OpenCV(3.4.2): error: (-215:Assertion failed) with Template Matching Method
OpenCV(3.4.2): error: (-215:Assertion failed) with Template Matching Method
我使用规范化作为模板匹配的预处理方法。
但是，当我 运行 代码
时遇到错误
错误：
错误：OpenCV（3.4.2）/opt/concourse/worker/volumes/live/9523d527-1b9e-48e0-7ed0-a36adde286f0/volume/opencv-suite_1535558719691/work/modules/imgproc/src/templmatch.cpp:1102：错误：（-215：断言失败）（深度== 0 ||深度== 5）&&类型== _templ.type（） && _img.dims() <= 2 函数 'matchTemplate'
这是我的预处理方法：
def Image_Preprocessing (image):
Gray_image = cv2.cvtColor(image , cv2.COLOR_BGR2GRAY) # converting the image to grayscale image
resized_image = cv2.resize(Gray_image, (width, height)) # Resize the image
mean, stdDev = cv2.meanStdDev(resized_image) #Get Mean and Standard-deviation
Normalized_image = (resized_image-mean)/stdDev #Normalize the image
# Scale the normalized values to integer range
Normalized_image -= Normalized_image.min()
Normalized_image /= Normalized_image.max()
Normalized_image *= 255 # [0, 255] range
return Normalized_image
我该如何解决这个问题？
无论如何，你应该验证@HansHirse 的答案，如果问题甚至是你的预处理，你可以试试这个：
def Image_Preprocessing (image):
Gray_image = cv2.cvtColor(image , cv2.COLOR_BGR2GRAY) # converting the image to grayscale image
resized_image = cv2.resize(Gray_image, (width, height)) # Resize the image
Normalized_image = np.array(np.divide(resized_image, np.amax(resized_image)), dtype=np.float64) # Normalizes to float 0 - 1, ensure float
# Scale the normalized values to integer range
Normalized_image *= 255 # [0, 255] range
Normalized_image = np.uint8(Normalized_image)
return Normalized_image
这个returnsuint8图片，如果你的模板也是uint8，应该没有问题。
我使用规范化作为模板匹配的预处理方法。 但是，当我 运行 代码
时遇到错误错误： 错误：OpenCV（3.4.2）/opt/concourse/worker/volumes/live/9523d527-1b9e-48e0-7ed0-a36adde286f0/volume/opencv-suite_1535558719691/work/modules/imgproc/src/templmatch.cpp:1102：错误：（-215：断言失败）（深度== 0 ||深度== 5）&&类型== _templ.type（） && _img.dims() <= 2 函数 'matchTemplate'
这是我的预处理方法：
def Image_Preprocessing (image):
Gray_image = cv2.cvtColor(image , cv2.COLOR_BGR2GRAY) # converting the image to grayscale image
resized_image = cv2.resize(Gray_image, (width, height)) # Resize the image
mean, stdDev = cv2.meanStdDev(resized_image) #Get Mean and Standard-deviation
Normalized_image = (resized_image-mean)/stdDev #Normalize the image
# Scale the normalized values to integer range
Normalized_image -= Normalized_image.min()
Normalized_image /= Normalized_image.max()
Normalized_image *= 255 # [0, 255] range
return Normalized_image
我该如何解决这个问题？
无论如何，你应该验证@HansHirse 的答案，如果问题甚至是你的预处理，你可以试试这个：
def Image_Preprocessing (image):
Gray_image = cv2.cvtColor(image , cv2.COLOR_BGR2GRAY) # converting the image to grayscale image
resized_image = cv2.resize(Gray_image, (width, height)) # Resize the image
Normalized_image = np.array(np.divide(resized_image, np.amax(resized_image)), dtype=np.float64) # Normalizes to float 0 - 1, ensure float
# Scale the normalized values to integer range
Normalized_image *= 255 # [0, 255] range
Normalized_image = np.uint8(Normalized_image)
return Normalized_image
这个returnsuint8图片，如果你的模板也是uint8，应该没有问题。