From 5e889c6dd896e32687b4609462921f9799bb75ff Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=A4=9A=E5=BD=A9AI?= <18633108@qq.com> Date: Fri, 8 Nov 2024 16:53:59 +0800 Subject: [PATCH] =?UTF-8?q?=E5=A2=9E=E5=8A=A0=E9=BB=91=E8=89=B2=E8=BE=B9?= =?UTF-8?q?=E7=BC=98=E8=A3=81=E5=89=AA?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- GridImageSplitter.py | 163 ++++++++++++++++++++++++++++++------------- 1 file changed, 113 insertions(+), 50 deletions(-) diff --git a/GridImageSplitter.py b/GridImageSplitter.py index 2ca21c7..2854127 100644 --- a/GridImageSplitter.py +++ b/GridImageSplitter.py @@ -21,67 +21,136 @@ class GridImageSplitter: def remove_external_borders(self, img_np): """ - 处理图片外部边缘的白边 + 处理外部边缘，加强对黑色边框的检测 """ if img_np.size == 0 or img_np is None: return img_np - # 转换为HSV颜色空间 + # 转换为多个色彩空间 + gray = cv2.cvtColor(img_np, cv2.COLOR_RGB2GRAY) hsv = cv2.cvtColor(img_np, cv2.COLOR_RGB2HSV) - # 获取饱和度和亮度通道 - sat = hsv[:, :, 1] - val = hsv[:, :, 2] - - # 定义白色区域的条件 - is_white = (sat < 25) & (val > 230) - height, width = img_np.shape[:2] - # 查找非白色区域的边界 - y_nonwhite = np.where(np.any(~is_white, axis=1))[0] - x_nonwhite = np.where(np.any(~is_white, axis=0))[0] + # 最小裁剪量 + min_trim = 18 + # 检查范围扩大到30像素 + check_width = 30 - if len(y_nonwhite) == 0 or len(x_nonwhite) == 0: + def is_black_region(region): + """检查区域是否为黑色区域""" + if len(region.shape) == 3: + region_gray = cv2.cvtColor(region, cv2.COLOR_RGB2GRAY) + else: + region_gray = region + + # 计算暗色像素的比例 + dark_ratio = np.mean(region_gray < 40) + # 如果超过60%的像素是暗色的，认为是黑边 + return dark_ratio > 0.6 + + def is_white_region(region): + """检查区域是否为白色区域""" + if len(region.shape) == 3: + region_hsv = cv2.cvtColor(region, cv2.COLOR_RGB2HSV) + sat_mean = np.mean(region_hsv[:,:,1]) + val_mean = np.mean(region_hsv[:,:,2]) + return sat_mean < 30 and val_mean > 225 + return False + + def find_border(gray_img, is_vertical=True, from_start=True): + """查找边界""" + if is_vertical: + total_size = width + chunk_size = 5 # 每次检查5个像素 + else: + total_size = height + chunk_size = 5 + + if from_start: + range_iter = range(0, total_size-chunk_size, chunk_size) + else: + range_iter = range(total_size-chunk_size, 0, -chunk_size) + + for i in range_iter: + if is_vertical: + chunk = img_np[:, i:i+chunk_size] if from_start else img_np[:, i-chunk_size:i] + else: + chunk = img_np[i:i+chunk_size, :] if from_start else img_np[i-chunk_size:i, :] + + # 分别检查黑边和白边 + if not (is_black_region(chunk) or is_white_region(chunk)): + return i if from_start else i + + return min_trim if from_start else total_size - min_trim + + # 检测左边界 + left = find_border(gray, is_vertical=True, from_start=True) + + # 检测右边界 + right = find_border(gray, is_vertical=True, from_start=False) + + # 检测上边界 + top = find_border(gray, is_vertical=False, from_start=True) + + # 检测下边界 + bottom = find_border(gray, is_vertical=False, from_start=False) + + # 强制应用最小裁剪 + # 检查左侧边缘 + left_region = gray[:, :check_width] + if np.mean(left_region < 40) > 0.3: # 如果有超过30%的暗色像素 + left = max(left, min_trim) + + # 检查右侧边缘 + right_region = gray[:, -check_width:] + if np.mean(right_region < 40) > 0.3: # 如果有超过30%的暗色像素 + right = min(right, width - min_trim) + + # 检查上边缘 + top_region = gray[:check_width, :] + if np.mean(top_region < 40) > 0.3: + top = max(top, min_trim) + + # 检查下边缘 + bottom_region = gray[-check_width:, :] + if np.mean(bottom_region < 40) > 0.3: + bottom = min(bottom, height - min_trim) + + # 确保裁剪合理 + if (right - left) < width * 0.5 or (bottom - top) < height * 0.5: return img_np - - # 获取边界 - top = y_nonwhite[0] - bottom = y_nonwhite[-1] + 1 - left = x_nonwhite[0] - right = x_nonwhite[-1] + 1 - - # 添加小边距 - margin = 1 - top = max(0, top - margin) - bottom = min(height, bottom + margin) - left = max(0, left - margin) - right = min(width, right + margin) - - # 裁剪图像 + + # 应用裁剪 cropped = img_np[top:bottom, left:right] + # 进行二次检查，确保没有遗漏的黑边 + if cropped.shape[1] > 2 * min_trim: + right_edge = cv2.cvtColor(cropped[:, -min_trim:], cv2.COLOR_RGB2GRAY) + if np.mean(right_edge < 40) > 0.3: + cropped = cropped[:, :-min_trim] + return cropped - - def detect_white_border(self, img_strip, is_vertical=True): + def detect_split_borders(self, img_strip, is_vertical=True): """ - 检测条带中的白色边界，使用更保守的阈值 + 检测分割线区域的边框 """ hsv = cv2.cvtColor(img_strip, cv2.COLOR_RGB2HSV) + lab = cv2.cvtColor(img_strip, cv2.COLOR_RGB2LAB) - # 获取饱和度和亮度 sat = hsv[:, :, 1] val = hsv[:, :, 2] + l_channel = lab[:, :, 0] - # 使用更严格的白色区域条件 - is_white = (sat < 10) & (val > 248) # 更严格的条件 + # 检测白色和黑色区域 + is_border = ((sat < 10) & (val > 248)) | (l_channel < 30) if is_vertical: - white_ratios = np.mean(is_white, axis=1) - indices = np.where(white_ratios < 0.95)[0] # 更高的阈值 + border_ratios = np.mean(is_border, axis=1) + indices = np.where(border_ratios < 0.95)[0] else: - white_ratios = np.mean(is_white, axis=0) - indices = np.where(white_ratios < 0.95)[0] # 更高的阈值 + border_ratios = np.mean(is_border, axis=0) + indices = np.where(border_ratios < 0.95)[0] if len(indices) == 0: return 0, img_strip.shape[1] if is_vertical else img_strip.shape[0] @@ -90,7 +159,7 @@ class GridImageSplitter: def adjust_split_line(self, img_np, split_pos, is_vertical=True, margin=15): """ - 调整分割线附近的白色边界，使用更小的检测范围 + 调整分割线附近的边界 """ height, width = img_np.shape[:2] @@ -98,10 +167,9 @@ class GridImageSplitter: left_bound = max(0, split_pos - margin) right_bound = min(width, split_pos + margin) strip = img_np[:, left_bound:right_bound] - start, end = self.detect_white_border(strip, False) + start, end = self.detect_split_borders(strip, False) - # 添加偏移以保留更多内容 - start = max(0, start - 5) # 向外扩展几个像素 + start = max(0, start - 5) end = min(strip.shape[1], end + 5) return left_bound + start, left_bound + end @@ -109,18 +177,14 @@ class GridImageSplitter: top_bound = max(0, split_pos - margin) bottom_bound = min(height, split_pos + margin) strip = img_np[top_bound:bottom_bound, :] - start, end = self.detect_white_border(strip, True) + start, end = self.detect_split_borders(strip, True) - # 添加偏移以保留更多内容 - start = max(0, start - 5) # 向外扩展几个像素 + start = max(0, start - 5) end = min(strip.shape[0], end + 5) return top_bound + start, top_bound + end def find_split_positions(self, image, num_splits, is_vertical, split_method): - """ - 找到分割位置 - """ if split_method == "uniform": size = image.shape[1] if is_vertical else image.shape[0] return [i * size // (num_splits + 1) for i in range(1, num_splits + 1)] @@ -145,12 +209,11 @@ class GridImageSplitter: 处理第二行图片 - 固定裁剪上部30像素 """ processed_cells = [] - trim_pixels = 30 # 固定裁剪像素数 + trim_pixels = 30 for cell in cells: height = cell.shape[0] if height > trim_pixels: - # 从第30个像素开始裁剪 cropped = cell[trim_pixels:, :] processed_cells.append(cropped) else: