In this image I am trying to detect horizontal lines. The code works well when image is not skewed. However, it is not working on such skewed images. I have tried this method to detect the right angle by histogram but many times is actually making it more skewed - python-opencv-skew-correction-for-ocr
Below is code to detect horizontal lines:
gray=cv2.cvtColor(img_final_bin,cv2.COLOR_BGR2GRAY)
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (100,1))
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
detected_lines = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, horizontal_kernel, iterations=2)
cnts, hierarchy = cv2.findContours(detected_lines, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
boundingBoxes = [list(cv2.boundingRect(c)) for c in cnts]
Below is the code for skew correction, which is giving wrong results to me:
def correct_skew(image, delta=0.001, limit=3):
def determine_score(arr, angle):
data = inter.rotate(arr, angle, reshape=False, order=0)
histogram = np.sum(data, axis=1)
score = np.sum((histogram[1:] - histogram[:-1]) ** 2)
return histogram, score
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
print("thresh", thresh.shape)
thresh1 = thresh[0:500, 0:500]
print("thresh1", thresh1.shape)
scores = []
angles = np.arange(-limit, limit + delta, delta)
for i, angle in enumerate(angles):
histogram, score = determine_score(thresh1, angle)
scores.append(score)
# if i%100 == 0:
# print(score, angle, len(angles), i)
best_angle = angles[scores.index(max(scores))]
(h, w) = image.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, best_angle, 1.0)
rotated = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC, \
borderMode=cv2.BORDER_REPLICATE)
return best_angle, rotated
