import math import cmath import traceback import numpy as np import cv2 # 图片旋转 from format_convert.judge_platform import get_platform def rotate_bound(image, angle): try: # 获取宽高 (h, w) = image.shape[:2] (cX, cY) = (w // 2, h // 2) # 提取旋转矩阵 sin cos M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0) cos = np.abs(M[0, 0]) sin = np.abs(M[0, 1]) # 计算图像的新边界尺寸 nW = int((h * sin) + (w * cos)) # nH = int((h * cos) + (w * sin)) nH = h # 调整旋转矩阵 M[0, 2] += (nW / 2) - cX M[1, 2] += (nH / 2) - cY return cv2.warpAffine(image, M, (nW, nH),flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE) except Exception as e: print("rotate_bound", e) # 获取图片旋转角度 def get_minAreaRect(image): try: gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) ret, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY) if get_platform() == "Windows": cv2.imshow("binary", binary) cv2.waitKey(0) contours, hier = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # 绘制矩形 cv2.drawContours(image, contours, 0, (255, 0, 255), 3) if get_platform() == "Windows": cv2.imshow("image", image) cv2.waitKey(0) for c in contours: #遍历轮廓 rect = cv2.minAreaRect(c) #生成最小外接矩形 box_ = cv2.boxPoints(rect) h = abs(box_[3, 1] - box_[1, 1]) w = abs(box_[3, 0] - box_[1, 0]) print("宽，高", w, h) # 只保留需要的轮廓 if h > 3000 or w > 2200: continue if h < 2500 or w < 1500: continue # 计算最小面积矩形的坐标 box = cv2.boxPoints(rect) # 将坐标规范化为整数 box = np.int0(box) # 获取矩形相对于水平面的角度 angle = rect[2] if angle > 0: if abs(angle) > 45: angle = 90 - abs(angle) else: if abs(angle) > 45: angle = (90 - abs(angle)) print("轮廓数量", len(contours)) return image, box, angle except Exception as e: print("get_minAreaRect", traceback.print_exc()) return [-1], [-1], [-1] def get_table_line(image): # 二值化 gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) binary = cv2.adaptiveThreshold(~gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 203, -10) # cv2.imshow("cell", binary) # cv2.waitKey(0) # 轮廓 kernel_row = np.array([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]], np.float32) kernel_col = np.array([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]], np.float32) # Sobel # kernel_row = np.array([[-1, -2, -1], [0, 0, 0], [1, 2, 1]], np.float32) # kernel_col = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], np.float32) # binary = cv2.filter2D(binary, -1, kernel=kernel) binary_row = cv2.filter2D(binary, -1, kernel=kernel_row) binary_col = cv2.filter2D(binary, -1, kernel=kernel_col) # cv2.imshow("custom_blur_demo", binary) # cv2.waitKey(0) # rows, cols = binary.shape # scale = 20 # # 识别横线 # kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (cols//scale, 1)) # erodedcol = cv2.erode(binary_row, kernel, iterations=1) # cv2.imshow("Eroded Image", erodedcol) # cv2.waitKey(0) # dilatedcol = cv2.dilate(erodedcol, kernel, iterations=3) # cv2.imshow("Dilated Image", dilatedcol) # cv2.waitKey(0) # # # 识别竖线 # scale = 20 # kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, rows//scale)) # erodedrow = cv2.erode(binary_col, kernel, iterations=1) # cv2.imshow("Eroded Image", erodedrow) # cv2.waitKey(0) # dilatedrow = cv2.dilate(erodedrow, kernel, iterations=3) # cv2.imshow("Dilated Image", dilatedrow) # cv2.waitKey(0) # # # 标识表格 # merge = cv2.add(dilatedcol, dilatedrow) # cv2.imshow("add Image", merge) # cv2.imwrite('table_outline.jpg', merge) # cv2.waitKey(0) return binary_row def detect_line(img): try: gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) edges = cv2.Canny(gray, 100, 1000) # cv2.imshow("edges", edges) # cv2.waitKey(0) lines = cv2.HoughLines(edges, 1, np.pi/180, 200) if lines is None or len(lines) == 0: return 0 angle_list = [] for i in range(len(lines)): # 第一个元素是距离rho rho = lines[i][0][0] # 第二个元素是角度theta theta = lines[i][0][1] a = np.cos(theta) b = np.sin(theta) x0 = a*rho y0 = b*rho x1 = float(x0 + 1000*(-b)) y1 = float(y0 + 1000*a) x2 = float(x0 - 1000*(-b)) y2 = float(y0 - 1000*a) if x2-x1 == 0 or y2-y1 == 0 or x2-x1 <= 0.01 or y2-y1 <= 0.01: continue k = -(y2-y1) / (x2-x1) if abs(k) <= 5: # h = math.atan(k) # angle = math.degrees(h) angle = np.arctan(k) * (180/math.pi) # 调整 angle_list.append(angle) # print(angle) # cv2.line(img, (x1, y1), (x2, y2), (0, 0, 255), 2) angle_sum = 0 for a in angle_list: angle_sum += a angle_avg = angle_sum/len(angle_list) print("angle_avg", angle_avg) return angle_avg except Exception as e: print("detect_line", e) def get_rotated_image_old(image, output_path): try: angle = detect_line(image) if angle != 0: rotated = rotate_bound(image, angle) # cv2.putText(rotated, "angle: {:.3f} ".format(angle), # (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) # show the output image # cv2.imshow("imput", image) # cv2.waitKey(0) # cv2.imshow("output", rotated) # cv2.waitKey(0) print("[INFO] angle: {:.3f}".format(angle)) cv2.imwrite(output_path, rotated) return True else: print("angle", angle) return False except Exception as e: print("get_rotated_image", e) return False def rotate_bound(image, angle): # 获取宽高 (h, w) = image.shape[:2] (cX, cY) = (w // 2, h // 2) # 提取旋转矩阵 sin cos M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0) cos = np.abs(M[0, 0]) sin = np.abs(M[0, 1]) # 计算图像的新边界尺寸 nW = int((h * sin) + (w * cos)) # nH = int((h * cos) + (w * sin)) nH = h # 调整旋转矩阵 M[0, 2] += (nW / 2) - cX M[1, 2] += (nH / 2) - cY return cv2.warpAffine(image, M, (nW, nH),flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE) # 获取图片旋转角度 def get_minAreaRect(image): gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) gray = cv2.bitwise_not(gray) thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1] coords = np.column_stack(np.where(thresh > 0)) return cv2.minAreaRect(coords) def get_rotated_image(image, output_path): try: image_temp, box, angle = get_minAreaRect(image) if angle == [-1]: return [-1] # angle = get_minAreaRect(image)[-1] if abs(angle) >= 15: angle = 0 rotated = rotate_bound(image, angle) # cv2.putText(rotated, "angle: {:.2f} ".format(angle), # (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) # show the output image # print("[INFO] angle: {:.3f}".format(angle)) if not angle: cv2.imwrite(output_path, rotated) # cv2.imshow("input", image) # cv2.waitKey(0) # cv2.imshow("output", rotated) # cv2.waitKey(0) return True except cv2.error: traceback.print_exc() return [-3] except Exception as e: traceback.print_exc() return [-1] if __name__ == '__main__': temp_path = "temp/complex/8.png" # temp_path = "temp/92d885dcb77411eb914300163e0ae709/92d8ef5eb77411eb914300163e0ae709_pdf_page1.png" image = cv2.imread(temp_path) get_rotated_image(image, temp_path) # test()