knettercalculator

commit c331325541ffc1fa6b56efc91c69cae6ef1574a4
parent 08c0a99e81744a130725213759ea815f15bb07e6
Author: Marcel <MTRNord@users.noreply.github.com>
Date:   Fri,  7 Oct 2016 22:38:25 +0200

Change path to learning image and rename to actual name
Diffstat:
D
code/main.py
 | 
45
---------------------------------------------
A
code/training.py
 | 
45
+++++++++++++++++++++++++++++++++++++++++++++

2 files changed, 45 insertions(+), 45 deletions(-)
diff --git a/code/main.py b/code/main.py
@@ -1,45 +0,0 @@
-############## Code from: http://stackoverflow.com/questions/9413216/simple-digit-recognition-ocr-in-opencv-python  #################
-import sys
-
-import numpy as np
-import cv2
-
-im = cv2.imread('pitrain.png')
-im3 = im.copy()
-
-gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
-blur = cv2.GaussianBlur(gray,(5,5),0)
-thresh = cv2.adaptiveThreshold(blur,255,1,1,11,2)
-
-#################      Now finding Contours         ###################
-
-contours,hierarchy = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
-
-samples =  np.empty((0,100))
-responses = []
-keys = [i for i in range(48,58)]
-
-for cnt in contours:
-    if cv2.contourArea(cnt)>50:
-        [x,y,w,h] = cv2.boundingRect(cnt)
-
-        if  h>28:
-            cv2.rectangle(im,(x,y),(x+w,y+h),(0,0,255),2)
-            roi = thresh[y:y+h,x:x+w]
-            roismall = cv2.resize(roi,(10,10))
-            cv2.imshow('norm',im)
-            key = cv2.waitKey(0)
-
-            if key == 27:  # (escape to quit)
-                sys.exit()
-            elif key in keys:
-                responses.append(int(chr(key)))
-                sample = roismall.reshape((1,100))
-                samples = np.append(samples,sample,0)
-
-responses = np.array(responses,np.float32)
-responses = responses.reshape((responses.size,1))
-print "training complete"
-
-np.savetxt('generalsamples.data',samples)
-np.savetxt('generalresponses.data',responses)
diff --git a/code/training.py b/code/training.py
@@ -0,0 +1,45 @@
+############## Code from: http://stackoverflow.com/questions/9413216/simple-digit-recognition-ocr-in-opencv-python  #################
+import sys
+
+import numpy as np
+import cv2
+
+im = cv2.imread('images/NumberLearning.png')
+im3 = im.copy()
+
+gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
+blur = cv2.GaussianBlur(gray,(5,5),0)
+thresh = cv2.adaptiveThreshold(blur,255,1,1,11,2)
+
+#################      Now finding Contours         ###################
+
+contours,hierarchy = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
+
+samples =  np.empty((0,100))
+responses = []
+keys = [i for i in range(48,58)]
+
+for cnt in contours:
+    if cv2.contourArea(cnt)>50:
+        [x,y,w,h] = cv2.boundingRect(cnt)
+
+        if  h>28:
+            cv2.rectangle(im,(x,y),(x+w,y+h),(0,0,255),2)
+            roi = thresh[y:y+h,x:x+w]
+            roismall = cv2.resize(roi,(10,10))
+            cv2.imshow('norm',im)
+            key = cv2.waitKey(0)
+
+            if key == 27:  # (escape to quit)
+                sys.exit()
+            elif key in keys:
+                responses.append(int(chr(key)))
+                sample = roismall.reshape((1,100))
+                samples = np.append(samples,sample,0)
+
+responses = np.array(responses,np.float32)
+responses = responses.reshape((responses.size,1))
+print "training complete"
+
+np.savetxt('generalsamples.data',samples)
+np.savetxt('generalresponses.data',responses)