I have update the Script but one Error i can not fix. Here my script Version:
import sys
import numpy as np
import cv2
im = cv2.imread('test001.png')
res = cv2.resize(im,None,fx=2, fy=2, interpolation = cv2.INTER_CUBIC)
im3 = res.copy()
gray = cv2.cvtColor(res,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) and (cv2.contourArea(cnt)<900):
[x,y,w,h] = cv2.boundingRect(cnt)
if ((h>0) and (h<35)) and ((w>0) and (w<35)):
cv2.rectangle(res,(x,y),(x+w,y+h),(0,0,255),1)
roi = thresh[y:y+h,x:x+w]
roismall = cv2.resize(roi,(30,30))
cv2.imshow('norm',res)
key = cv2.waitKey(0) % 256
print ("+")
print (key)
print ("+")
if key == 27: # (escape to quit)
sys.exit()
elif key in keys:
print ("-")
print (key)
print ("-")
responses.append(int(key))
print (len(roismall))
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)
When i run the code i get this Error:
Traceback (most recent call last): File "file001.py", line 45, in sample = roismall.reshape((1,100)) ValueError: total size of new array must be unchanged
The last print "print (len(roismall))" have a Value of 30.
regards Thomas
this Error was Homemade:
sample = roismall.reshape((1,100))
is corresponding to this line:
roismall = cv2.resize(roi,(30,30))
The 30 x 30 = 900 is the right Value or 10,10 = 100. I change it back to:
roismall = cv2.resize(roi,(10,10))
Here the complett script:
import sys
import numpy as np
import cv2
im = cv2.imread('test001.png')
res = cv2.resize(im,None,fx=2, fy=2, interpolation = cv2.INTER_CUBIC)
im3 = res.copy()
gray = cv2.cvtColor(res,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))
sample = np.empty((0,100))
responses = []
keys = [i for i in range(48,58)]
for cnt in contours:
if (cv2.contourArea(cnt)>10) and (cv2.contourArea(cnt)<900):
[x,y,w,h] = cv2.boundingRect(cnt)
if ((h>15) and (h<30)) and ((w>8) and (w<30)):
cv2.rectangle(res,(x,y),(x+w,y+h),(0,0,255),1)
roi = thresh[y:y+h,x:x+w]
roismall = cv2.resize(roi,(10,10))
cv2.imshow('roi',roismall)
cv2.imshow('norm',res)
key = cv2.waitKey(0) % 256
if key == 27: # (escape to quit)
sys.exit()
elif key in keys:
responses.append(int(key))
sample = roismall.reshape((1,100))
samples = np.append(samples,sample)
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)
regards Thomas
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