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我已經搜索了很多，但是這個錯誤主要是當圖像很大時。我的圖片不是很大，我仍然收到這個錯誤。我試圖通過openCV識別手寫數字。我檢查過我的圖像是否正在加載，所以這不是問題。這是我的代碼。小圖像OpenCV ssize.area（）> 0錯誤
im = cv2.imread(filename,1)
# Convert to grayscale and apply Gaussian filtering
# Convert image from one color space to another
im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
im_gray = cv2.GaussianBlur(im_gray, (5, 5), 0)
# Threshold to binary the image
ret, im_th = cv2.threshold(im_gray, 90, 255, cv2.THRESH_BINARY_INV)
# Find contours in the image
_, ctrs, hier = cv2.findContours(im_th.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# Get rectangles contains each contour
rects = [cv2.boundingRect(ctr) for ctr in ctrs]
# For each rectangular region, calculate HOG features and predict
# the digit using Linear SVM.
for rect in rects:
# Draw the rectangles
cv2.rectangle(im, (rect[0], rect[1]), (rect[0] + rect[2], rect[1] + rect[3]), (0, 255, 0), 2)
# Make the rectangular region around the digit
leng = int(rect[3] * 1.6)
pt1 = int(rect[1] + rect[3] // 2 - leng // 2)
pt2 = int(rect[0] + rect[2] // 2 - leng // 2)
roi = im_th[pt1:pt1+leng, pt2:pt2+leng]
# Resize the image
roi = cv2.resize(roi, (28, 28), interpolation=cv2.INTER_AREA)
roi = cv2.dilate(roi, (3, 3))
# Calculate the HOG features
roi_hog_fd = hog(roi, orientations=9, pixels_per_cell=(14, 14), cells_per_block=(1, 1), visualise=False)
nbr = clf.predict(np.array([roi_hog_fd], 'float64'))
我對這兩條線有疑問，他們是從錯誤出現的地方。
ret, im_th = cv2.threshold(im_gray, 90, 255, cv2.THRESH_BINARY_INV)
和
roi = cv2.resize(roi, (28, 28), interpolation=cv2.INTER_AREA)
它也不接受油漆圖象。任何幫助，將不勝感激。
