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