: input image
+        :param pts: points
+        """
+        x1, y1, x2, y2, x3, y3, x4, y4 = pts.ravel()
+        target_pts = np.array([[0, 0], [136, 0], [136, 36], [0, 36]]).astype(np.float32)
+        org_pts = np.array([[x1, y1], [x2, y2], [x3, y3], [x4, y4]]).astype(np.float32)
+        mat = cv2.getPerspectiveTransform(org_pts, target_pts)
+        dsize = (136, 36)
+        warped = cv2.warpPerspective(image, mat, dsize)
+        return warped
+
+    def finetune(self,image_, stage=2):
+        """
+        cascade fine tune a image by regress four corner of plate.
+        :param image_rgb: input image
+        :param stages: cascade stage
+        """
+
+        tof = image_.copy()
+        image = cv2.resize(tof, (120, 48))
+        blob = cv2.dnn.blobFromImage(image, size=(120, 48), swapRB=False, mean=(127.5, 127.5, 127.5), scalefactor=0.0078125, crop=False)
+        self.refine_net.setInput(blob)
+        h, w, c = image_.shape
+        pts = (self.refine_net.forward("conv6-3").reshape(4, 2) * np.array([w, h])).astype(np.int)
+        g = self.to_refine(image_, pts)
+        blob = cv2.dnn.blobFromImage(g, size=(120, 48), swapRB=False, mean=(127.5, 127.5, 127.5), scalefactor=0.0078125, crop=False)
+        self.refine_net.setInput(blob)
+        h, w, c = g.shape
+        pts = (self.refine_net.forward("conv6-3").reshape(4, 2) * np.array([w, h])).astype(np.int)
+        cropped = self.affine_crop(g, pts)
+        return cropped
+
+
+    def segmentation_free_recognition(self,src):
+        """
+        return: ctc decode results
+        """
+        temp = cv2.resize(src,( 160,40))
+        temp = temp.transpose(1, 0, 2)
+        blob = cv2.dnn.blobFromImage(temp, 1/255.0, (40, 160), (0,0,0), False, False)
+        self.modelRecognition.setInput(blob)
+        y_pred = self.modelRecognition.forward()[0]
+        y_pred = y_pred[:,2:,:]
+        y_pred = np.squeeze(y_pred)
+        return self.decode_ctc(y_pred)
+
+
+    def plate_recognition(self,image,minSize=30,charSelectionDeskew=True,mode='ssd'):
+        """
+        the simple pipline consists of detection . deskew , fine mapping alignment, recognition.
+        :param image: the input BGR image from imread used by opencv
+        :param minSize: the minSize of plate
+        :param charSelectionDeskew: use character detection when fine mapping stage which will reduce the False Accept Rate as far as possible.
+        :return: will return [ [plate1 string ,confidence1, location1  ],
+                               [plate2 string ,confidence2, location2  ] ....
+                             ]
+
+        usage:
+            import cv2
+            import numpy as np
+            from hyperlpr import LPR
+            pr = LPR("models")
+            image  = cv2.imread("tests/image")
+            print(pr.plateRecognition(image))
+        """
+
+        images = self.detect_ssd(image)
+        res_set = []
+        for j,plate in enumerate(images):
+            plate,[left,top,right,bottom] = plate
+            print(left,top,right,bottom)
+            cropped = self.loose_crop(image, [left, top, right, bottom], 120 / 48)
+            cropped_finetuned = self.finetune(cropped)
+            res, confidence = self.segmentation_free_recognition(cropped_finetuned)
+            res_set.append([res,confidence,[left,top,right,bottom ]])
+        return res_set
+
+
+#
diff --git a/hyperlpr_pip_pkg/hyperlpr/models/cascade/char/char_single.xml b/hyperlpr_pip_pkg/hyperlpr/models/cascade/char/char_single.xml
new file mode 100755
index 0000000..84c155d
--- /dev/null
+++ b/hyperlpr_pip_pkg/hyperlpr/models/cascade/char/char_single.xml
@@ -0,0 +1,10408 @@
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+  <featureParams>
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+    <featSize>1</featSize>
+    <mode>ALL</mode></featureParams>
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diff --git a/hyperlpr_pip_pkg/hyperlpr/models/cascade/detector/detector_ch.xml b/hyperlpr_pip_pkg/hyperlpr/models/cascade/detector/detector_ch.xml
new file mode 100644
index 0000000..72eacf1
--- /dev/null
+++ b/hyperlpr_pip_pkg/hyperlpr/models/cascade/detector/detector_ch.xml
@@ -0,0 +1,1242 @@
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+    <!-- stage 7 -->
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+        29 3 2 4</rect></_>
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+        29 4 1 3</rect></_>
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+        30 8 2 2</rect></_>
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+</opencv_storage>
diff --git a/hyperlpr_pip_pkg/hyperlpr/table_chs.py b/hyperlpr_pip_pkg/hyperlpr/table_chs.py
new file mode 100644
index 0000000..e7aa39c
--- /dev/null
+++ b/hyperlpr_pip_pkg/hyperlpr/table_chs.py
@@ -0,0 +1,5 @@
+chars = [u"京", u"沪", u"津", u"渝", u"冀", u"晋", u"蒙", u"辽", u"吉", u"黑", u"苏", u"浙", u"皖", u"闽", u"赣", u"鲁", u"豫", u"鄂", u"湘", u"粤", u"桂",
+             u"琼", u"川", u"贵", u"云", u"藏", u"陕", u"甘", u"青", u"宁", u"新", u"0", u"1", u"2", u"3", u"4", u"5", u"6", u"7", u"8", u"9", u"A",
+             u"B", u"C", u"D", u"E", u"F", u"G", u"H", u"J", u"K", u"L", u"M", u"N", u"P", u"Q", u"R", u"S", u"T", u"U", u"V", u"W", u"X",
+             u"Y", u"Z",u"港",u"学",u"使",u"警",u"澳",u"挂",u"军",u"北",u"南",u"广",u"沈",u"兰",u"成",u"济",u"海",u"民",u"航",u"空"
+             ]
diff --git a/hyperlpr_pip_pkg/setup.py b/hyperlpr_pip_pkg/setup.py
new file mode 100644
index 0000000..fd2e605
--- /dev/null
+++ b/hyperlpr_pip_pkg/setup.py
@@ -0,0 +1,33 @@
+import setuptools
+
+# with open("README.md", "r") as fh:
+#     long_description = fh.read()
+long_description = "LPR framework"
+setuptools.setup(
+    name="hyperlpr",
+    version="0.0.2",
+    author="Jack Yu",
+    author_email="jack-yu-business@foxmail.com",
+    description="A general small license plate recognition framework",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    url="https://github.com/zeusees/HyperLPR",
+    packages=setuptools.find_packages(),
+    classifiers=(
+        "Programming Language :: Python :: 3",
+        "Programming Language :: Python :: 2",
+        "License :: OSI Approved :: MIT License",
+        "Operating System :: OS Independent",
+    ),
+    install_requires=[
+        "opencv-python"
+    ],
+    package_data={
+        "hyperlpr": [
+            "models/cascade/char/*.*",
+            "models/cascade/detector/*.*",
+            "models/dnn/*.*"
+        ]
+    },
+
+)