Compare commits
2 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 CI Bot
|
b4e84eb7b6 | ||
|
xiaoxia
|
ed20fbad49 |
@@ -1,182 +0,0 @@
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/* DurationWheelPicker —— 弹层式滚轮选择器（样式与表单一致） */
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/* 触发按钮：外观复用 .vv-select 风格 */
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.dw-trigger {
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display: flex;
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align-items: center;
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justify-content: space-between;
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width: 100%;
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height: 36px;
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padding: 0 12px;
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background: #fff;
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border: 1px solid #e0e0e8;
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border-radius: 8px;
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font-size: 13px;
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color: #1f2937;
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cursor: pointer;
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box-sizing: border-box;
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transition: all 0.15s;
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user-select: none;
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}
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.dw-trigger:hover {
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border-color: #c0c0d0;
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}
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.dw-trigger-open,
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.dw-trigger:focus-within {
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border-color: #7c3aed !important;
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box-shadow: 0 0 0 2px rgba(124, 58, 237, 0.12);
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}
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.dw-trigger-disabled {
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opacity: 0.5;
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pointer-events: none;
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cursor: not-allowed;
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}
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.dw-trigger-val {
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flex: 1;
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overflow: hidden;
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text-overflow: ellipsis;
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white-space: nowrap;
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}
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.dw-trigger-placeholder {
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color: #9ca3af;
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}
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.dw-trigger-arrow {
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font-size: 10px;
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color: #9ca3af;
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margin-left: 8px;
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transition: transform 0.2s;
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}
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.dw-trigger-arrow-up {
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transform: rotate(180deg);
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}
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/* 弹层容器 */
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.dw-popup {
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padding: 8px;
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min-width: 140px;
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}
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/* 滚轮 */
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.dw-picker {
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position: relative;
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width: 100%;
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overflow: hidden;
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border-radius: 8px;
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background: #fafafe;
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border: 1px solid #e5e7eb;
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}
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.dw-picker-list {
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margin: 0;
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padding: 0;
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list-style: none;
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height: 100%;
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overflow-y: scroll;
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scroll-snap-type: y mandatory;
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-webkit-overflow-scrolling: touch;
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scrollbar-width: none;
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}
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.dw-picker-list::-webkit-scrollbar {
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display: none;
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}
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.dw-picker-item {
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display: flex;
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align-items: baseline;
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justify-content: center;
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gap: 3px;
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scroll-snap-align: center;
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cursor: pointer;
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font-size: 15px;
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color: #9ca3af;
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font-weight: 400;
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transition:
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color 0.15s,
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transform 0.15s,
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font-weight 0.15s;
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}
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.dw-picker-item-val {
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font-variant-numeric: tabular-nums;
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}
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.dw-picker-item-unit {
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font-size: 13px;
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color: inherit;
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}
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.dw-picker-item-active {
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color: #7c3aed;
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font-weight: 600;
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}
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.dw-picker-item-active .dw-picker-item-val {
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font-size: 18px;
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}
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.dw-picker-item-active .dw-picker-item-unit {
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font-size: 14px;
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}
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/* 中心选中条 */
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.dw-picker-mask {
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position: absolute;
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left: 6px;
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right: 6px;
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pointer-events: none;
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background: #f5f0ff;
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border-radius: 6px;
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z-index: 1;
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}
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.dw-picker-mask::before,
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.dw-picker-mask::after {
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content: "";
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position: absolute;
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left: 0;
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right: 0;
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height: 1px;
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background: #d8c4ff;
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}
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.dw-picker-mask::before {
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top: 0;
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}
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.dw-picker-mask::after {
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bottom: 0;
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}
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/* 上下渐变 */
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.dw-picker-fade {
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position: absolute;
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left: 0;
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right: 0;
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height: 40%;
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pointer-events: none;
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z-index: 2;
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}
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.dw-picker-fade-top {
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top: 0;
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background: linear-gradient(to bottom, #fafafe 25%, rgba(250, 250, 254, 0));
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}
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.dw-picker-fade-bottom {
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bottom: 0;
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background: linear-gradient(to top, #fafafe 25%, rgba(250, 250, 254, 0));
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}
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/* 弹层按钮区 */
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.dw-popup-actions {
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display: flex;
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gap: 8px;
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justify-content: flex-end;
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margin-top: 8px;
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}
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.dw-popup-actions .ant-btn {
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border-radius: 6px;
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}
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.dw-popup-actions .ant-btn-primary {
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background: #7c3aed;
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}
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.dw-popup-actions .ant-btn-primary:hover {
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background: #6d28d9 !important;
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}
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/* 覆盖 antd Popover 默认内边距 */
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.dw-popover .ant-popover-inner {
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padding: 0 !important;
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overflow: hidden;
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}
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.dw-popover .ant-popover-arrow {
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display: none;
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}
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@@ -1,180 +0,0 @@
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/**
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* DurationWheelPicker —— 竖屏滚轮式时长选择器（弹层版）
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*
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* 设计：
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* - 外观是和其他表单 Select 一致的输入框（白色底+1px灰边+紫色focus ring）
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* - 点击输入框弹出 Popover，内部是滚轮 picker（原生 scroll-snap，零依赖）
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* - 滚轮样式：白底容器，选中行 #7c3aed 紫字加粗+浅紫背景条
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* - 支持触摸/鼠标滚轮/点击；松手吸附；底部"确认/取消"按钮
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* - 默认范围 15–30 秒，步长 1 秒
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*/
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import React, { useEffect, useMemo, useRef, useState, useCallback } from "react"
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import { Popover, Button } from "antd"
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import { DownOutlined } from "@ant-design/icons"
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import "./DurationWheelPicker.css"
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export interface DurationWheelPickerProps {
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value?: number
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min?: number
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max?: number
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step?: number
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unit?: string
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onChange?: (value: number) => void
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placeholder?: string
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disabled?: boolean
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/** 弹层宽度，默认 160px */
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popupWidth?: number
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/** 弹层内滚轮高度，默认 180px */
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wheelHeight?: number
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}
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const ITEM_HEIGHT = 36
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const DurationWheelPicker: React.FC<DurationWheelPickerProps> = ({
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value = 20,
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min = 15,
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max = 30,
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step = 1,
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unit = "秒",
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onChange,
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placeholder = "请选择时长",
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disabled = false,
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popupWidth = 160,
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wheelHeight = 180,
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}) => {
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const options = useMemo(() => {
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const arr: number[] = []
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for (let v = min; v <= max; v += step) arr.push(v)
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return arr
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}, [min, max, step])
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const [open, setOpen] = useState(false)
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// 弹层内暂存值，点确认才提交
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const [draft, setDraft] = useState<number>(value)
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const listRef = useRef<HTMLUListElement>(null)
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const scrollTimerRef = useRef<ReturnType<typeof setTimeout> | null>(null)
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useEffect(() => {
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if (open) {
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setDraft(value)
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// 下一帧滚到当前值
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requestAnimationFrame(() => scrollToValue(value, false))
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}
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// eslint-disable-next-line react-hooks/exhaustive-deps
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}, [open])
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const scrollToValue = useCallback(
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(v: number, smooth = true) => {
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const list = listRef.current
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if (!list) return
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const idx = options.indexOf(v)
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if (idx < 0) return
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list.scrollTo({ top: idx * ITEM_HEIGHT, behavior: smooth ? "smooth" : "auto" })
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},
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[options],
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)
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const handleScroll = () => {
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if (scrollTimerRef.current) clearTimeout(scrollTimerRef.current)
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scrollTimerRef.current = setTimeout(() => {
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const list = listRef.current
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if (!list) return
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const idx = Math.round(list.scrollTop / ITEM_HEIGHT)
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const clamped = Math.max(0, Math.min(options.length - 1, idx))
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const targetTop = clamped * ITEM_HEIGHT
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if (Math.abs(list.scrollTop - targetTop) > 1) {
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list.scrollTo({ top: targetTop, behavior: "smooth" })
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}
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setDraft(options[clamped])
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}, 100)
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}
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const handleConfirm = () => {
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onChange?.(draft)
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setOpen(false)
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}
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const handleCancel = () => {
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setOpen(false)
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}
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const handleItemClick = (v: number) => {
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setDraft(v)
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scrollToValue(v, true)
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}
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const maskTop = wheelHeight / 2 - ITEM_HEIGHT / 2
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const wheel = (
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<div className="dw-popup">
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<div
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className="dw-picker"
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style={{ height: wheelHeight, width: popupWidth - 24 /* padding */ }}
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>
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<div className="dw-picker-mask" style={{ top: maskTop, height: ITEM_HEIGHT }} aria-hidden />
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<div className="dw-picker-fade dw-picker-fade-top" aria-hidden />
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<div className="dw-picker-fade dw-picker-fade-bottom" aria-hidden />
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<ul
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ref={listRef}
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className="dw-picker-list"
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onScroll={handleScroll}
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style={{
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paddingTop: wheelHeight / 2 - ITEM_HEIGHT / 2,
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paddingBottom: wheelHeight / 2 - ITEM_HEIGHT / 2,
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}}
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>
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{options.map((v) => {
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const isActive = v === draft
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return (
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<li
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key={v}
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className={`dw-picker-item${isActive ? " dw-picker-item-active" : ""}`}
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style={{ height: ITEM_HEIGHT, lineHeight: `${ITEM_HEIGHT}px` }}
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onClick={() => handleItemClick(v)}
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aria-selected={isActive}
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role="option"
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>
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<span className="dw-picker-item-val">{v}</span>
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<span className="dw-picker-item-unit">{unit}</span>
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</li>
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)
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})}
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</ul>
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</div>
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<div className="dw-popup-actions">
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<Button size="small" onClick={handleCancel}>
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取消
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</Button>
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<Button size="small" type="primary" onClick={handleConfirm}>
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确认
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</Button>
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</div>
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</div>
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)
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return (
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<Popover
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open={!disabled && open}
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onOpenChange={(v) => setOpen(v)}
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content={wheel}
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trigger="click"
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placement="bottomLeft"
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overlayClassName="dw-popover"
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overlayStyle={{ padding: 0 }}
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overlayInnerStyle={{ padding: 0, borderRadius: 10 }}
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destroyTooltipOnHide
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>
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<div
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className={`dw-trigger${disabled ? " dw-trigger-disabled" : ""}${open ? " dw-trigger-open" : ""}`}
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style={{ height: 36 }}
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>
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<span className={`dw-trigger-val${value != null ? "" : " dw-trigger-placeholder"}`}>
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{value != null ? `${value}${unit}` : placeholder}
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</span>
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<DownOutlined className={`dw-trigger-arrow${open ? " dw-trigger-arrow-up" : ""}`} />
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</div>
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</Popover>
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)
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}
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export default DurationWheelPicker
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@@ -218,12 +218,12 @@ const PURPOSES = [
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"悬念短剧",
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"情绪短片",
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]
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const DURATIONS = [15, 20, 30, 45, 60]
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const RATIOS = [
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{ v: "9:16", label: "9:16 竖屏（抖音/视频号）" },
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{ v: "16:9", label: "16:9 横屏（B站/YouTube）" },
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{ v: "1:1", label: "1:1 方形（小红书）" },
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]
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const DURATIONS = Array.from({ length: 16 }, (_, i) => 15 + i)
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/** 兜底模型列表（接口未返回时使用，字段与 ViralVideoModel 对齐；后端返回后自动覆盖） */
|
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const FALLBACK_VIDEO_MODELS: ViralVideoModel[] = [
|
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{
|
||||
@@ -437,7 +437,7 @@ const emptyTask = (id: string, title: string): TabTask => ({
|
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language: "中文(普通话)",
|
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viralStructure: STRUCTURES[0],
|
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marketingPurpose: "",
|
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duration: 20,
|
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duration: 15,
|
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persona: "",
|
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videoRatio: "9:16",
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videoModel: "seedance-2.5",
|
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@@ -2431,7 +2431,7 @@ const ViralVideoPage: React.FC = () => {
|
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options={PURPOSES.map((i) => ({ value: i, label: i }))}
|
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/>
|
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</div>
|
||||
<div className="vv-form-row">
|
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<div className="vv-form-row" style={{ gridColumn: "1 / -1" }}>
|
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<label className="vv-label">文案视频时长</label>
|
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<Select
|
||||
className="vv-select"
|
||||
|
||||
@@ -1,9 +1,7 @@
|
||||
"""爆款视频 Celery 编排器 — ViralVideoOrchestrator.
|
||||
"""爆款视频 Celery 编排器 — ViralVideoOrchestrator (v1.6 单次 Seedance 出片版).
|
||||
|
||||
V2 图片分析（10-05）：火山OCR专用API + doubao-lite强约束JSON并行，单图<3s，8图<15s；pro VLM单次兜底。输出字段兼容旧格式，下游信任链/t2i零改动。
|
||||
|
||||
流水线步骤：
|
||||
1. _step_image_analysis 图片分析（V2: OCR+qwen3.8-flash并行 + qwen3.7-plus兜底）
|
||||
v1.6 重大简化（Seedance 2.5 单次最长 30 秒，直接出片）：
|
||||
1. _step_image_analysis 图片 VLM 分析（保留）
|
||||
1.5 _step_video_analysis 参考视频风格分析（可选）
|
||||
2. _step_intent_parsing 用户文案意图解析
|
||||
3. _step_script_generation 编导分镜脚本生成（融合原 copy_fusion+storyboard+review，输出 copy_result 结构 + voiceover_script）
|
||||
@@ -24,9 +22,11 @@ from __future__ import annotations
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import re
|
||||
import tempfile
|
||||
import threading
|
||||
import time
|
||||
from concurrent.futures import ThreadPoolExecutor, as_completed
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
@@ -330,62 +330,633 @@ def _vision_fallback(idx: int, reason: str, extra: dict | None = None) -> dict:
|
||||
return d
|
||||
|
||||
|
||||
def _normalize_image_url(raw: str, idx: int) -> str:
|
||||
"""将 job.images 中的 storage_key/相对路径/空值统一归一化为可公网访问 URL。
|
||||
- http(s):// → 直接用
|
||||
- 其他 → storage_key，通过 SharedStorageService.get_url() 转公网 URL
|
||||
- 空值/非字符串 → 抛 ValueError
|
||||
def _is_vision_result_usable(result: dict) -> bool:
|
||||
"""判断 VLM 返回是否有效。
|
||||
#2198b: 判定条件放宽——有人像(portrait_prompt非空/非'无人像')即视为usable（爆款视频核心
|
||||
是要人物描述给信任链t2i用，product name/brand/features识别不准是次要的）。
|
||||
非人像场景才要求name+summary+features有效。
|
||||
"""
|
||||
if not isinstance(result, dict):
|
||||
return False
|
||||
# 有人像描述（爆款视频最核心需求，portrait_prompt给信任链t2i做参考）就视为usable
|
||||
pp = (result.get("portrait_prompt") or "").strip()
|
||||
if pp and pp not in ("无人像", "无法判断", "未识别"):
|
||||
return True
|
||||
# 非人像场景：要求name+summary有效
|
||||
name = (result.get("name") or "").strip()
|
||||
if not name or name in ("未识别", "无法判断", "未知"):
|
||||
return False
|
||||
summary = (result.get("summary") or "").strip()
|
||||
if len(summary) < 5 or summary in ("无法判断", "未识别"):
|
||||
return False
|
||||
category = (result.get("category") or "").strip()
|
||||
if category == "非产品图":
|
||||
return True
|
||||
feats = result.get("key_features") or []
|
||||
if not isinstance(feats, list) or len(feats) == 0 or feats == ["无法判断"]:
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def _normalize_image_url(raw: str, idx: int) -> str:
|
||||
"""#2188: 将 job.images 中的 storage_key/相对路径/空值统一归一化为可公网访问 URL。
|
||||
- 以 http:// 或 https:// 开头 → 视为公网 URL
|
||||
- 其他 → 视为 storage_key，用 SharedStorageService.get_url() 转公网 URL
|
||||
- 空值/None/非字符串 → 抛 ValueError（上层 catch 后走 400 错误）
|
||||
返回前做 HTTP 可达性检查（GET+Range:0-1024 避免 OSS 签名 URL 对 HEAD 返回 403 的假阴性）。
|
||||
"""
|
||||
import requests as _req
|
||||
|
||||
if not raw or not isinstance(raw, str):
|
||||
raise ValueError(f"图片 #{idx} URL 为空或类型错误: {type(raw).__name__}={raw!r}")
|
||||
url = raw.strip()
|
||||
if not url:
|
||||
raise ValueError(f"图片 #{idx} URL 为空白字符串")
|
||||
if url.startswith("http://") or url.startswith("https://"):
|
||||
return url
|
||||
storage_key = url.lstrip("/")
|
||||
try:
|
||||
from packages.shared.storage import get_storage_service
|
||||
# storage_key 判定：不以 http 开头
|
||||
if not url.startswith("http://") and not url.startswith("https://"):
|
||||
# 去掉可能的前导斜杠
|
||||
storage_key = url.lstrip("/")
|
||||
try:
|
||||
from packages.shared.storage import get_storage_service
|
||||
|
||||
url = get_storage_service().get_url(storage_key)
|
||||
_svc = get_storage_service()
|
||||
url = _svc.get_url(storage_key)
|
||||
except Exception as _e:
|
||||
raise ValueError(f"图片 #{idx} storage_key={storage_key!r} 转公网URL失败: {_e}") from _e
|
||||
logger.info("[爆款视频] 图片 #%d storage_key 已转公网 URL: %s", idx, url[:120])
|
||||
# #2194: 用 GET+Range 代替 HEAD。
|
||||
# Aliyun OSS 签名 URL 把 HTTP Method 纳入签名，前端/OSS SDK 生成的签名是 GET-only，
|
||||
# 用 HEAD 请求会返回 403 SignatureDoesNotMatch 误判 URL 无效，实际 GET 下载完全正常。
|
||||
# Range: bytes=0-1024 只取前1KB，开销极小。
|
||||
try:
|
||||
_r = _req.get(url, timeout=5, allow_redirects=True, stream=True, headers={"Range": "bytes=0-1024"})
|
||||
if _r.status_code >= 400:
|
||||
logger.warning("[爆款视频] 图片 #%d URL 可达性检查返回 %d: %s", idx, _r.status_code, url[:120])
|
||||
_r.close()
|
||||
except Exception as _e:
|
||||
raise ValueError(f"图片 #{idx} storage_key={storage_key!r} 转公网URL失败: {_e}") from _e
|
||||
logger.info("[爆款视频] 图片 #%d storage_key → 公网URL: %s", idx, url[:120])
|
||||
logger.warning("[爆款视频] 图片 #%d URL 可达性检查异常: %s url=%s", idx, _e, url[:120])
|
||||
return url
|
||||
|
||||
|
||||
def _step_image_analysis(job: ViralVideoJob) -> dict:
|
||||
"""步骤 1: 图片分析（V2 主路径）。
|
||||
def _analyze_single_image(
|
||||
idx: int,
|
||||
img_url: str,
|
||||
vision_model: str,
|
||||
timeout: int,
|
||||
*,
|
||||
pro_fallback_model: str | None = None,
|
||||
) -> dict:
|
||||
"""单张图片 VLM 分析（#2040：改为从 prompt_loader 读模板 + XML 解析）。
|
||||
|
||||
架构：
|
||||
- 主力：火山 MediaKit OCR（专用API，未配置时自动跳过）+ qwen3.8-flash 强约束 JSON，每图2路并行，目标<3s；
|
||||
- 外层全并发（workers=8），目标8图<15s；
|
||||
- 兜底：fast 结果不可用时单次调用 qwen3.7-plus（简单、无竞速）。
|
||||
- 唯一后端：阿里云百炼 DashScope，API Key 从环境变量 DASHSCOPE_API_KEY 读取。
|
||||
输出 dict 字段（name/brand/category/appearance/key_features/scene/mood/portrait_prompt/summary/_source）
|
||||
与旧版格式完全一致，下游信任链/t2i/intent_parsing/script_generation 零改动。
|
||||
lite 失败/不可用时用 pro 降级重试 1 次。失败/None 最终返回含默认字段的 dict。
|
||||
"""
|
||||
try:
|
||||
from packages.application.viral_video import xml_parser as xp
|
||||
from packages.application.viral_video.prompt_loader import (
|
||||
get_template,
|
||||
render_system_prompt,
|
||||
render_user_prompt,
|
||||
)
|
||||
from packages.shared.ai_service import call_vision
|
||||
except ImportError as e:
|
||||
logger.warning("[爆款视频] prompt 模板/解析模块不可用: %s", e)
|
||||
return _vision_fallback(idx, f"fallback_import_error:{e}")
|
||||
|
||||
if not img_url or not isinstance(img_url, str):
|
||||
return _vision_fallback(idx, "invalid_url")
|
||||
|
||||
template = get_template("image_analysis")
|
||||
system = render_system_prompt(template)
|
||||
user = render_user_prompt(
|
||||
template,
|
||||
image_count=1,
|
||||
industry="通用",
|
||||
image_urls=f"第1张：{img_url}",
|
||||
)
|
||||
|
||||
def _call(model: str, tmo: int, label: str):
|
||||
# #2194/#2198: max_retries=0 由外层 _step_image_analysis 统一设置（阶段前置0、阶段后恢复），
|
||||
# 子线程只读不改，避免嵌套并行竞速时多线程同时改 client.max_retries 产生竞态
|
||||
_t0 = time.time()
|
||||
try:
|
||||
import json as _json
|
||||
|
||||
from packages.shared.ai_client import get_doubao_client as _gdc
|
||||
|
||||
_client = _gdc()
|
||||
_messages = [
|
||||
{"role": "system", "content": system},
|
||||
{"role": "user", "content": user},
|
||||
]
|
||||
raw = _client.vision_completion(
|
||||
messages=_messages,
|
||||
images=[img_url],
|
||||
temperature=0.3,
|
||||
max_tokens=1200,
|
||||
timeout=tmo,
|
||||
model=model,
|
||||
)
|
||||
_elapsed = time.time() - _t0
|
||||
logger.info(
|
||||
"[爆款视频] 图片 #%d VLM(%s/%s) 完成 elapsed=%.1fs timeout=%d",
|
||||
idx,
|
||||
label,
|
||||
model,
|
||||
_elapsed,
|
||||
tmo,
|
||||
)
|
||||
if raw is None:
|
||||
return None
|
||||
stripped = raw.strip()
|
||||
if stripped.startswith("```"):
|
||||
stripped = stripped.strip("`")
|
||||
if stripped.startswith("json"):
|
||||
stripped = stripped[4:].lstrip()
|
||||
try:
|
||||
return _json.loads(stripped)
|
||||
except (_json.JSONDecodeError, TypeError):
|
||||
return stripped
|
||||
except Exception as e:
|
||||
_elapsed = time.time() - _t0
|
||||
logger.warning(
|
||||
"[爆款视频] 图片 #%d call_vision(%s/%s) 异常 elapsed=%.1fs err=%s",
|
||||
idx,
|
||||
label,
|
||||
model,
|
||||
_elapsed,
|
||||
e,
|
||||
)
|
||||
return None
|
||||
|
||||
def _xml_to_product(nodes: list, raw_text: str) -> dict:
|
||||
product_nodes = [n for n in nodes if n["tag"] == "product"]
|
||||
scene = xp.text_of(raw_text, "scene") or "通用"
|
||||
mood = xp.text_of(raw_text, "mood") or ""
|
||||
# #2184: #2177 XML 重构后人物信息放在顶层 <people has_person count gender age_range pose expression/>，
|
||||
# 不再是 <product> 的 portrait_prompt 属性。需从顶层 people 标签提取并拼装 portrait_prompt。
|
||||
portrait_prompt = "无人像"
|
||||
try:
|
||||
people_node = xp.find_first(raw_text, "people")
|
||||
if people_node:
|
||||
_pa = people_node.get("attrs") or {}
|
||||
_has_person = xp.attr_bool(_pa.get("has_person"), False)
|
||||
if _has_person:
|
||||
_gender = _pa.get("gender", "无法判断") or "无法判断"
|
||||
_age = _pa.get("age_range", "无法判断") or "无法判断"
|
||||
_hair = _pa.get("hair", "无法判断") or "无法判断"
|
||||
_skin = _pa.get("skin_tone", "无法判断") or "无法判断"
|
||||
_face = _pa.get("face_shape", "无法判断") or "无法判断"
|
||||
_outfit = _pa.get("outfit", "无法判断") or "无法判断"
|
||||
_pose = _pa.get("pose", "无法判断") or "无法判断"
|
||||
_expr = _pa.get("expression", "无法判断") or "无法判断"
|
||||
_count = xp.attr_int(_pa.get("count"), 1)
|
||||
# #2185: VLM有时对外貌属性输出"无法判断"，用通用兜底值确保portrait_prompt始终有完整外貌描述
|
||||
if _hair == "无法判断":
|
||||
_hair = "自然发型"
|
||||
if _skin == "无法判断":
|
||||
_skin = "自然"
|
||||
if _face == "无法判断":
|
||||
_face = "标准"
|
||||
if _outfit == "无法判断":
|
||||
_outfit = "日常服装"
|
||||
_parts = []
|
||||
if _gender != "无法判断":
|
||||
_g = _gender + ("性" if not _gender.endswith("性") else "")
|
||||
_parts.append(_g)
|
||||
else:
|
||||
_parts.append("成年人")
|
||||
if _age != "无法判断":
|
||||
_parts.append(_age)
|
||||
_parts.append("人物")
|
||||
_parts.append(_hair)
|
||||
_parts.append(f"{_skin}肤色")
|
||||
_parts.append(f"{_face}脸型")
|
||||
_parts.append(f"身着{_outfit}")
|
||||
if _pose != "无法判断":
|
||||
_parts.append(f"姿态{_pose}")
|
||||
if _expr != "无法判断":
|
||||
_parts.append(f"表情{_expr}")
|
||||
else:
|
||||
_parts.append("表情自然")
|
||||
# #2186: 智能回填——VLM有时省略hair/outfit等外貌属性，但product.name/features/colors里已有相关信息
|
||||
# 从product名字和features中提取服装关键词回填outfit
|
||||
if _outfit in ("日常服装", "无法判断"):
|
||||
for _ppn in product_nodes:
|
||||
_pn = (_ppn.get("attrs") or {}).get("name", "") or ""
|
||||
_pf = (_ppn.get("attrs") or {}).get("features", "") or ""
|
||||
_ptxt = _pn + " " + _pf
|
||||
# 服装关键词识别（常见上装/下装/裙装/套装）
|
||||
_cloth_kws = [
|
||||
# 衬衫/T恤类
|
||||
"衬衫",
|
||||
"T恤",
|
||||
"POLO衫",
|
||||
"polo衫",
|
||||
"Polo衫",
|
||||
"打底衫",
|
||||
"雪纺衫",
|
||||
"罩衫",
|
||||
"针织衫",
|
||||
# 毛衣/卫衣/针织类
|
||||
"毛衣",
|
||||
"卫衣",
|
||||
"帽衫",
|
||||
"针织",
|
||||
"毛衫",
|
||||
"开衫",
|
||||
# 外套/西装/夹克/风衣类
|
||||
"外套",
|
||||
"西装",
|
||||
"西服",
|
||||
"夹克",
|
||||
"皮衣",
|
||||
"皮夹克",
|
||||
"风衣",
|
||||
"大衣",
|
||||
"羽绒服",
|
||||
"棉服",
|
||||
"棉服",
|
||||
"马甲",
|
||||
"背心",
|
||||
"开衫外套",
|
||||
# 裙装
|
||||
"连衣裙",
|
||||
"半身裙",
|
||||
"短裙",
|
||||
"长裙",
|
||||
"百褶裙",
|
||||
"A字裙",
|
||||
"旗袍",
|
||||
"汉服",
|
||||
"JK裙",
|
||||
# 裤装
|
||||
"牛仔裤",
|
||||
"休闲裤",
|
||||
"西裤",
|
||||
"运动裤",
|
||||
"短裤",
|
||||
"阔腿裤",
|
||||
"打底裤",
|
||||
# 制服/套装
|
||||
"制服",
|
||||
"套装",
|
||||
"职业装",
|
||||
"工装",
|
||||
# 通用上装/下装词（兜底）
|
||||
"上衣",
|
||||
"短袖",
|
||||
"长袖",
|
||||
"无袖",
|
||||
"半袖",
|
||||
"吊带",
|
||||
"背心",
|
||||
"网纱",
|
||||
"雪纺",
|
||||
"真丝",
|
||||
"纯棉",
|
||||
"亚麻",
|
||||
]
|
||||
for _ckw in _cloth_kws:
|
||||
if _ckw in _ptxt:
|
||||
_ci = _ptxt.find(_ckw)
|
||||
# 向前找颜色/材质/款式形容词（白/黑/米/红/蓝/灰/棉/麻/长/短/厚/薄/长袖/短袖/翻领/圆领/V领/印花/条纹等）
|
||||
_start = max(0, _ci - 12)
|
||||
# 向后包含款式词（长袖/短袖/外套/套装/上衣等后续修饰）
|
||||
_end = min(len(_ptxt), _ci + len(_ckw) + 8)
|
||||
_outfit_extract = _ptxt[_start:_end].strip(" ,，。.、")
|
||||
# 仅清理明确的品牌/产品类前缀（不清理颜色/款式/尺寸形容词）
|
||||
_outfit_extract = re.sub(
|
||||
r"^(\S{0,4}牌|\S{0,3}品牌|\S{0,3}款|产品|商品|的)", "", _outfit_extract
|
||||
).strip()
|
||||
# 尾部清理：去掉残留的品牌字/型号字（如"标""ml""g""装"等单字杂字）
|
||||
_outfit_extract = re.sub(
|
||||
r"(标[0-9a-zA-Z]*|\d+\s*(?:ml|g|L|斤|件|个|瓶|盒|包|袋|装)|\s+\d+\s*)$",
|
||||
"",
|
||||
_outfit_extract,
|
||||
flags=re.IGNORECASE,
|
||||
).strip()
|
||||
if len(_outfit_extract) >= 2:
|
||||
_outfit = _outfit_extract
|
||||
break
|
||||
if _outfit not in ("日常服装", "无法判断"):
|
||||
break
|
||||
# 从color标签中提取头发颜色回填hair
|
||||
if _hair in ("自然发型", "无法判断"):
|
||||
_hair_color = ""
|
||||
_color_nodes = [n for n in nodes if n["tag"] == "color"]
|
||||
_hair_kws_map = {
|
||||
"黑": "黑色",
|
||||
"棕": "棕色",
|
||||
"金": "金色",
|
||||
"栗": "栗色",
|
||||
"红": "红色",
|
||||
"白": "白色",
|
||||
"灰": "灰色",
|
||||
"蓝": "蓝色",
|
||||
"黄": "黄色",
|
||||
"紫": "紫色",
|
||||
}
|
||||
for _cn in _color_nodes:
|
||||
_cname = (_cn.get("attrs") or {}).get("name", "") or ""
|
||||
# 小占比颜色更可能是发色（非主色的小面积色），且名称含头发/黑/棕/金等
|
||||
_ccov = 0.0
|
||||
try:
|
||||
_ccov = float((_cn.get("attrs") or {}).get("coverage", "0") or 0)
|
||||
except Exception:
|
||||
pass
|
||||
for _hk, _hv in _hair_kws_map.items():
|
||||
if _hk in _cname and _ccov < 0.3:
|
||||
_hair_color = _hv
|
||||
break
|
||||
if _hair_color:
|
||||
break
|
||||
if _hair_color:
|
||||
_hair = f"{_hair_color}头发"
|
||||
else:
|
||||
_hair = "自然发型"
|
||||
# 重新拼装_parts（回填后）
|
||||
_parts = []
|
||||
if _gender != "无法判断":
|
||||
_g = _gender + ("性" if not _gender.endswith("性") else "")
|
||||
_parts.append(_g)
|
||||
else:
|
||||
_parts.append("成年人")
|
||||
if _age != "无法判断":
|
||||
_parts.append(_age)
|
||||
_parts.append("人物")
|
||||
_parts.append(_hair)
|
||||
_parts.append(f"{_skin}肤色")
|
||||
_parts.append(f"{_face}脸型")
|
||||
_parts.append(f"身着{_outfit}")
|
||||
if _pose != "无法判断":
|
||||
_parts.append(f"姿态{_pose}")
|
||||
if _expr != "无法判断":
|
||||
_parts.append(f"表情{_expr}")
|
||||
else:
|
||||
_parts.append("表情自然")
|
||||
portrait_prompt = "，".join(_parts)
|
||||
logger.info(
|
||||
"[爆款视频] 图片 #%d 解析<people>(回填后): count=%d gender=%s age=%s hair=%s skin=%s face=%s outfit=%s pose=%s expr=%s → %s",
|
||||
idx,
|
||||
_count,
|
||||
_gender,
|
||||
_age,
|
||||
_hair,
|
||||
_skin,
|
||||
_face,
|
||||
_outfit,
|
||||
_pose,
|
||||
_expr,
|
||||
portrait_prompt,
|
||||
)
|
||||
except Exception as _pe:
|
||||
logger.warning("[爆款视频] 图片 #%d 解析<people>标签异常: %s，回退无人像", idx, _pe)
|
||||
for p in product_nodes:
|
||||
a = p["attrs"]
|
||||
text_on_pkg = a.get("text_on_package", "")
|
||||
p_body = p.get("text", "") or ""
|
||||
if not text_on_pkg and p_body:
|
||||
text_on_pkg = xp.text_of(p_body, "text_on_package") or ""
|
||||
text_list = [x.strip() for x in re.split(r"[,，;；]", text_on_pkg) if x.strip()] if text_on_pkg else []
|
||||
features = a.get("features", "")
|
||||
feat_list = [x.strip() for x in re.split(r"[,，;；]", features) if x.strip()] if features else []
|
||||
name = a.get("name", "") or "未识别"
|
||||
brand = a.get("brand", "") or "无法判断"
|
||||
category = a.get("category", "") or "无法判断"
|
||||
appearance = a.get("appearance", "") or "无法判断"
|
||||
packaging = a.get("packaging", "") or "无法判断"
|
||||
summary = a.get("summary", "") or f"{brand} {name}"
|
||||
# 优先取 product 属性上的 portrait_prompt（兼容旧schema），否则用顶层 <people> 解析结果
|
||||
_pp_from_attr = a.get("portrait_prompt", "")
|
||||
if _pp_from_attr and _pp_from_attr != "无人像":
|
||||
portrait_prompt = _pp_from_attr
|
||||
return {
|
||||
"name": name,
|
||||
"brand": brand,
|
||||
"category": category,
|
||||
"appearance": appearance,
|
||||
"packaging": packaging,
|
||||
"text_on_package": text_list,
|
||||
"key_features": feat_list or [features] if features else ["无法判断"],
|
||||
"scene": scene,
|
||||
"mood": mood,
|
||||
"portrait_prompt": portrait_prompt,
|
||||
"summary": summary,
|
||||
"_source": "xml",
|
||||
}
|
||||
# 没有 product 标签但有 <people has_person="true"> 也要能取到人物描述（兜底）
|
||||
if portrait_prompt != "无人像":
|
||||
return {
|
||||
"name": "未识别",
|
||||
"brand": "无法判断",
|
||||
"category": "无法判断",
|
||||
"appearance": "无法判断",
|
||||
"packaging": "无法判断",
|
||||
"text_on_package": [],
|
||||
"key_features": ["无法判断"],
|
||||
"scene": scene,
|
||||
"mood": mood,
|
||||
"portrait_prompt": portrait_prompt,
|
||||
"summary": "未识别",
|
||||
"_source": "xml_no_product",
|
||||
}
|
||||
return _vision_fallback(idx, "no_product_tag")
|
||||
|
||||
def _normalize(raw, source: str) -> dict:
|
||||
if raw is None:
|
||||
return _vision_fallback(idx, f"{source}_none")
|
||||
# VLM 偶尔直接返回 JSON 对象（不包裹```json），_call 里 json.loads 后已是 dict
|
||||
if isinstance(raw, dict):
|
||||
_prod = {
|
||||
"name": raw.get("name") or "未识别",
|
||||
"brand": raw.get("brand") or "无法判断",
|
||||
"category": raw.get("category") or "无法判断",
|
||||
"appearance": raw.get("appearance") or "无法判断",
|
||||
"packaging": raw.get("packaging") or "无法判断",
|
||||
"text_on_package": raw.get("text_on_package") or [],
|
||||
"key_features": raw.get("key_features") or raw.get("features") or ["无法判断"],
|
||||
"scene": raw.get("scene") or "通用",
|
||||
"mood": raw.get("mood") or "",
|
||||
"portrait_prompt": raw.get("portrait_prompt") or "无人像",
|
||||
"summary": raw.get("summary") or f"{raw.get('brand','')} {raw.get('name','')}",
|
||||
"_source": source,
|
||||
}
|
||||
return _prod
|
||||
if not isinstance(raw, str):
|
||||
return _vision_fallback(idx, f"{source}_badtype")
|
||||
nodes = xp.parse_tags(raw)
|
||||
if not nodes:
|
||||
# 不是 XML 也不是 dict：尝试当作纯 JSON 字符串再解析一次
|
||||
try:
|
||||
import json as _j2
|
||||
|
||||
_jd = _j2.loads(raw)
|
||||
if isinstance(_jd, dict):
|
||||
return _normalize(_jd, source)
|
||||
except Exception:
|
||||
pass
|
||||
logger.warning("[爆款视频] 图片 #%d XML/JSON 解析都失败 source=%s raw_head=%s", idx, source, raw[:200])
|
||||
return _vision_fallback(idx, f"{source}_parse_fail", {"_raw": raw[:500]})
|
||||
product = _xml_to_product(nodes, raw)
|
||||
product.setdefault("_source", source)
|
||||
product["raw"] = raw[:500]
|
||||
return product
|
||||
|
||||
# #2198: lite/pro 并行竞速。同时发两个请求，先返回 usable 结果就用哪个，避免
|
||||
# 串行 lite超时→再发pro 累计80-100s的惩罚。外层 max_workers=2 图片并发时，竞速模式下
|
||||
# VLM 总并发=4（2图 × 2模型），实测 Ark 可以承受，且因为取快者而不是等两个都完，
|
||||
# 单图通常 40-50s 就能拿到 pro 结果（pro 正常 42-46s），lite 偶发 30s 内返回时更快。
|
||||
race_t0 = time.time()
|
||||
lite_tag = vision_model.split("/")[-1] if "/" in vision_model else vision_model
|
||||
winner: dict | None = None
|
||||
with ThreadPoolExecutor(max_workers=2) as _inner_pool:
|
||||
f_lite = _inner_pool.submit(_call, vision_model, timeout, "lite")
|
||||
# pro 给 75s（原60s太紧实测1/3超时，pro正常42-65s给10s余量）
|
||||
pro_tmo = 75
|
||||
f_pro = _inner_pool.submit(_call, pro_fallback_model or vision_model, pro_tmo, "pro")
|
||||
_fmap = {f_lite: ("lite", lite_tag), f_pro: ("pro", "pro_fallback")}
|
||||
for _fut in as_completed(_fmap, timeout=pro_tmo + 15):
|
||||
_lbl, _tag = _fmap[_fut]
|
||||
try:
|
||||
_raw = _fut.result()
|
||||
except Exception as _e:
|
||||
logger.warning("[爆款视频] 图片 #%d %s future异常: %s", idx, _lbl, _e)
|
||||
_raw = None
|
||||
_res = _normalize(_raw, _tag)
|
||||
if _is_vision_result_usable(_res):
|
||||
winner = _res
|
||||
if _lbl == "pro":
|
||||
winner["_fallback_used"] = True
|
||||
logger.info(
|
||||
"[爆款视频] 图片 #%d 竞速胜出=%s elapsed=%.1fs",
|
||||
idx,
|
||||
_lbl,
|
||||
time.time() - race_t0,
|
||||
)
|
||||
break
|
||||
if winner is not None:
|
||||
return winner
|
||||
# 两个都失败，返回最后一次 _normalize 结果（通常是 pro 的失败 fallback，含 _source=pro_fallback_none）
|
||||
try:
|
||||
_last_raw = f_pro.result(timeout=1)
|
||||
except Exception:
|
||||
_last_raw = None
|
||||
_last = _normalize(_last_raw, "pro_fallback")
|
||||
logger.warning(
|
||||
"[爆款视频] 图片 #%d lite/pro 竞速均失败 elapsed=%.1fs",
|
||||
idx,
|
||||
time.time() - race_t0,
|
||||
)
|
||||
return _last
|
||||
|
||||
|
||||
def _step_image_analysis(job: ViralVideoJob) -> dict:
|
||||
"""步骤 1: 图片分析。
|
||||
|
||||
V2（VISION_V2_ENABLED=true，10-05 新方案）:
|
||||
- 每图并行 2 路：火山 MediaKit OCR（专用API）+ doubao-seed-2.1-lite 强约束 JSON
|
||||
（火山云端无人体属性/商品检测/图像标签公开 HTTP API，用 lite JSON-only VLM 弥补），
|
||||
目标单图 <3s；
|
||||
- 外层 8 图全并发，目标 8 图 <15s；
|
||||
- 置信度低/全失败时降级 doubao-seed-2.1-pro 完整 VLM 兜底（复用旧竞速逻辑）；
|
||||
- 输出 dict 格式与旧 _normalize() 完全一致，下游信任链/t2i 零改动。
|
||||
|
||||
V1（默认，#2198/#2199 lite/pro 并行竞速）:
|
||||
- 过渡版兜底，单图 lite(30s)/pro(75s) 竞速，外层 max_workers=2，典型 40-75s/图。
|
||||
"""
|
||||
try:
|
||||
from packages.shared.ai_service import call_vision # noqa: F401
|
||||
except ImportError:
|
||||
logger.warning("[爆款视频] ai_service.call_vision 不可用，使用占位结果")
|
||||
return {"products": [_vision_fallback(0, "fallback_import_error")]}
|
||||
|
||||
if not job.images:
|
||||
logger.warning("[爆款视频] 任务无 images，跳过图片分析")
|
||||
return {"products": []}
|
||||
|
||||
# URL 归一化（storage_key→公网URL；空值直接400）
|
||||
# URL 归一化 — storage_key→公网URL + 空值报400
|
||||
normalized_urls: list[str] = []
|
||||
for idx, raw in enumerate(job.images):
|
||||
normalized_urls.append(_normalize_image_url(raw, idx))
|
||||
|
||||
try:
|
||||
from worker_app.tasks.vision import analyze_images_v2 as _aiv2
|
||||
except ImportError:
|
||||
try:
|
||||
from tasks.vision import analyze_images_v2 as _aiv2 # type: ignore
|
||||
except ImportError as e:
|
||||
logger.error("[爆款视频] vision 模块导入失败: %s", e)
|
||||
return {"products": [_vision_fallback(0, f"vision_import_error:{e}")]}
|
||||
normalized_urls.append(_normalize_image_url(raw, idx))
|
||||
except ValueError as _ve:
|
||||
logger.error("[爆款视频] 图片 #%d URL 归一化失败: %s", idx, _ve)
|
||||
raise
|
||||
|
||||
# V2 内部 httpx 直连 dashscope，单次调用无重试，无需调整全局 client
|
||||
results = _aiv2(normalized_urls)
|
||||
return {"products": list(results)}
|
||||
# 模型配置（V1/V2 共用）
|
||||
try:
|
||||
_s = get_shared_settings()
|
||||
lite_model = _s.doubao_vision_lite_model
|
||||
pro_model = _s.doubao_vision_model
|
||||
except Exception:
|
||||
lite_model = "doubao-seed-2-1-lite-260915"
|
||||
pro_model = "doubao-seed-2-1-pro-260915"
|
||||
|
||||
# ========== V2 路径（VISION_V2_ENABLED=true）==========
|
||||
import os as _os_v2
|
||||
|
||||
_v2_enabled = _os_v2.environ.get("VISION_V2_ENABLED", "false").lower() in ("1", "true", "yes", "on")
|
||||
if _v2_enabled:
|
||||
try:
|
||||
from worker_app.tasks.vision import analyze_images_v2 as _aiv2
|
||||
except ImportError:
|
||||
try:
|
||||
from tasks.vision import analyze_images_v2 as _aiv2 # type: ignore
|
||||
except ImportError:
|
||||
logger.warning("[vision.v2] 模块导入失败，回退 V1 路径")
|
||||
_aiv2 = None # type: ignore
|
||||
if _aiv2 is not None:
|
||||
from packages.shared.ai_client import get_doubao_client as _gdc_v2
|
||||
|
||||
_cli = _gdc_v2()
|
||||
_orig_retries_v2 = _cli.max_retries
|
||||
_cli.max_retries = 0
|
||||
try:
|
||||
results_v2 = _aiv2(normalized_urls, lite_model=lite_model, pro_model=pro_model)
|
||||
finally:
|
||||
_cli.max_retries = _orig_retries_v2
|
||||
return {"products": list(results_v2)}
|
||||
# 导入失败 → fallthrough 走 V1
|
||||
|
||||
# ========== V1 路径（默认，lite/pro 并行竞速）==========
|
||||
vision_model = lite_model
|
||||
vision_timeout = 30
|
||||
|
||||
from packages.shared.ai_client import get_doubao_client as _gdc_step
|
||||
|
||||
_step_client = _gdc_step()
|
||||
_step_orig_retries = _step_client.max_retries
|
||||
_step_client.max_retries = 0
|
||||
|
||||
results: list[dict] = [None] * len(normalized_urls) # type: ignore
|
||||
max_workers = min(2, max(1, len(normalized_urls))) # 并发≤2 防方舟限流（竞速模式下总并发=4）
|
||||
logger.info(
|
||||
"[爆款视频] 开始并行竞速图片分析(V1) n=%d lite=%s(%ds) pro=%s(75s) img_workers=%d",
|
||||
len(normalized_urls),
|
||||
vision_model,
|
||||
vision_timeout,
|
||||
pro_model,
|
||||
max_workers,
|
||||
)
|
||||
try:
|
||||
with ThreadPoolExecutor(max_workers=max_workers) as pool:
|
||||
future_to_idx = {
|
||||
pool.submit(
|
||||
_analyze_single_image, idx, url, vision_model, vision_timeout, pro_fallback_model=pro_model
|
||||
): idx
|
||||
for idx, url in enumerate(normalized_urls)
|
||||
}
|
||||
for fut in as_completed(future_to_idx):
|
||||
idx = future_to_idx[fut]
|
||||
try:
|
||||
results[idx] = fut.result()
|
||||
except Exception as e:
|
||||
logger.warning("[爆款视频] 图片 #%d future 异常 err=%s", idx, e, exc_info=True)
|
||||
results[idx] = _vision_fallback(idx, "future_exception", {"_error": str(e)[:200]})
|
||||
finally:
|
||||
_step_client.max_retries = _step_orig_retries
|
||||
|
||||
return {"products": results}
|
||||
|
||||
|
||||
def _step_video_analysis(job: ViralVideoJob) -> dict | None:
|
||||
|
||||
@@ -1,4 +1,16 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""V2 图片分析：火山OCR专用API + doubao-lite强约束JSON并行，单次pro VLM兜底。"""
|
||||
"""V2 图片分析：专用 API 组合路径（OCR + lite JSON VLM 并行 + pro VLM 兜底）。
|
||||
|
||||
灵应指令（10-05）：
|
||||
- 优先火山引擎视觉智能 API：OCR 是真实专用云端 API；
|
||||
- 人体属性/商品检测/图像标签：火山云端无公开 HTTP API（仅有移动端 SDK），
|
||||
采用 doubao-seed-2.1-lite + 强约束 JSON-only prompt 作为"伪专用 API"，
|
||||
目标 1-3s 返回结构化字段；
|
||||
- VLM（doubao-seed-2.1-pro）保留为终极兜底（置信度低/全失败时降级）；
|
||||
- 单图并行 2 路（OCR + lite JSON VLM），外层 8 图全并发，目标 8 图 <15s。
|
||||
|
||||
输出 dict 格式与 viral_video._normalize() 完全一致，下游信任链/t2i 零改动。
|
||||
灰度开关：VISION_V2_ENABLED=true（默认 false，走旧 #2198/#2199 竞速逻辑）。
|
||||
"""
|
||||
|
||||
from .fast_path import analyze_image_v2, analyze_images_v2 # noqa: F401
|
||||
|
||||
@@ -21,6 +21,8 @@ def _join_parts(*parts: str | None) -> str:
|
||||
|
||||
|
||||
_AGE_PREFIX = {
|
||||
"儿童": "小女孩" if None else "儿童",
|
||||
"青少年": "少女" if None else "少年",
|
||||
"青年": "年轻",
|
||||
"中年": "中年",
|
||||
"老年": "老年",
|
||||
@@ -153,8 +155,7 @@ def _build_portrait_prompt(fj: dict[str, Any]) -> str:
|
||||
if detail_parts:
|
||||
pieces.append("，".join(detail_parts))
|
||||
if style_parts:
|
||||
# 风格词之间不用逗号，用空格紧凑
|
||||
pieces.append("".join(style_parts) + "风格")
|
||||
pieces.append("，".join(style_parts) + "风格")
|
||||
else:
|
||||
pieces.append("人像写真")
|
||||
|
||||
|
||||
@@ -1,12 +1,9 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""V2 图片分析主路径：每图并行 OCR（火山MediaKit，未配置时自动跳过）+ qwen3.8-flash JSON VLM，
|
||||
失败时单次 qwen3.7-plus 兜底。
|
||||
"""V2 快速路径：每图并行 OCR + lite JSON VLM，失败降级 pro VLM。
|
||||
|
||||
架构（灵应10-05确认）：
|
||||
- 唯一后端：阿里云百炼 DashScope，qwen3.8-flash 做快速路径、qwen3.7-plus 做兜底
|
||||
- 主力：单图2路并行（OCR + fast VLM），外层N图全并发（workers=8）
|
||||
- 兜底：单次 pro VLM 调用，无竞速/重试/复杂超时
|
||||
- 输出 dict 格式与旧版完全一致，下游零改动
|
||||
单图并行 2 路（OCR + lite JSON VLM），目标 <3s。
|
||||
外层 8 图全并发，目标 8 图 <15s。
|
||||
终极兜底：复用旧 _analyze_single_image 完整 pro VLM 逻辑。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
@@ -17,128 +14,230 @@ import time
|
||||
from concurrent.futures import ThreadPoolExecutor, as_completed
|
||||
from typing import Any
|
||||
|
||||
from . import assembler, ocr_volc, vlm_fallback, vlm_fast_json
|
||||
from . import assembler, ocr_volc, vlm_fast_json
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# 超时（可通过环境变量覆盖）
|
||||
_IMG_WORKERS = int(os.environ.get("VISION_V2_IMG_WORKERS", "8"))
|
||||
_FAST_TIMEOUT = float(os.environ.get("VISION_V2_FAST_TIMEOUT", "12"))
|
||||
_FAST_JSON_TIMEOUT = float(os.environ.get("VISION_V2_FAST_JSON_TIMEOUT", "12"))
|
||||
_OCR_TIMEOUT = float(os.environ.get("VISION_V2_OCR_TIMEOUT", "6"))
|
||||
_PRO_TIMEOUT = float(os.environ.get("VISION_V2_PRO_TIMEOUT", "25"))
|
||||
|
||||
_FALLBACK_RESULT = {
|
||||
"name": "未识别",
|
||||
"brand": "无法判断",
|
||||
"category": "非产品图",
|
||||
"appearance": "无法判断",
|
||||
"packaging": "无法判断",
|
||||
"text_on_package": [],
|
||||
"key_features": ["无法判断"],
|
||||
"scene": "通用",
|
||||
"mood": "",
|
||||
"portrait_prompt": "无法判断",
|
||||
"summary": "未识别",
|
||||
}
|
||||
# ---------- 配置项（可通过环境变量覆盖） ----------
|
||||
VISION_V2_ENABLED = os.environ.get("VISION_V2_ENABLED", "false").lower() in ("1", "true", "yes", "on")
|
||||
# 单图 fast 路径总超时（包含 OCR + fast_json 并行）
|
||||
V2_FAST_TIMEOUT = float(os.environ.get("VISION_V2_FAST_TIMEOUT", "10"))
|
||||
# 外层图片并发（默认 8，即全并行）
|
||||
V2_IMG_WORKERS = int(os.environ.get("VISION_V2_IMG_WORKERS", "8"))
|
||||
# fast_json 单次超时
|
||||
V2_FAST_JSON_TIMEOUT = float(os.environ.get("VISION_V2_FAST_JSON_TIMEOUT", "8"))
|
||||
# OCR 单次超时
|
||||
V2_OCR_TIMEOUT = float(os.environ.get("VISION_V2_OCR_TIMEOUT", "8"))
|
||||
# pro VLM 兜底超时（仅在 fast 路径完全失败时触发）
|
||||
V2_PRO_TIMEOUT = float(os.environ.get("VISION_V2_PRO_TIMEOUT", "45"))
|
||||
V2_LITE_TIMEOUT = float(os.environ.get("VISION_V2_LITE_TIMEOUT", "20"))
|
||||
|
||||
|
||||
def _is_usable(r: dict[str, Any]) -> bool:
|
||||
pp = (r.get("portrait_prompt") or "").strip()
|
||||
def _is_result_usable(result: dict[str, Any]) -> bool:
|
||||
"""与 viral_video._is_vision_result_usable 对齐的可用判定。"""
|
||||
pp = (result.get("portrait_prompt") or "").strip()
|
||||
if pp and pp not in ("无人像", "无法判断", "未识别"):
|
||||
return True
|
||||
name = (r.get("name") or "").strip()
|
||||
name = result.get("name") or ""
|
||||
if name and name not in ("未识别", "无法判断", "未知"):
|
||||
return True
|
||||
summary = result.get("summary") or ""
|
||||
if len(summary) >= 5 and summary not in ("无法判断", "未识别"):
|
||||
return True
|
||||
cat = result.get("category") or ""
|
||||
if cat == "非产品图" and pp != "无人像":
|
||||
return True
|
||||
kf = result.get("key_features") or []
|
||||
if kf and kf != ["无法判断"]:
|
||||
# 只要有非默认特征且非空
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def analyze_image_v2(idx: int, img_url: str) -> dict[str, Any]:
|
||||
t0 = time.time()
|
||||
def _call_pro_fallback(img_url: str, idx: int, lite_model: str, pro_model: str) -> dict[str, Any] | None:
|
||||
"""fast 路径失败时，调用旧的 lite/pro 竞速 VLM。
|
||||
|
||||
fj_result: dict[str, Any] | None = None
|
||||
ocr_result: list[str] = []
|
||||
with ThreadPoolExecutor(max_workers=2) as pool:
|
||||
f_fj = pool.submit(vlm_fast_json.call_fast_json, img_url, timeout=_FAST_JSON_TIMEOUT)
|
||||
f_ocr = pool.submit(ocr_volc.call_ocr, img_url, timeout=_OCR_TIMEOUT)
|
||||
复用 viral_video._analyze_single_image 的实现，避免重复代码。
|
||||
"""
|
||||
try:
|
||||
from worker_app.tasks.viral_video import _analyze_single_image
|
||||
except ImportError:
|
||||
try:
|
||||
for fut in as_completed([f_fj, f_ocr], timeout=_FAST_TIMEOUT):
|
||||
try:
|
||||
res = fut.result(timeout=1)
|
||||
except Exception as e:
|
||||
logger.warning("[vision.v2] 图片 #%d 子任务异常: %s", idx, e)
|
||||
continue
|
||||
if fut is f_fj and isinstance(res, dict):
|
||||
fj_result = res
|
||||
elif fut is f_ocr and isinstance(res, list):
|
||||
ocr_result = res
|
||||
except TimeoutError:
|
||||
for f in (f_fj, f_ocr):
|
||||
if not f.done():
|
||||
f.cancel()
|
||||
logger.warning("[vision.v2] 图片 #%d fast路径超时(%.0fs)，走pro兜底", idx, _FAST_TIMEOUT)
|
||||
from tasks.viral_video import _analyze_single_image # type: ignore
|
||||
except ImportError:
|
||||
logger.warning("[vision.v2] 无法 import _analyze_single_image，跳过 pro 兜底")
|
||||
return None
|
||||
try:
|
||||
return _analyze_single_image(
|
||||
idx,
|
||||
img_url,
|
||||
vision_model=lite_model,
|
||||
timeout=int(V2_LITE_TIMEOUT),
|
||||
pro_fallback_model=pro_model,
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning("[vision.v2] 图片 #%d pro 兜底异常 err=%s", idx, e, exc_info=True)
|
||||
return None
|
||||
|
||||
|
||||
def analyze_image_v2(
|
||||
idx: int,
|
||||
img_url: str,
|
||||
*,
|
||||
lite_model: str | None = None,
|
||||
pro_model: str | None = None,
|
||||
) -> dict[str, Any]:
|
||||
"""单张图片 V2 分析：OCR + lite JSON VLM 并行，必要时降级 pro VLM。
|
||||
|
||||
返回的 dict 与 viral_video._normalize() 输出格式完全一致。
|
||||
"""
|
||||
t0 = time.time()
|
||||
# ---- 第 1 层：fast 路径并行 ----
|
||||
fast_json_result: dict[str, Any] | None = None
|
||||
ocr_result: list[str] = []
|
||||
|
||||
with ThreadPoolExecutor(max_workers=2) as pool:
|
||||
f_fj = pool.submit(
|
||||
vlm_fast_json.call_fast_json,
|
||||
img_url,
|
||||
model=lite_model,
|
||||
timeout=V2_FAST_JSON_TIMEOUT,
|
||||
)
|
||||
f_ocr = pool.submit(ocr_volc.call_ocr, img_url, timeout=V2_OCR_TIMEOUT)
|
||||
|
||||
# 等全部完成或超时
|
||||
for fut in as_completed([f_fj, f_ocr], timeout=V2_FAST_TIMEOUT):
|
||||
try:
|
||||
res = fut.result(timeout=1)
|
||||
except Exception as e:
|
||||
logger.warning("[vision.v2] 图片 #%d fast 子任务异常: %s", idx, e)
|
||||
continue
|
||||
if fut is f_fj:
|
||||
fast_json_result = res if isinstance(res, dict) else None
|
||||
elif fut is f_ocr:
|
||||
ocr_result = res if isinstance(res, list) else []
|
||||
|
||||
fast_elapsed = time.time() - t0
|
||||
|
||||
if fj_result:
|
||||
assembled = assembler.assemble_result(idx, fj_result, ocr_result)
|
||||
if _is_usable(assembled):
|
||||
# ---- 组装 fast 结果 ----
|
||||
assembled: dict[str, Any] | None = None
|
||||
if fast_json_result:
|
||||
assembled = assembler.assemble_result(idx, fast_json_result, ocr_result)
|
||||
if _is_result_usable(assembled):
|
||||
assembled["_fast_elapsed"] = round(fast_elapsed, 2)
|
||||
logger.info(
|
||||
"[vision.v2] 图片 #%d fast命中 elapsed=%.2fs pp=%s",
|
||||
"[vision.v2] 图片 #%d fast 路径命中 elapsed=%.2fs portrait_prompt=%s",
|
||||
idx,
|
||||
fast_elapsed,
|
||||
(assembled.get("portrait_prompt") or "")[:40],
|
||||
)
|
||||
return assembled
|
||||
logger.info(
|
||||
"[vision.v2] 图片 #%d fast 结果不可用 portrait_prompt=%s，走 pro 兜底",
|
||||
idx,
|
||||
(assembled.get("portrait_prompt") or "")[:40],
|
||||
)
|
||||
else:
|
||||
logger.info("[vision.v2] 图片 #%d fast_json 返回空 elapsed=%.2fs，走 pro 兜底", idx, fast_elapsed)
|
||||
|
||||
# ---- 第 2 层：pro VLM 兜底（复用旧竞速逻辑）----
|
||||
pro_t0 = time.time()
|
||||
pro_result = vlm_fallback.call_pro_vlm(img_url, idx, timeout=_PRO_TIMEOUT)
|
||||
if pro_result and _is_usable(pro_result):
|
||||
use_lite = lite_model or vlm_fast_json.DEFAULT_LITE_MODEL
|
||||
use_pro = pro_model or "doubao-seed-2-1-pro-260915"
|
||||
pro_result = _call_pro_fallback(img_url, idx, use_lite, use_pro)
|
||||
if pro_result and _is_result_usable(pro_result):
|
||||
pro_result["_fallback_used"] = True
|
||||
pro_result["_fast_elapsed"] = round(fast_elapsed, 2)
|
||||
pro_result["_pro_elapsed"] = round(time.time() - pro_t0, 2)
|
||||
if ocr_result and not pro_result.get("text_on_package"):
|
||||
pro_result["text_on_package"] = ocr_result[:8]
|
||||
logger.info("[vision.v2] 图片 #%d pro兜底命中 total=%.2fs", idx, time.time() - t0)
|
||||
logger.info(
|
||||
"[vision.v2] 图片 #%d pro 兜底命中 total_elapsed=%.2fs",
|
||||
idx,
|
||||
time.time() - t0,
|
||||
)
|
||||
return pro_result
|
||||
|
||||
logger.warning("[vision.v2] 图片 #%d 全路径失败 elapsed=%.2fs", idx, time.time() - t0)
|
||||
out = dict(_FALLBACK_RESULT)
|
||||
out["_source"] = "v2_all_failed"
|
||||
out["text_on_package"] = ocr_result[:8]
|
||||
out["_fast_elapsed"] = round(fast_elapsed, 2)
|
||||
return out
|
||||
# ---- 第 3 层：兜底失败，返回 assembled 或标准 fallback ----
|
||||
if assembled:
|
||||
assembled["_source"] = "v2_fast_json_degraded"
|
||||
logger.warning(
|
||||
"[vision.v2] 图片 #%d pro 兜底也失败，返回降级 fast 结果 elapsed=%.2fs",
|
||||
idx,
|
||||
time.time() - t0,
|
||||
)
|
||||
return assembled
|
||||
|
||||
# 最后的最后：返回最小可用结构
|
||||
logger.warning("[vision.v2] 图片 #%d 所有路径均失败 elapsed=%.2fs", idx, time.time() - t0)
|
||||
return {
|
||||
"name": "未识别",
|
||||
"brand": "无法判断",
|
||||
"category": "非产品图",
|
||||
"appearance": "无法判断",
|
||||
"packaging": "无法判断",
|
||||
"text_on_package": ocr_result[:8],
|
||||
"key_features": ["无法判断"],
|
||||
"scene": "通用",
|
||||
"mood": "",
|
||||
"portrait_prompt": "无法判断",
|
||||
"summary": "未识别",
|
||||
"_source": "v2_all_failed",
|
||||
}
|
||||
|
||||
|
||||
def analyze_images_v2(img_urls: list[str]) -> list[dict[str, Any]]:
|
||||
def analyze_images_v2(
|
||||
img_urls: list[str],
|
||||
*,
|
||||
lite_model: str | None = None,
|
||||
pro_model: str | None = None,
|
||||
max_workers: int | None = None,
|
||||
) -> list[dict[str, Any]]:
|
||||
"""批量图片 V2 分析（外层全并行）。"""
|
||||
if not img_urls:
|
||||
return []
|
||||
workers = min(_IMG_WORKERS, len(img_urls), 16)
|
||||
workers = max_workers if max_workers and max_workers > 0 else V2_IMG_WORKERS
|
||||
workers = min(workers, len(img_urls), 16) # 安全上限 16
|
||||
results: list[dict[str, Any] | None] = [None] * len(img_urls)
|
||||
|
||||
logger.info(
|
||||
"[vision.v2] 开始图片分析 n=%d workers=%d fast_timeout=%.0fs pro_timeout=%.0fs",
|
||||
"[vision.v2] 开始 V2 并行图片分析 n=%d workers=%d fast_timeout=%.0fs",
|
||||
len(img_urls),
|
||||
workers,
|
||||
_FAST_TIMEOUT,
|
||||
_PRO_TIMEOUT,
|
||||
V2_FAST_TIMEOUT,
|
||||
)
|
||||
t0 = time.time()
|
||||
with ThreadPoolExecutor(max_workers=workers) as pool:
|
||||
future_to_idx = {pool.submit(analyze_image_v2, idx, url): idx for idx, url in enumerate(img_urls)}
|
||||
future_to_idx = {
|
||||
pool.submit(analyze_image_v2, idx, url, lite_model=lite_model, pro_model=pro_model): idx
|
||||
for idx, url in enumerate(img_urls)
|
||||
}
|
||||
for fut in as_completed(future_to_idx):
|
||||
idx = future_to_idx[fut]
|
||||
try:
|
||||
results[idx] = fut.result()
|
||||
except Exception as e:
|
||||
logger.warning("[vision.v2] 图片 #%d future异常: %s", idx, e, exc_info=True)
|
||||
r = dict(_FALLBACK_RESULT)
|
||||
r["_source"] = "v2_future_exception"
|
||||
results[idx] = r
|
||||
|
||||
logger.warning("[vision.v2] 图片 #%d future 异常 err=%s", idx, e, exc_info=True)
|
||||
results[idx] = {
|
||||
"name": "未识别",
|
||||
"brand": "无法判断",
|
||||
"category": "非产品图",
|
||||
"appearance": "无法判断",
|
||||
"packaging": "无法判断",
|
||||
"text_on_package": [],
|
||||
"key_features": ["无法判断"],
|
||||
"scene": "通用",
|
||||
"mood": "",
|
||||
"portrait_prompt": "无法判断",
|
||||
"summary": "未识别",
|
||||
"_source": "v2_future_exception",
|
||||
}
|
||||
elapsed = time.time() - t0
|
||||
succ = sum(1 for r in results if r and _is_usable(r))
|
||||
succ = sum(1 for r in results if r and _is_result_usable(r))
|
||||
fb = sum(1 for r in results if r and r.get("_fallback_used"))
|
||||
logger.info("[vision.v2] 完成 n=%d usable=%d pro_fallback=%d elapsed=%.2fs", len(img_urls), succ, fb, elapsed)
|
||||
logger.info(
|
||||
"[vision.v2] V2 图片分析完成 n=%d success=%d pro_fallback=%d elapsed=%.2fs",
|
||||
len(img_urls),
|
||||
succ,
|
||||
fb,
|
||||
elapsed,
|
||||
)
|
||||
return [r for r in results if r is not None]
|
||||
|
||||
@@ -1,175 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""V2 pro 兜底：qwen3.7-plus（阿里云百炼/DashScope）单次调用。
|
||||
|
||||
fast_json 结果不可用时单次调用，无竞速、无重试、无复杂超时逻辑。
|
||||
直接 httpx 发精简 JSON-only prompt（比旧版 prompt_loader XML 模板短很多，降低延迟）。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import time
|
||||
from typing import Any
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_BASE_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1"
|
||||
_PRO_MODEL = "qwen3.7-plus"
|
||||
_DEFAULT_TIMEOUT = 25
|
||||
_DEFAULT_MAX_TOKENS = 800
|
||||
|
||||
_PRO_SYSTEM = (
|
||||
"你是图片分析助手。仔细观察图片，严格按JSON schema返回一个对象，不要任何解释、"
|
||||
"不要markdown、不要代码块、不要前后缀文字。字段值不确定时填null或空数组。\n"
|
||||
"{\n"
|
||||
' "has_person": true/false,\n'
|
||||
' "gender": "男"/"女"/null,\n'
|
||||
' "age_range": "儿童"/"青少年"/"青年"/"中年"/"老年"/null,\n'
|
||||
' "outfit": "人物穿搭描述，60字以内（例：白色T恤+牛仔裤）",\n'
|
||||
' "hair": "发型",\n'
|
||||
' "pose": "姿态",\n'
|
||||
' "expression": "表情",\n'
|
||||
' "scene": "场景",\n'
|
||||
' "mood": "氛围",\n'
|
||||
' "has_product": true/false,\n'
|
||||
' "category": "服饰/鞋包/美妆/数码/食品/家居/配饰/母婴/非产品图",\n'
|
||||
' "product_name": "产品名称，非产品图填null",\n'
|
||||
' "brand": "品牌或文字标识，无则null",\n'
|
||||
' "key_features": ["特征数组"]\n'
|
||||
"}"
|
||||
)
|
||||
_PRO_USER = "分析这张图片，返回符合schema的JSON。"
|
||||
|
||||
|
||||
def _strip_code_fence(s: str) -> str:
|
||||
s = s.strip()
|
||||
if s.startswith("```"):
|
||||
lines = s.split("\n")
|
||||
if lines and lines[0].startswith("```"):
|
||||
lines = lines[1:]
|
||||
if lines and lines[-1].strip().startswith("```"):
|
||||
lines = lines[:-1]
|
||||
s = "\n".join(lines).strip()
|
||||
return s
|
||||
|
||||
|
||||
def _assemble_pp(obj: dict[str, Any]) -> str:
|
||||
if not obj.get("has_person", False):
|
||||
return "无人像"
|
||||
parts: list[str] = []
|
||||
gender = obj.get("gender")
|
||||
age = obj.get("age_range")
|
||||
if gender:
|
||||
parts.append(gender + ("性" if not gender.endswith("性") else ""))
|
||||
if age:
|
||||
parts.append(age)
|
||||
parts.append("人物")
|
||||
hair = obj.get("hair")
|
||||
if hair:
|
||||
parts.append(hair)
|
||||
outfit = obj.get("outfit")
|
||||
if outfit:
|
||||
parts.append(f"身着{outfit}")
|
||||
pose = obj.get("pose")
|
||||
if pose:
|
||||
parts.append(f"姿态{pose}")
|
||||
expr = obj.get("expression")
|
||||
if expr:
|
||||
parts.append(f"表情{expr}")
|
||||
return "，".join(parts) if parts else "无人像"
|
||||
|
||||
|
||||
def call_pro_vlm(
|
||||
img_url: str,
|
||||
idx: int,
|
||||
*,
|
||||
timeout: int = _DEFAULT_TIMEOUT,
|
||||
) -> dict[str, Any] | None:
|
||||
"""单次调用 qwen3.7-plus，解析后返回 product dict；失败返回 None。"""
|
||||
t0 = time.time()
|
||||
import httpx
|
||||
|
||||
api_key = os.environ.get("DASHSCOPE_API_KEY")
|
||||
if not api_key:
|
||||
logger.warning("[vision.v2] DASHSCOPE_API_KEY 未配置，跳过 pro 兜底")
|
||||
return None
|
||||
|
||||
url = f"{_BASE_URL}/chat/completions"
|
||||
payload: dict[str, Any] = {
|
||||
"model": _PRO_MODEL,
|
||||
"messages": [
|
||||
{"role": "system", "content": _PRO_SYSTEM},
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "image_url", "image_url": {"url": img_url}},
|
||||
{"type": "text", "text": _PRO_USER},
|
||||
],
|
||||
},
|
||||
],
|
||||
"temperature": 0.3,
|
||||
"max_tokens": _DEFAULT_MAX_TOKENS,
|
||||
"stream": False,
|
||||
"enable_thinking": False,
|
||||
"response_format": {"type": "json_object"},
|
||||
}
|
||||
try:
|
||||
r = httpx.post(
|
||||
url,
|
||||
headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
|
||||
json=payload,
|
||||
timeout=timeout,
|
||||
)
|
||||
elapsed = time.time() - t0
|
||||
if r.status_code != 200:
|
||||
logger.warning("[vision.v2] pro HTTP %d elapsed=%.1fs body=%s", r.status_code, elapsed, r.text[:200])
|
||||
return None
|
||||
data = r.json()
|
||||
raw = (data.get("choices") or [{}])[0].get("message", {}).get("content")
|
||||
if not raw:
|
||||
logger.warning("[vision.v2] pro 返回空 elapsed=%.1fs", elapsed)
|
||||
return None
|
||||
usage = data.get("usage") or {}
|
||||
logger.info(
|
||||
"[vision.v2] pro 完成 idx=%d model=%s elapsed=%.1fs in=%d out=%d",
|
||||
idx,
|
||||
_PRO_MODEL,
|
||||
elapsed,
|
||||
usage.get("prompt_tokens", 0),
|
||||
usage.get("completion_tokens", 0),
|
||||
)
|
||||
text = _strip_code_fence(raw)
|
||||
l, r_pos = text.find("{"), text.rfind("}")
|
||||
if l < 0 or r_pos <= l:
|
||||
logger.warning("[vision.v2] pro 无JSON elapsed=%.1fs head=%s", elapsed, raw[:200])
|
||||
return None
|
||||
obj = json.loads(text[l : r_pos + 1])
|
||||
if not isinstance(obj, dict):
|
||||
return None
|
||||
scene = obj.get("scene") or "通用"
|
||||
mood = obj.get("mood") or ""
|
||||
pp = _assemble_pp(obj)
|
||||
has_person = obj.get("has_person", False)
|
||||
has_product = obj.get("has_product", False)
|
||||
name = obj.get("product_name") or "未识别"
|
||||
brand = obj.get("brand") or "无法判断"
|
||||
category = obj.get("category") or ("非产品图" if has_person and not has_product else "无法判断")
|
||||
return {
|
||||
"name": name,
|
||||
"brand": brand,
|
||||
"category": category,
|
||||
"appearance": obj.get("outfit") or "无法判断",
|
||||
"packaging": "无法判断",
|
||||
"text_on_package": [],
|
||||
"key_features": obj.get("key_features") or ["无法判断"],
|
||||
"scene": scene,
|
||||
"mood": mood,
|
||||
"portrait_prompt": pp,
|
||||
"summary": f"{brand} {name}" if name != "未识别" else "未识别",
|
||||
"_source": "vlm_pro",
|
||||
}
|
||||
except Exception as e:
|
||||
logger.warning("[vision.v2] pro 异常 idx=%d elapsed=%.1fs err=%s", idx, time.time() - t0, e, exc_info=True)
|
||||
return None
|
||||
@@ -1,43 +1,35 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""V2 快速路径：qwen3.8-flash（阿里云百炼/DashScope）强约束 JSON-only 调用。
|
||||
"""doubao-seed-2.1-lite 强约束 JSON-only 调用。
|
||||
|
||||
目标：替代"人体属性/商品检测/图像标签"三个火山不存在的专用云端 API。
|
||||
设计要点：
|
||||
- 直接用 httpx 发最小 payload 到 DashScope OpenAI 兼容 endpoint，不走 ai_client 包装
|
||||
- enable_thinking=false 关闭推理链（reasoning 是延迟主因）
|
||||
- system prompt 极致精简，只给字段 schema 和强约束（禁止自然语言、禁止 markdown）
|
||||
- max_tokens=350、temperature=0.1（稳定输出 JSON）
|
||||
- timeout=12s（失败由外层走 pro 兜底）
|
||||
- API Key 从环境变量 DASHSCOPE_API_KEY 读取
|
||||
- max_tokens=350（比旧 VLM 的 1200 小很多，降低延迟）
|
||||
- temperature=0.1（极低，稳定输出 JSON）
|
||||
- timeout=8s（够快，失败则由外层走 pro VLM 兜底）
|
||||
- 期望返回纯 JSON object（无 ```json 包裹、无解释文字）
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import time
|
||||
from typing import Any
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# DashScope OpenAI 兼容 endpoint
|
||||
_BASE_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1"
|
||||
_FAST_MODEL = "qwen3.8-flash"
|
||||
_DEFAULT_TIMEOUT = 12
|
||||
_DEFAULT_MAX_TOKENS = 350
|
||||
|
||||
# 极简 system prompt：只给字段定义 + 硬性输出要求
|
||||
_FAST_SYSTEM = (
|
||||
"你是图片结构化识别器。严格按下方 JSON schema 返回一个对象，不要任何解释、"
|
||||
"不要markdown、不要代码块、不要前后缀文字。字段值不确定时填 null 或空数组。\n"
|
||||
"{\n"
|
||||
' "has_person": true/false,\n'
|
||||
' "has_person": true/false, // 图中是否有人\n'
|
||||
' "gender": "男"/"女"/null,\n'
|
||||
' "age_range": "儿童"/"青少年"/"青年"/"中年"/"老年"/null,\n'
|
||||
' "upper_wear": "上装款式，如T恤/衬衫/卫衣/毛衣/西装/夹克/连衣裙/吊带/背心/外套等",\n'
|
||||
' "upper_color": "上装主色",\n'
|
||||
' "lower_wear": "下装款式；穿连衣裙时填null",\n'
|
||||
' "lower_wear": "下装款式，如牛仔裤/休闲裤/短裙/长裙/短裤/西裤/运动裤等；穿连衣裙时填null",\n'
|
||||
' "lower_color": "下装主色",\n'
|
||||
' "dress_color": "连衣裙主色（穿连衣裙时填）",\n'
|
||||
' "accessories": ["眼镜"/"帽子"/"项链"/"耳环"/"背包"/"手表"等数组],\n'
|
||||
@@ -46,7 +38,7 @@ _FAST_SYSTEM = (
|
||||
' "pose": "姿势，如站立/坐姿/侧身/行走等",\n'
|
||||
' "scene": "场景，如室内/街拍/户外/办公室/家居/海边/雪景/森林等",\n'
|
||||
' "style": "风格，如休闲/商务/运动/复古/潮流/甜美/酷飒/优雅/街头/法式等",\n'
|
||||
' "has_product": true/false,\n'
|
||||
' "has_product": true/false, // 是否有明确商品展示\n'
|
||||
' "category": "产品类目：服饰/鞋包/美妆/数码/食品/家居/配饰/母婴/非产品图",\n'
|
||||
' "product_name": "产品名称，非产品图填null",\n'
|
||||
' "brand": "品牌或文字标识，无则null",\n'
|
||||
@@ -59,17 +51,21 @@ _FAST_SYSTEM = (
|
||||
|
||||
_FAST_USER = "识别这张图片的人物穿搭与主体信息，只返回JSON对象。"
|
||||
|
||||
|
||||
def _api_key() -> str | None:
|
||||
return os.environ.get("DASHSCOPE_API_KEY")
|
||||
# 默认模型
|
||||
DEFAULT_LITE_MODEL = "doubao-seed-2-1-lite-260915"
|
||||
DEFAULT_TIMEOUT = 8
|
||||
DEFAULT_MAX_TOKENS = 350
|
||||
|
||||
|
||||
def _strip_code_fence(s: str) -> str:
|
||||
"""剥离 ```json ... ``` 包裹（即使要求纯 JSON，模型偶尔仍会包代码块）。"""
|
||||
s = s.strip()
|
||||
if s.startswith("```"):
|
||||
lines = s.split("\n")
|
||||
# 去掉首行 ```json
|
||||
if lines and lines[0].startswith("```"):
|
||||
lines = lines[1:]
|
||||
# 去掉尾行 ```
|
||||
if lines and lines[-1].strip().startswith("```"):
|
||||
lines = lines[:-1]
|
||||
s = "\n".join(lines).strip()
|
||||
@@ -79,93 +75,61 @@ def _strip_code_fence(s: str) -> str:
|
||||
def call_fast_json(
|
||||
img_url: str,
|
||||
*,
|
||||
timeout: int = _DEFAULT_TIMEOUT,
|
||||
max_tokens: int = _DEFAULT_MAX_TOKENS,
|
||||
model: str | None = None,
|
||||
timeout: int = DEFAULT_TIMEOUT,
|
||||
max_tokens: int = DEFAULT_MAX_TOKENS,
|
||||
) -> dict[str, Any] | None:
|
||||
"""调用 qwen3.8-flash 返回结构化 dict；失败/非 JSON 返回 None。"""
|
||||
"""调用 lite VLM 返回结构化 dict；失败/非 JSON 返回 None。
|
||||
|
||||
注意：不做重试（外层竞速/降级逻辑负责），max_retries=0 由外层统一设置。
|
||||
"""
|
||||
t0 = time.time()
|
||||
import httpx
|
||||
|
||||
api_key = _api_key()
|
||||
if not api_key:
|
||||
logger.warning("[vision.v2] DASHSCOPE_API_KEY 未配置，跳过 fast_json")
|
||||
return None
|
||||
|
||||
url = f"{_BASE_URL}/chat/completions"
|
||||
payload: dict[str, Any] = {
|
||||
"model": _FAST_MODEL,
|
||||
"messages": [
|
||||
{"role": "system", "content": _FAST_SYSTEM},
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "image_url", "image_url": {"url": img_url}},
|
||||
{"type": "text", "text": _FAST_USER},
|
||||
],
|
||||
},
|
||||
],
|
||||
"temperature": 0.1,
|
||||
"max_tokens": max_tokens,
|
||||
"stream": False,
|
||||
"enable_thinking": False,
|
||||
"response_format": {"type": "json_object"},
|
||||
}
|
||||
try:
|
||||
resp = httpx.post(
|
||||
url,
|
||||
headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
|
||||
json=payload,
|
||||
from packages.shared.ai_client import get_doubao_client
|
||||
|
||||
client = get_doubao_client()
|
||||
if not client.is_available:
|
||||
logger.warning("[vision.v2] doubao client 不可用，跳过 fast_json")
|
||||
return None
|
||||
|
||||
use_model = model or DEFAULT_LITE_MODEL
|
||||
raw = client.vision_completion(
|
||||
messages=[
|
||||
{"role": "system", "content": _FAST_SYSTEM},
|
||||
{"role": "user", "content": _FAST_USER},
|
||||
],
|
||||
images=[img_url],
|
||||
temperature=0.1,
|
||||
max_tokens=max_tokens,
|
||||
timeout=timeout,
|
||||
model=use_model,
|
||||
)
|
||||
elapsed = time.time() - t0
|
||||
if resp.status_code == 400 and "enable_thinking" in resp.text[:300].lower():
|
||||
# 极少数 endpoint 版本不识别 enable_thinking，重试一次不带
|
||||
logger.warning("[vision.v2] fast_json HTTP 400 thinking 参数不兼容，重试 elapsed=%.1fs", elapsed)
|
||||
payload.pop("enable_thinking", None)
|
||||
resp = httpx.post(
|
||||
url,
|
||||
headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
|
||||
json=payload,
|
||||
timeout=timeout,
|
||||
)
|
||||
elapsed = time.time() - t0
|
||||
if resp.status_code != 200:
|
||||
logger.warning(
|
||||
"[vision.v2] fast_json HTTP %d elapsed=%.1fs body=%s", resp.status_code, elapsed, resp.text[:200]
|
||||
)
|
||||
if raw is None:
|
||||
logger.warning("[vision.v2] fast_json 返回 None elapsed=%.1fs model=%s", elapsed, use_model)
|
||||
return None
|
||||
data = resp.json()
|
||||
raw = (data.get("choices") or [{}])[0].get("message", {}).get("content")
|
||||
if not raw:
|
||||
logger.warning("[vision.v2] fast_json 返回空 elapsed=%.1fs", elapsed)
|
||||
return None
|
||||
usage = data.get("usage") or {}
|
||||
reasoning_tokens = usage.get("reasoning_tokens", 0)
|
||||
ctd = usage.get("completion_tokens_details") or {}
|
||||
if not reasoning_tokens:
|
||||
reasoning_tokens = ctd.get("reasoning_tokens", 0)
|
||||
logger.info(
|
||||
"[vision.v2] fast_json 完成 model=%s elapsed=%.1fs in=%d out=%d reasoning=%d",
|
||||
_FAST_MODEL,
|
||||
elapsed,
|
||||
usage.get("prompt_tokens", 0),
|
||||
usage.get("completion_tokens", 0),
|
||||
reasoning_tokens,
|
||||
)
|
||||
|
||||
text = _strip_code_fence(raw)
|
||||
l, r = text.find("{"), text.rfind("}")
|
||||
# 截到第一个 { 和最后一个 } 之间，容忍前后偶发文字
|
||||
l = text.find("{")
|
||||
r = text.rfind("}")
|
||||
if l >= 0 and r > l:
|
||||
text = text[l : r + 1]
|
||||
try:
|
||||
obj = json.loads(text)
|
||||
except json.JSONDecodeError:
|
||||
logger.warning("[vision.v2] fast_json JSON 解析失败 elapsed=%.1fs head=%s", elapsed, raw[:200])
|
||||
logger.warning(
|
||||
"[vision.v2] fast_json JSON 解析失败 elapsed=%.1fs head=%s",
|
||||
elapsed,
|
||||
raw[:200],
|
||||
)
|
||||
return None
|
||||
if not isinstance(obj, dict):
|
||||
logger.warning("[vision.v2] fast_json 非 dict: %s", type(obj))
|
||||
return None
|
||||
logger.info(
|
||||
"[vision.v2] fast_json 完成 elapsed=%.1fs has_person=%s has_product=%s category=%s",
|
||||
"[vision.v2] fast_json 完成 model=%s elapsed=%.1fs has_person=%s has_product=%s category=%s",
|
||||
use_model,
|
||||
elapsed,
|
||||
obj.get("has_person"),
|
||||
obj.get("has_product"),
|
||||
|
||||
Reference in New Issue
Block a user