From b987154ebc6757050c691d01dcb63d3d33cd8f7e Mon Sep 17 00:00:00 2001
From: Alexander Mordvintsev <no@email>
Date: Wed, 27 Jun 2012 08:29:22 +0000
Subject: [PATCH] digits_video.py prints warning if trained classifier (should
 be created by digits.py) not found

---
 samples/python2/digits_video.py | 121 +++++++++++++++++---------------
 1 file changed, 66 insertions(+), 55 deletions(-)

diff --git a/samples/python2/digits_video.py b/samples/python2/digits_video.py
index 9b17bfacc..350a637e5 100644
--- a/samples/python2/digits_video.py
+++ b/samples/python2/digits_video.py
@@ -1,63 +1,74 @@
 import numpy as np
 import cv2
-#import video
 import digits
+import os
+import video
 from common import mosaic
 
-#cap = video.create_capture()
-cap = cv2.VideoCapture(0)
-
-model = digits.SVM()
-model.load('digits_svm.dat')
-
-SZ = 20
-
-while True:
-    ret, frame = cap.read()
-    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-
-    bin = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 31, 10)
-    bin = cv2.medianBlur(bin, 3)
-    contours, _ = cv2.findContours( bin.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
-
-    boxes = []
-    for cnt in contours:
-        x, y, w, h = cv2.boundingRect(cnt)
-        if h < 20 or h > 60 or 1.2*h < w:
-            continue
-        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0))
-        sub = bin[y:,x:][:h,:w]
-        #sub = ~cv2.equalizeHist(sub)
-        #_, sub_bin = cv2.threshold(sub, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)
-
-        s = 1.1*h/SZ
-        m = cv2.moments(sub)
-        m00 = m['m00']
-        if m00/255 < 0.1*w*h or m00/255 > 0.9*w*h:
-            continue
-
-        #frame[y:,x:][:h,:w] = sub[...,np.newaxis]
-        c1 = np.float32([m['m10'], m['m01']]) / m00
-        c0 = np.float32([SZ/2, SZ/2])
-        t = c1 - s*c0
-        A = np.zeros((2, 3), np.float32)
-        A[:,:2] = np.eye(2)*2
-        A[:,2] = t
-        sub1 = cv2.warpAffine(sub, A, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)
-        sub1 = digits.deskew(sub1)
-        sample = np.float32(sub1).reshape(1,SZ*SZ) / 255.0
-        digit = model.predict(sample)[0]
-
-        cv2.putText(frame, '%d'%digit, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.0, (200, 0, 0), thickness = 1)
-
-        boxes.append(sub1)
 
 
-    if len(boxes) > 0:
-        cv2.imshow('box', mosaic(10, boxes))
-        
+def main():
+    cap = video.create_capture()
 
-    cv2.imshow('frame', frame)
-    cv2.imshow('bin', bin)
-    if cv2.waitKey(1) == 27:
-        break
+    classifier_fn = 'digits_svm.dat'
+    if not os.path.exists(classifier_fn):
+        print '"%s" not found, run digits.py first' % classifier_fn
+        return 
+    
+    model = digits.SVM()
+    model.load('digits_svm.dat')
+
+    SZ = 20
+
+    while True:
+        ret, frame = cap.read()
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+        bin = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 31, 10)
+        bin = cv2.medianBlur(bin, 3)
+        contours, _ = cv2.findContours( bin.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
+
+        boxes = []
+        for cnt in contours:
+            x, y, w, h = cv2.boundingRect(cnt)
+            if h < 20 or h > 60 or 1.2*h < w:
+                continue
+            cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0))
+            sub = bin[y:,x:][:h,:w]
+            #sub = ~cv2.equalizeHist(sub)
+            #_, sub_bin = cv2.threshold(sub, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)
+
+            s = 1.1*h/SZ
+            m = cv2.moments(sub)
+            m00 = m['m00']
+            if m00/255 < 0.1*w*h or m00/255 > 0.9*w*h:
+                continue
+
+            #frame[y:,x:][:h,:w] = sub[...,np.newaxis]
+            c1 = np.float32([m['m10'], m['m01']]) / m00
+            c0 = np.float32([SZ/2, SZ/2])
+            t = c1 - s*c0
+            A = np.zeros((2, 3), np.float32)
+            A[:,:2] = np.eye(2)*2
+            A[:,2] = t
+            sub1 = cv2.warpAffine(sub, A, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)
+            sub1 = digits.deskew(sub1)
+            sample = np.float32(sub1).reshape(1,SZ*SZ) / 255.0
+            digit = model.predict(sample)[0]
+
+            cv2.putText(frame, '%d'%digit, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.0, (200, 0, 0), thickness = 1)
+
+            boxes.append(sub1)
+
+
+        if len(boxes) > 0:
+            cv2.imshow('box', mosaic(10, boxes))
+            
+
+        cv2.imshow('frame', frame)
+        cv2.imshow('bin', bin)
+        if cv2.waitKey(1) == 27:
+            break
+
+if __name__ == '__main__':
+    main()