我对OpenCV有一个奇怪的问题。我在Python和C ++上都使用OpenCV进行模板匹配,但是,即使Python在后台使用C ++方法,我也会得到截然不同的结果。Python方法给了我真正准确的位置,C ++甚至还差得远。这是什么原因呢?是我的C ++代码还是其他东西?
我使用Python 2.7.11,Apple LLVM版本7.3.0(clang-703.0.29)和OpenCV3.0。
我的Python代码:
def toGray(img):
_, _, channels = img.shape
if channels == 3:
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
else:
gray = img
return gray
def template_match(img, template):
w, h = template.shape[::-1]
res = cv2.matchTemplate(img,template,cv2.TM_CCOEFF_NORMED)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
top_left = max_loc
bottom_right = (top_left[0] + w, top_left[1] + h)
cv2.rectangle(img,top_left, bottom_right, 255, 2)
plt.subplot(121),plt.imshow(res,cmap = 'gray')
plt.title('Matching Result'), plt.xticks([]), plt.yticks([])
plt.subplot(122),plt.imshow(img,cmap = 'gray')
plt.title('Detected Point'), plt.xticks([]), plt.yticks([])
plt.suptitle("TM_CCOEFF_NORMED")
plt.show()
if __name__ == "__main__":
img_name = sys.argv[1]
img_name2 = sys.argv[2]
img_rgb = cv2.imread(img_name)
img_rgb2 = cv2.imread(img_name2)
gimg1 = toGray(img_rgb)
gimg2 = toGray(img_rgb2)
template_match(gimg1, gimg2)
我的C ++代码(与OpenCV文档完全相同):
Mat img; Mat templ; Mat result;
char* image_window = "Source Image";
char* result_window = "Result window";
int match_method;
int max_Trackbar = 5;
/// Function Headers
void MatchingMethod( int, void* );
/** @function main */
int main( int argc, char** argv )
{
/// Load image and template
img = imread( argv[1], 1 );
templ = imread( argv[2], 1 );
/// Create windows
namedWindow( image_window, CV_WINDOW_AUTOSIZE );
namedWindow( result_window, CV_WINDOW_AUTOSIZE );
/// Create Trackbar
char* trackbar_label = "Method: \n 0: SQDIFF \n 1: SQDIFF NORMED \n 2: TM CCORR \n 3: TM CCORR NORMED \n 4: TM COEFF \n 5: TM COEFF NORMED";
createTrackbar( trackbar_label, image_window, &match_method, max_Trackbar, MatchingMethod );
MatchingMethod( 0, 0 );
waitKey(0);
return 0;
}
/**
* @function MatchingMethod
* @brief Trackbar callback
*/
void MatchingMethod( int, void* )
{
/// Source image to display
Mat img_display;
img.copyTo( img_display );
/// Create the result matrix
int result_cols = img.cols - templ.cols + 1;
int result_rows = img.rows - templ.rows + 1;
result.create( result_rows, result_cols, CV_32FC1 );
/// Do the Matching and Normalize
matchTemplate( img, templ, result, match_method );
normalize( result, result, 0, 1, NORM_MINMAX, -1, Mat() );
/// Localizing the best match with minMaxLoc
double minVal; double maxVal; Point minLoc; Point maxLoc;
Point matchLoc;
minMaxLoc( result, &minVal, &maxVal, &minLoc, &maxLoc, Mat() );
/// For SQDIFF and SQDIFF_NORMED, the best matches are lower values. For all the other methods, the higher the better
if( match_method == CV_TM_SQDIFF || match_method == CV_TM_SQDIFF_NORMED )
{ matchLoc = minLoc; }
else
{ matchLoc = maxLoc; }
/// Show me what you got
rectangle( img_display, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
rectangle( result, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
imshow( image_window, img_display );
imshow( result_window, result );
cv::imwrite("rec.jpg", img_display);
return;
}
从这两种实现来看,它们之间最明显的区别是所使用图像的颜色格式。
在Python版本中,您按原样加载图像。由于您输入的图像是RGB(如变量名所暗示的那样),因此您将在彩色图像上进行模板匹配。
img_rgb = cv2.imread(img_name)
img_rgb2 = cv2.imread(img_name2)
但是,在C ++中,由于将1
as作为第二个参数传递,因此将图像加载为灰度图像。
img = imread( argv[1], 1 );
templ = imread( argv[2], 1 );
根据cv::matchTemplate
文档:
在彩色图像的情况下,分子的模板求和和分母的每个求和都在所有通道上进行,每个通道使用单独的平均值。即,该功能可以获取颜色模板和彩色图像。结果仍然是单通道图像,更易于分析。
这表明,将其应用于3通道图像时,与将其应用于同一图像的单通道版本相比,很有可能获得不同的结果。
本文收集自互联网,转载请注明来源。
如有侵权,请联系[email protected] 删除。
我来说两句