""" 透视矫正三步流程： Step1: Gemini 去背景 → 纯白背景 Step2: OpenCV 在白背景图上检测四角 → warpPerspective 展平 Step3: Gemini 对展平结果做高清增强 用法： python perspective_fix.py <图片路径或URL> [--debug] [--skip-step1] [--skip-step3] """ import sys, io sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8", errors="replace") sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding="utf-8", errors="replace") import os, asyncio, uuid, tempfile import numpy as np import cv2 from dotenv import load_dotenv load_dotenv() _OUTPUT_DIR = os.getenv("RESULT_IMAGE_DIR", "results") os.makedirs(_OUTPUT_DIR, exist_ok=True) # ═══════════════════════════════════════════════════════════════ # Gemini 辅助函数 # ═══════════════════════════════════════════════════════════════ async def _gemini_call(input_path: str, output_path: str, prompt: str, aspect_ratio: str = "1:1", label: str = "") -> bool: from services.service_gemini import GeminiExtractV2Service service = GeminiExtractV2Service() try: ok, msg, _ = await service.extract_pattern( input_path=input_path, output_path=output_path, custom_prompt=prompt, aspect_ratio=aspect_ratio, ) status = "成功" if ok else "失败" print(f" [{label}] Gemini {status}: {msg[:80]}") return ok and os.path.exists(output_path) except Exception as e: print(f" [{label}] Gemini 异常: {e}") return False finally: await service.cleanup() PROMPT_WHITE_BG = ( "请处理这张图片：\n" "1. 识别图中的地毯/地垫/印花布料/产品本体作为主体\n" "2. 去掉主体上面放置的所有物品（杯子、碗、餐具、装饰品等），只保留地垫本身\n" "3. 把所有背景（桌面、地板、墙壁、阴影）全部替换为纯白色(#FFFFFF)\n" "4. 保持地垫/产品的颜色、图案、边缘完全不变\n" "输出：只有主体产品、纯白背景、无杂物的干净产品图。" ) # 当第一次去背景效果不好时（白色覆盖率过低），用更强硬的提示词重试 PROMPT_WHITE_BG_STRONG = ( "严格执行：将这张图的背景彻底替换为纯白色 RGB(255,255,255)。\n" "只保留图片中央的产品/地毯/布料主体，其他所有区域（桌面/地板/墙/阴影/物品）" "一律改为纯白色。产品边缘要干净锐利，不留任何半透明或灰色区域。\n" "重要：不论主体上摆放了什么东西，统统去掉，只输出产品本身+白色背景。" ) PROMPT_ENHANCE = ( "请对这张已展平的图案进行高清增强：提升整体清晰度和色彩饱和度，" "修复边缘锯齿，补全缺失细节，输出印刷级高质量平面图，背景保持纯白。" ) # Step3 增强失败时的兜底提示词（更简单，成功率更高） PROMPT_ENHANCE_SIMPLE = ( "请提升这张图片的清晰度和画质，输出高清版本，背景保持纯白。" ) def _measure_white_coverage(image: np.ndarray) -> float: """返回图片中白色像素的百分比，用于判断去背景效果""" gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) _, mask = cv2.threshold(gray, 245, 255, cv2.THRESH_BINARY) return float(np.sum(mask == 255)) / mask.size def _color_match(source: np.ndarray, target: np.ndarray, strength: float = 0.75, exclude_white: bool = True) -> np.ndarray: """ 将 target 的色调匹配到 source（类 PS「匹配颜色」）。 使用 LAB 色彩空间 Reinhard 均值/标准差迁移。 Args: source: 原图（色彩参考来源） target: 待调整图（处理后结果） strength: 迁移强度 0.0-1.0，推荐 0.6-0.85 exclude_white: 统计时排除白色像素，避免背景影响肤色/图案计算 Returns: 调色后的 BGR 图像 """ src_f = source.astype(np.float32) / 255.0 tgt_f = target.astype(np.float32) / 255.0 src_lab = cv2.cvtColor(src_f, cv2.COLOR_BGR2Lab) tgt_lab = cv2.cvtColor(tgt_f, cv2.COLOR_BGR2Lab) result = tgt_lab.copy() for ch in range(3): if exclude_white: # 排除极亮像素（L > 95）统计，只看图案区域 src_mask = src_lab[:, :, 0] < 95 tgt_mask = tgt_lab[:, :, 0] < 95 src_vals = src_lab[:, :, ch][src_mask] tgt_vals = tgt_lab[:, :, ch][tgt_mask] else: src_vals = src_lab[:, :, ch].ravel() tgt_vals = tgt_lab[:, :, ch].ravel() if src_vals.size == 0 or tgt_vals.size == 0: continue src_mean, src_std = float(src_vals.mean()), float(src_vals.std()) tgt_mean, tgt_std = float(tgt_vals.mean()), float(tgt_vals.std()) if tgt_std < 1e-6: continue # Reinhard 迁移：先归一化到目标，再重映射到源分布 shifted = (tgt_lab[:, :, ch] - tgt_mean) / tgt_std * src_std + src_mean # 按 strength 混合：strength=1 完全迁移，0 保持不变 result[:, :, ch] = shifted * strength + tgt_lab[:, :, ch] * (1.0 - strength) result_bgr = cv2.cvtColor(result, cv2.COLOR_Lab2BGR) result_bgr = np.clip(result_bgr * 255, 0, 255).astype(np.uint8) print(f" [颜色匹配] 强度={strength:.0%} | " f"源均值L={src_lab[:,:,0].mean():.1f} → 目标均值L={tgt_lab[:,:,0].mean():.1f}") return result_bgr # ═══════════════════════════════════════════════════════════════ # OpenCV 透视矫正 # ═══════════════════════════════════════════════════════════════ def order_points(pts: np.ndarray) -> np.ndarray: """ 把四个点排列为 [左上, 右上, 右下, 左下]。 使用质心角度排序，对矩形、菱形、平行四边形等各种透视形状均适用。 """ cx, cy = pts[:, 0].mean(), pts[:, 1].mean() # 计算每个点相对质心的角度（从正上方顺时针） angles = np.arctan2(pts[:, 1] - cy, pts[:, 0] - cx) # 顺时针排序：从右上开始（角度最小的） order = np.argsort(angles) sorted_pts = pts[order] # 找到最左上角作为起点（x+y 最小） s = sorted_pts.sum(axis=1) start = np.argmin(s) # 从左上角开始顺时针排列 → [左上, 右上, 右下, 左下] indices = [(start + i) % 4 for i in range(4)] rect = sorted_pts[indices].astype("float32") return rect def four_point_transform(image: np.ndarray, pts: np.ndarray) -> np.ndarray: rect = order_points(pts) tl, tr, br, bl = rect w1 = np.linalg.norm(br - bl) w2 = np.linalg.norm(tr - tl) h1 = np.linalg.norm(tr - br) h2 = np.linalg.norm(tl - bl) W = int(max(w1, w2)) H = int(max(h1, h2)) print(f" [CV] 角点: TL={tl.astype(int)} TR={tr.astype(int)} BR={br.astype(int)} BL={bl.astype(int)}") print(f" [CV] 矫正后目标尺寸: {W}x{H}") dst = np.array([ [0, 0 ], [W - 1, 0 ], [W - 1, H - 1], [0, H - 1], ], dtype="float32") M = cv2.getPerspectiveTransform(rect, dst) warped = cv2.warpPerspective( image, M, (W, H), flags=cv2.INTER_LANCZOS4, borderMode=cv2.BORDER_CONSTANT, borderValue=(255, 255, 255), ) return warped def _detect_bg_color(image: np.ndarray, corner_size: int = 24) -> np.ndarray: """ 从图片四个角落采样，估计背景颜色（BGR）。 适用于白色、米色、黄色、灰色等各种背景。 """ H, W = image.shape[:2] cs = min(corner_size, H // 5, W // 5) corners = [ image[:cs, :cs], # 左上 image[:cs, W-cs:], # 右上 image[H-cs:, :cs], # 左下 image[H-cs:, W-cs:], # 右下 ] pixels = np.concatenate([c.reshape(-1, 3) for c in corners], axis=0) bg = np.median(pixels, axis=0).astype(np.uint8) return bg # BGR def tool_trim_white_border(image: np.ndarray, tolerance: int = 18, bg_ratio: float = 0.90, padding: int = 4) -> tuple[np.ndarray, bool, dict]: """ 【Tool】智能背景边裁切（支持任意背景色：白/黄/米/灰等）。 算法： 1. 从四角采样估计背景色 2. 逐行/列扫描：若该行/列中 bg_ratio 以上的像素与背景色差异 <= tolerance，则为背景行/列 3. 找到内容区域边界后裁切 Returns: (裁切后图片, 是否裁切, 详情dict) """ H, W = image.shape[:2] bg_color = _detect_bg_color(image) img_f = image.astype(np.int32) # 每个像素与背景色的最大通道差异 diff = np.abs(img_f - bg_color.astype(np.int32)).max(axis=2) # H x W is_bg = diff <= tolerance # True = 接近背景色 row_bg_ratio = is_bg.mean(axis=1) # 每行的背景像素占比 col_bg_ratio = is_bg.mean(axis=0) # 每列的背景像素占比 top = next((i for i in range(H) if row_bg_ratio[i] < bg_ratio), H) bottom = next((i for i in range(H-1,-1,-1) if row_bg_ratio[i] < bg_ratio), -1) + 1 left = next((i for i in range(W) if col_bg_ratio[i] < bg_ratio), W) right = next((i for i in range(W-1,-1,-1) if col_bg_ratio[i] < bg_ratio), -1) + 1 border_top = top border_bottom = H - bottom border_left = left border_right = W - right max_border = max(border_top, border_bottom, border_left, border_right) bg_hex = "#{:02X}{:02X}{:02X}".format(int(bg_color[2]), int(bg_color[1]), int(bg_color[0])) info = {"top": border_top, "bottom": border_bottom, "left": border_left, "right": border_right, "bg_color": bg_hex} if max_border < 5: print(f" [裁边] 背景色{bg_hex} | 上{border_top} 下{border_bottom} 左{border_left} 右{border_right}px → 无需裁切") return image, False, info y1 = max(0, top - padding) y2 = min(H, bottom + padding) x1 = max(0, left - padding) x2 = min(W, right + padding) cropped = image[y1:y2, x1:x2] ch, cw = cropped.shape[:2] print(f" [裁边] 背景色{bg_hex} | 上{border_top} 下{border_bottom} 左{border_left} 右{border_right}px → 裁切 {W}x{H}→{cw}x{ch}") return cropped, True, info async def tool_color_match(orig_img: np.ndarray, result_img: np.ndarray, strength: float = 0.75) -> np.ndarray: """【Tool】颜色匹配（封装版，供 AI 决策层调用）""" return _color_match(orig_img, result_img, strength=strength) async def ai_decide_postprocess(orig_img: np.ndarray, result_img: np.ndarray) -> dict: """ 【AI 决策层】用视觉模型分析出图效果，决定是否需要颜色匹配和白边裁切。 Returns: { "need_color_match": bool, "color_strength": float, # 0.5-0.9 "need_trim": bool, "reason": str, } """ import base64 from dotenv import load_dotenv load_dotenv() api_key = os.getenv("OPENAI_API_KEY") base_url = os.getenv("OPENAI_BASE_URL") model = os.getenv("VISION_MODEL", "glm-4v-flash") # 无 API 时默认两个都做 if not api_key: return {"need_color_match": True, "color_strength": 0.75, "need_trim": True, "reason": "无API Key，默认执行"} def _encode(img: np.ndarray) -> str: resized = cv2.resize(img, (512, 512)) _, buf = cv2.imencode(".jpg", resized, [cv2.IMWRITE_JPEG_QUALITY, 80]) return base64.b64encode(buf).decode() orig_b64 = _encode(orig_img) result_b64 = _encode(result_img) prompt = ( "你是图片后处理决策助手。图一是原图，图二是AI处理后的结果图。请判断：\n\n" "【问题1】颜色差异：处理后图片的整体色调与原图相比，差异是否明显？\n" "（明显=色调/饱和度/冷暖差异很大；轻微=有轻微偏差；无=颜色基本一致）\n\n" "【问题2】多余边框：处理后图片四周是否有不属于图案内容的多余空白边框？\n" "注意：边框颜色不一定是白色，也可能是黄色、米色、灰色等任何纯色。\n" "判断标准：图案内容的外围是否有一圈明显的纯色空白带。\n\n" "严格按格式回答（每行一个字段，不要多余内容）：\n" "颜色差异: <明显|轻微|无>\n" "多余边框: <有|无>\n" "边框位置: <有边框的方向如「上下」，没有则填无>" ) try: from openai import AsyncOpenAI client = AsyncOpenAI(base_url=base_url, api_key=api_key) response = await client.chat.completions.create( model=model, messages=[{ "role": "user", "content": [ {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{orig_b64}"}}, {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{result_b64}"}}, {"type": "text", "text": prompt}, ], }], ) text = response.choices[0].message.content or "" print(f" [AI决策] 原始回答: {text.strip()[:120]}") def _get(key): for line in text.splitlines(): line = line.strip() if line.startswith(key): return line.split(":", 1)[-1].strip() return "" color_level = _get("颜色差异") has_border = "有" in _get("多余边框") border_pos = _get("边框位置") strength_map = {"明显": 0.80, "轻微": 0.55, "无": 0.0} color_strength = strength_map.get(color_level, 0.75) need_color = color_strength > 0 reason = f"颜色差异={color_level or '?'}, 边框={'有('+border_pos+')' if has_border else '无'}" print(f" [AI决策] {reason} → 颜色匹配={'✓' if need_color else '✗'}(强度{color_strength:.0%}), 裁边={'✓' if has_border else '✗'}") return { "need_color_match": need_color, "color_strength": color_strength, "need_trim": has_border, "reason": reason, } except Exception as e: print(f" [AI决策] 调用失败({e})，默认执行颜色匹配+裁边") return {"need_color_match": True, "color_strength": 0.75, "need_trim": True, "reason": f"AI决策失败: {e}"} def _points_are_unique(pts: np.ndarray, min_dist: float = 20.0) -> bool: """检查4个角点两两之间距离都大于 min_dist，防止重复点导致退化变换""" for i in range(len(pts)): for j in range(i + 1, len(pts)): if np.linalg.norm(pts[i] - pts[j]) < min_dist: return False return True def find_quad(image: np.ndarray): """ 在白背景图上检测主体四边形角点。 策略（按优先级）： 1. 二值化 + approxPolyDP（epsilon 从小到大尝试） 2. 凸包取极值四点（最左/最右/最上/最下） 3. minAreaRect 四角 """ h, w = image.shape[:2] img_area = h * w gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # ── 获取主体轮廓 ────────────────────────────────────────── _, thresh = cv2.threshold(gray, 245, 255, cv2.THRESH_BINARY_INV) kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (20, 20)) closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel) cnts, _ = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) if not cnts: edges = cv2.Canny(gray, 30, 100) k2 = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 10)) closed = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, k2) cnts, _ = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) if not cnts: print(" [CV] 无法检测轮廓") return None c = max(cnts, key=cv2.contourArea) area = cv2.contourArea(c) print(f" [CV] 主体轮廓面积: {area:.0f} / {img_area} ({area/img_area*100:.1f}%)") if area < img_area * 0.05: print(" [CV] 面积太小，背景可能去除不完全") return None peri = cv2.arcLength(c, True) # ── 策略1：approxPolyDP，epsilon 逐步放大直到得到4个唯一角点 ── for eps_ratio in [0.02, 0.03, 0.04, 0.05, 0.06]: approx = cv2.approxPolyDP(c, eps_ratio * peri, True) pts = approx.reshape(-1, 2).astype("float32") if len(pts) == 4 and _points_are_unique(pts): print(f" [CV] approxPolyDP 成功 (eps={eps_ratio}), 4个唯一角点") return pts print(f" [CV] approxPolyDP eps={eps_ratio}: {len(pts)} 顶点，唯一={_points_are_unique(pts) if len(pts)==4 else 'N/A'}") # ── 策略2：凸包极值四点（最左/最上/最右/最下）───────────── hull = cv2.convexHull(c).reshape(-1, 2).astype("float32") if len(hull) >= 4: # 取4个极值方向的点 left = hull[np.argmin(hull[:, 0])] # 最左 right = hull[np.argmax(hull[:, 0])] # 最右 top = hull[np.argmin(hull[:, 1])] # 最上 bottom = hull[np.argmax(hull[:, 1])] # 最下 pts = np.array([left, top, right, bottom], dtype="float32") if _points_are_unique(pts): print(f" [CV] 使用凸包极值四点: L={left.astype(int)} T={top.astype(int)} R={right.astype(int)} B={bottom.astype(int)}") return pts # ── 策略3：minAreaRect 四角（兜底）───────────────────────── print(f" [CV] 兜底：使用 minAreaRect") rect = cv2.minAreaRect(c) box = cv2.boxPoints(rect).astype("float32") return box def save_debug_img(image: np.ndarray, pts, path: str): """保存带角点标注的调试图""" dbg = image.copy() if pts is not None: rect = order_points(pts) labels = ["TL", "TR", "BR", "BL"] colors = [(0,0,255), (0,255,0), (255,0,0), (0,165,255)] for i, (px, py) in enumerate(rect): cv2.circle(dbg, (int(px), int(py)), 12, colors[i], -1) cv2.putText(dbg, labels[i], (int(px)+15, int(py)), cv2.FONT_HERSHEY_SIMPLEX, 1.2, colors[i], 3) box = rect.reshape((-1,1,2)).astype(np.int32) cv2.polylines(dbg, [box], True, (0,0,255), 3) cv2.imwrite(path, dbg, [cv2.IMWRITE_JPEG_QUALITY, 90]) print(f" [Debug] 调试图: {path}") # ═══════════════════════════════════════════════════════════════ # 主流程 # ═══════════════════════════════════════════════════════════════ async def process(src: str, debug: bool = False, skip_step1: bool = False, skip_step3: bool = False) -> str | None: uid = uuid.uuid4().hex tmp = [] # 临时文件列表，最后统一清理 # ── 下载（URL 情况）────────────────────────────────────── if src.startswith("http"): import aiohttp dl = os.path.join(tempfile.gettempdir(), f"pfix_dl_{uid}.jpg") tmp.append(dl) print("[下载] 原图中...") async with aiohttp.ClientSession(headers={ "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)", "Referer": "https://www.taobao.com/", }) as sess: async with sess.get(src, timeout=aiohttp.ClientTimeout(total=30)) as r: if r.status != 200: print(f"[下载] 失败: HTTP {r.status}") return None with open(dl, "wb") as f: f.write(await r.read()) local_src = dl else: local_src = src current = local_src # 当前处理中的文件 orig_img = cv2.imread(local_src) # 保留原图用于颜色匹配 # 记录原图宽高比，用于检测 Gemini 旋转问题 orig_ratio = (orig_img.shape[1] / orig_img.shape[0]) if orig_img is not None else 1.0 try: # ── Step 1: Gemini 去背景 → 白背景 ────────────────── if not skip_step1: print("\n" + "─"*50) print("Step 1 / 3 | Gemini 去背景 → 白色背景") print("─"*50) s1_out = os.path.join(tempfile.gettempdir(), f"pfix_s1_{uid}.jpg") tmp.append(s1_out) ok = await _gemini_call(current, s1_out, PROMPT_WHITE_BG, aspect_ratio="auto", label="去背景") if ok: # 检查白色覆盖率，判断背景去除是否充分 s1_img = cv2.imread(s1_out) white_pct = _measure_white_coverage(s1_img) if s1_img is not None else 0.0 print(f" [去背景] 白色覆盖率: {white_pct:.1%}", end="") if white_pct < 0.20: # 背景去除太差，用强化提示词重试 print(" → 太低，强化提示词重试...") s1_retry = os.path.join(tempfile.gettempdir(), f"pfix_s1r_{uid}.jpg") tmp.append(s1_retry) ok2 = await _gemini_call(current, s1_retry, PROMPT_WHITE_BG_STRONG, aspect_ratio="auto", label="去背景(强化)") if ok2: r_img = cv2.imread(s1_retry) retry_pct = _measure_white_coverage(r_img) if r_img is not None else 0.0 print(f" [去背景] 重试白色覆盖率: {retry_pct:.1%}", end="") if retry_pct >= white_pct: print(" → 效果更好，采用重试结果") current = s1_retry else: print(" → 效果未提升，保留首次结果") current = s1_out else: print(" [去背景] 重试失败，保留首次结果") current = s1_out else: print(" → 合格") current = s1_out else: print(" Step1 失败，用原图继续") else: print("\n[跳过 Step1] 直接用原图") # ── Step 2: OpenCV 在白背景图上检测+透视矫正 ───────── print("\n" + "─"*50) print("Step 2 / 3 | OpenCV 轮廓检测 + 透视矫正") print("─"*50) img = cv2.imread(current) if img is None: print(f" 无法读取: {current}") return None h, w = img.shape[:2] print(f" 输入尺寸: {w}x{h}") pts = find_quad(img) if debug: dbg_path = os.path.join(_OUTPUT_DIR, f"debug_{uid}.jpg") save_debug_img(img, pts, dbg_path) if pts is not None: warped = four_point_transform(img, pts) # ── 方向校正：Gemini 可能把图旋转 90°，需要纠正 ── wh2, ww2 = warped.shape[:2] warped_ratio = ww2 / wh2 # 宽/高 # 若原图横竖方向与矫正结果相反（比例差异超过 1.5 倍），旋转 90° if orig_ratio > 1.0 and warped_ratio < 1.0 / 1.5: # 原图横，结果竖 → 顺时针转 90° warped = cv2.rotate(warped, cv2.ROTATE_90_CLOCKWISE) print(f" [方向校正] 原图横({orig_ratio:.2f}) vs 矫正竖({warped_ratio:.2f}) → 旋转90°") elif orig_ratio < 1.0 and warped_ratio > 1.5: # 原图竖，结果横 → 逆时针转 90° warped = cv2.rotate(warped, cv2.ROTATE_90_COUNTERCLOCKWISE) print(f" [方向校正] 原图竖({orig_ratio:.2f}) vs 矫正横({warped_ratio:.2f}) → 旋转-90°") else: print(f" [方向校正] 方向一致，无需旋转 (原图比例={orig_ratio:.2f}, 矫正比例={warped_ratio:.2f})") s2_out = os.path.join(tempfile.gettempdir(), f"pfix_s2_{uid}.jpg") tmp.append(s2_out) cv2.imwrite(s2_out, warped, [cv2.IMWRITE_JPEG_QUALITY, 95]) current = s2_out wh2, ww2 = warped.shape[:2] print(f" 透视矫正完成 → {ww2}x{wh2}") else: print(" 角点检测失败，跳过透视矫正，继续用白背景图") # ── Step 3: Qwen 高清增强 ───────────────────────────── if not skip_step3: print("\n" + "─"*50) print("Step 3 / 5 | Qwen 高清增强（RunningHub）") print("─"*50) final_out = os.path.join(_OUTPUT_DIR, f"pfix_final_{uid}.jpg") from services.service_qwen import 清晰化_api ok = await 清晰化_api(img_path=current, save_path=final_out) if ok: print(f" [高清增强] Qwen 成功") else: # Qwen 失败，用 Gemini 简化提示词兜底 print(" Qwen 失败，Gemini 兜底重试...") ok = await _gemini_call(current, final_out, PROMPT_ENHANCE_SIMPLE, aspect_ratio="auto", label="高清增强(Gemini兜底)") if not ok: print(" Step3 全部失败，直接保存矫正结果") import shutil shutil.copy2(current, final_out) else: final_out = os.path.join(_OUTPUT_DIR, f"pfix_final_{uid}.jpg") import shutil shutil.copy2(current, final_out) print("\n[跳过 Step3] 直接保存矫正结果") # ── Step 4: AI 决策 + 后处理（颜色匹配 & 白边裁切）──── print("\n" + "─"*50) print("Step 4 / 4 | AI 决策后处理（颜色匹配 / 白边裁切）") print("─"*50) final_img = cv2.imread(final_out) if final_img is not None and orig_img is not None: decision = await ai_decide_postprocess(orig_img, final_img) # Tool 1: 颜色匹配 if decision["need_color_match"]: final_img = await tool_color_match(orig_img, final_img, strength=decision["color_strength"]) cv2.imwrite(final_out, final_img, [cv2.IMWRITE_JPEG_QUALITY, 95]) else: print(" [颜色匹配] AI 判断无需调色，跳过") # Tool 2: 白边裁切 if decision["need_trim"]: trimmed, did_trim, _ = tool_trim_white_border(final_img) if did_trim: cv2.imwrite(final_out, trimmed, [cv2.IMWRITE_JPEG_QUALITY, 95]) else: print(" [裁边] AI 判断无白边，跳过") else: print(" [Step4] 图片读取失败，跳过后处理") size_kb = os.path.getsize(final_out) / 1024 print(f"\n{'='*50}") print(f" 完成！输出文件: {final_out}") print(f" 文件大小: {size_kb:.0f} KB") print(f"{'='*50}") return final_out finally: for f in tmp: if os.path.exists(f): os.remove(f) if __name__ == "__main__": if len(sys.argv) < 2: print("用法: python perspective_fix.py <图片路径或URL> [--debug] [--skip-step1] [--skip-step3]") sys.exit(1) src_arg = sys.argv[1] debug_arg = "--debug" in sys.argv skip1_arg = "--skip-step1" in sys.argv skip3_arg = "--skip-step3" in sys.argv asyncio.run(process(src_arg, debug=debug_arg, skip_step1=skip1_arg, skip_step3=skip3_arg))