mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
101 lines
3.0 KiB
101 lines
3.0 KiB
#!/usr/bin/env python |
|
|
|
# Python 2/3 compatibility |
|
from __future__ import print_function |
|
|
|
import numpy as np |
|
import cv2 |
|
|
|
# built-in modules |
|
import os |
|
import sys |
|
|
|
# local modules |
|
import video |
|
from common import mosaic |
|
|
|
from digits import * |
|
|
|
def main(): |
|
try: |
|
src = sys.argv[1] |
|
except: |
|
src = 0 |
|
cap = video.create_capture(src) |
|
|
|
classifier_fn = 'digits_svm.dat' |
|
if not os.path.exists(classifier_fn): |
|
print('"%s" not found, run digits.py first' % classifier_fn) |
|
return |
|
|
|
if True: |
|
model = cv2.ml.SVM_load(classifier_fn) |
|
else: |
|
model = cv2.ml.SVM_create() |
|
model.load_(classifier_fn) #Known bug: https://github.com/opencv/opencv/issues/4969 |
|
|
|
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, heirs = cv2.findContours( bin.copy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE) |
|
try: |
|
heirs = heirs[0] |
|
except: |
|
heirs = [] |
|
|
|
for cnt, heir in zip(contours, heirs): |
|
_, _, _, outer_i = heir |
|
if outer_i >= 0: |
|
continue |
|
x, y, w, h = cv2.boundingRect(cnt) |
|
if not (16 <= h <= 64 and w <= 1.2*h): |
|
continue |
|
pad = max(h-w, 0) |
|
x, w = x - (pad // 2), w + pad |
|
cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0)) |
|
|
|
bin_roi = bin[y:,x:][:h,:w] |
|
|
|
m = bin_roi != 0 |
|
if not 0.1 < m.mean() < 0.4: |
|
continue |
|
''' |
|
gray_roi = gray[y:,x:][:h,:w] |
|
v_in, v_out = gray_roi[m], gray_roi[~m] |
|
if v_out.std() > 10.0: |
|
continue |
|
s = "%f, %f" % (abs(v_in.mean() - v_out.mean()), v_out.std()) |
|
cv2.putText(frame, s, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.0, (200, 0, 0), thickness = 1) |
|
''' |
|
|
|
s = 1.5*float(h)/SZ |
|
m = cv2.moments(bin_roi) |
|
c1 = np.float32([m['m10'], m['m01']]) / m['m00'] |
|
c0 = np.float32([SZ/2, SZ/2]) |
|
t = c1 - s*c0 |
|
A = np.zeros((2, 3), np.float32) |
|
A[:,:2] = np.eye(2)*s |
|
A[:,2] = t |
|
bin_norm = cv2.warpAffine(bin_roi, A, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR) |
|
bin_norm = deskew(bin_norm) |
|
if x+w+SZ < frame.shape[1] and y+SZ < frame.shape[0]: |
|
frame[y:,x+w:][:SZ, :SZ] = bin_norm[...,np.newaxis] |
|
|
|
sample = preprocess_hog([bin_norm]) |
|
digit = model.predict(sample)[0] |
|
cv2.putText(frame, '%d'%digit, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.0, (200, 0, 0), thickness = 1) |
|
|
|
|
|
cv2.imshow('frame', frame) |
|
cv2.imshow('bin', bin) |
|
ch = cv2.waitKey(1) |
|
if ch == 27: |
|
break |
|
|
|
if __name__ == '__main__': |
|
main() |
|
cv2.destroyAllWindows()
|
|
|