From d8bb35f328ba9dec7c5be54a9f3c664adfb331ed Mon Sep 17 00:00:00 2001 From: fujammy <467918@qq.com> Date: Thu, 26 Dec 2024 21:29:45 +0800 Subject: [PATCH] Enhance image resizing functionality by adding mask generation for all resizing methods. Updated the resize_image function to return a mask alongside the resized image, ensuring better handling of image boundaries. Improved the calculate_mask function to support various alignment modes and ensure accurate mask creation. This update enhances the overall image processing capabilities. --- .gitignore | 4 + __pycache__/__init__.cpython-311.pyc | Bin 1884 -> 1853 bytes __pycache__/click_popup.cpython-311.pyc | Bin 1067 -> 1036 bytes __pycache__/color_picker.cpython-311.pyc | Bin 2130 -> 2099 bytes .../dynamic_image_input.cpython-311.pyc | Bin 2209 -> 2178 bytes .../image_resolution_adjuster.cpython-311.pyc | Bin 15791 -> 19317 bytes __pycache__/install.cpython-311.pyc | Bin 3175 -> 3144 bytes .../multiline_text_input.cpython-311.pyc | Bin 1072 -> 1041 bytes .../remove_empty_lines.cpython-311.pyc | Bin 3656 -> 3625 bytes __pycache__/text_combiner.cpython-311.pyc | Bin 4417 -> 4386 bytes image_resolution_adjuster.py | 208 ++++++++++-------- 11 files changed, 126 insertions(+), 86 deletions(-) create mode 100644 .gitignore diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..72ccc96 --- /dev/null +++ b/.gitignore @@ -0,0 +1,4 @@ +# Python cache files +__pycache__/ +*.py[cod] +*$py.class \ No newline at end of file diff --git a/__pycache__/__init__.cpython-311.pyc b/__pycache__/__init__.cpython-311.pyc index a627e8d11212d96dcfbaaadd1165bc28d42f8e34..f2d68ec400252817ae3b2b4f1109826d35ac2402 100644 GIT binary patch delta 52 zcmcb^x0jD^IWI340}v>x=A_HAZ{+*NA|B*v731jX?i}NspPN=0>gkeNmYS1au-S@L Gi3tE~D-ON@ delta 83 zcmdnXcZZK}IWI340}xyk%1M9Dwvq1}i&>$YRg9yjyK_i>eok?WbAE1GWvHi1YFTPd fenBdXA77rCmy%yz9AA)MRFasKle$@&Rf!1zhQ}Px diff --git a/__pycache__/click_popup.cpython-311.pyc b/__pycache__/click_popup.cpython-311.pyc index 4818ffeb2d729c63dc127eb085425e17a0f1dbff..1de41fe707ca10fa65592af78a30b5e7e8678fce 100644 GIT binary patch delta 49 zcmZ3@(Zj*LoR^o20SFXTb2f4dGKz=jWzXhI+cBmZj$87iFykopPy44IWI340}#}SY~(u1C?4i%731jX?i}NspPN=0>gkeNmYS1au$h&~mkj_t C&ke!= delta 79 zcmdlia7ln`IWI340}upl+{ks5(X7%-8m#bKc_gxIX^e8GSt&0wJbF!zaSOH bk1x;6OUW-UjxWeBDoISrN!|R6(U%PX{GA)# diff --git a/__pycache__/dynamic_image_input.cpython-311.pyc b/__pycache__/dynamic_image_input.cpython-311.pyc index 79d557a51ac9e76cbd429c360ff5ddfe9dde59f7..0820103fae5998173e45463e4cf269b2a3d94463 100644 GIT binary patch delta 48 zcmZ1|*d)lcoR^o20SKxBH*!TWipP3d#W;GpJI6TZ=cZMLdb*^RrRL-pY@Wr)&JF-6 CPz{Cv delta 79 zcmZn?Tqwx3oR^o20SFG=*vJ*dXjbE9731jX?i`YzpHm#;oS&Ok8S3ehT9%rVUyusp b$Cqd3rR0|v#~0)ml_VzRq;9TeWM>Be>z*3{ diff --git a/__pycache__/image_resolution_adjuster.cpython-311.pyc b/__pycache__/image_resolution_adjuster.cpython-311.pyc index 8a76136cbdbc7f509c9d28a87f8aacead8b149fe..b83526c096ec888adf2ab8212eb7989274575e87 100644 GIT binary patch delta 7058 zcmd5=d2k!odEW&A5Wp@j0tCPt6e)_JsDl(KUa~F5Bt;#jWRj9)Ni+p>!9yYedI9QS z76uc`HJqq63F71y34`T7{qMZNhn!=w1s?hTu-m> zTYwZvQL;Uq&h%mN+wXnHyWiXIdvAa9vsXynC9PINK>CZvMg~6KeNJa1Bj?ubI!rL+ zFf~H=$w*>Edq_tTjGj@RArI*p1EYesfl;3!4jCB@U=~9GniwNPgTyQ&b`XsA48iCw z$_U~L{8Q;rHbZt1j;wR;H{9zOE0XQco1{+%tZ6Ru&;DF0*X zw4w|q_>-w>x{u5~3?!S2kOz>+^o;quLyW7#kKQ@$DnCU%2XRn~|6;!=* zYJI3~Mx86D`QPh{`3qSVzSo!=u;jdY?4@H7NA!?TT>swMSmUl(W2b28idnjXRNPz; zp+$4$q){|g2UT%%VWdDbZ=9?WO*N3U6n|hTo3WHdRg>jc+ow5kTbHoEM>skn?jI4> zxkZa7X7L26`!!SsEv?Ebp?%Xwgs!84{-~fj%B!**`SbdMd-n}2tV=DhZ}l_kJW-uD z^#{G~tBj3x<=8aLsPhGNKK}>3{r}nE=$3XhS;D_yE-?P4DDROb;x|onhbH5DHJBTzkpyl1Me;8!x|V^H2@*{umVLEM^vBmT0CJp?TF@f3d=`P)~pGfhaM7}e{CzMzYG!M$@N4|?cVt<0H;(u;Gr%Vk9-&x$P_%_HcO3P@c1^s>-ef`!jldg}E+Ib^p;WW0p5 z_RiF^&|`vKi-2b(yAGiOVLgJhPVOv=J1at!S`j$=3DuJWy?q|Ov%*A43*nE@@;QFA zVzc5EApd#lhZQB{aTxc-qyh>n{^UAvUnMx=2sGI*_bWJ3qLltjA2v6gjxEw$Ca3I| zU4;CVMV?>9$rv*Iy|Hqth)nu4|7V>cizGracM*s)P7v%eky{eUK_mxJk1unfF3D4n zOCSGCPJ!>`PKRIZQ!mk#bLw$eIV)PES*D}l)coc`a{%_YW*jDnQH*1EY3K~KB(G&; zoQl)Jj~a&>o|SPl{4^=LUxq^)BHe!uO;(-&iv0m6=ak2#VPGjv!DXgt1t<4wIE9~b z{}^;Lp?eKblVbca=!;*?W%{XQt-D7LI1Lmo%^8~m4VOj)td;&%`&RBg33aKYy1rC7 zz){fHg|gG5{MB3LZv0=)R`FMyE*YavuPWo0O`ZE;=>CF(NUhXnD8JTS4H{|Ji`A{v zSXc_6R|tAqs8>1_C@{Tiow&6N=6B0{xb4%4p_UC4yQlF+J!bPs#^ov zWd*_tS+O@X%j7W2E94ZXVW{-XTRG@lnl^s<(&toNM8KgNI|U2=qqi>HeCyTu3s>f! 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else: + else: new_width = target_width new_height = int(target_width / img_ratio) - resized_img = img.resize((new_width, new_height), Image.Resampling.LANCZOS) - x_offset = (new_width - target_width) // 2 - y_offset = (new_height - target_height) // 2 - cropped_img = resized_img.crop((x_offset, y_offset, x_offset + target_width, y_offset + target_height)) - return np.array(cropped_img).astype(np.float32)/255.0 , target_width, target_height + resized_img = img.resize((new_width, new_height), Image.Resampling.LANCZOS) + x_offset = (new_width - target_width) // 2 + y_offset = (new_height - target_height) // 2 + cropped_img = resized_img.crop((x_offset, y_offset, x_offset + target_width, y_offset + target_height)) + + # 生成mask + mask = calculate_mask((img_width, img_height), (target_width, target_height), method) + return np.array(cropped_img).astype(np.float32)/255.0, mask, target_width, target_height elif method == 'fill': # Fill: 拉伸图像以填充目标尺寸，忽略宽高比，可能变形 resized_img = img.resize((target_width, target_height), Image.Resampling.LANCZOS) - return np.array(resized_img).astype(np.float32)/255.0, target_width, target_height + # 生成mask + mask = calculate_mask((img_width, img_height), (target_width, target_height), method) + return np.array(resized_img).astype(np.float32)/255.0, mask, target_width, target_height elif method == 'inside': - # Inside: 和 contain 相同，保持宽高比，缩小或不改变图像使其完全适合容器 - img_ratio = img_width / img_height - target_ratio = target_width / target_height - if img_ratio > target_ratio: + # Inside: 和 contain 相同，保持宽高比，缩小或不改变图像使其完全适合容器 + img_ratio = img_width / img_height + target_ratio = target_width / target_height + if img_ratio > target_ratio: new_width = target_width new_height = int(target_width / img_ratio) - else: + else: new_height = target_height new_width = int(target_height * img_ratio) - resized_img = img.resize((new_width, new_height), Image.Resampling.LANCZOS) - - # 解析背景颜色 - try: - color = tuple(int(background_color.lstrip('#')[i:i+2], 16) for i in (0, 2, 4)) - except ValueError: - color = (0, 0, 0) # 默认黑色 - - padded_img = Image.new('RGB', (target_width, target_height), color) - x_offset = (target_width - new_width) // 2 - y_offset = (target_height - new_height) // 2 - padded_img.paste(resized_img, (x_offset, y_offset)) - return np.array(padded_img).astype(np.float32)/255.0, target_width, target_height + resized_img = img.resize((new_width, new_height), Image.Resampling.LANCZOS) + + # 解析背景颜色 + try: + color = tuple(int(background_color.lstrip('#')[i:i+2], 16) for i in (0, 2, 4)) + except ValueError: + color = (0, 0, 0) # 默认黑色 + + padded_img = Image.new('RGB', (target_width, target_height), color) + x_offset = (target_width - new_width) // 2 + y_offset = (target_height - new_height) // 2 + padded_img.paste(resized_img, (x_offset, y_offset)) + + # 生成mask + mask = calculate_mask((img_width, img_height), (target_width, target_height), method) + return np.array(padded_img).astype(np.float32)/255.0, mask, target_width, target_height elif method == 'outside': - # Outside: 和 cover 相同，保持宽高比，放大或不改变图像使其完全覆盖容器 - img_ratio = img_width / img_height - target_ratio = target_width / target_height - if img_ratio > target_ratio: - new_height = target_height - new_width = int(target_height * img_ratio) - else: - new_width = target_width - new_height = int(target_width / img_ratio) - resized_img = img.resize((new_width, new_height), Image.Resampling.LANCZOS) - x_offset = (new_width - target_width) // 2 - y_offset = (new_height - target_height) // 2 - cropped_img = resized_img.crop((x_offset, y_offset, x_offset + target_width, y_offset + target_height)) - return np.array(cropped_img).astype(np.float32)/255.0 , target_width, target_height + # Outside: 和 cover 相同，保持宽高比，放大或不改变图像使其完全覆盖容器 + img_ratio = img_width / img_height + target_ratio = target_width / target_height + if img_ratio > target_ratio: + new_height = target_height + new_width = int(target_height * img_ratio) + else: + new_width = target_width + new_height = int(target_width / img_ratio) + resized_img = img.resize((new_width, new_height), Image.Resampling.LANCZOS) + x_offset = (new_width - target_width) // 2 + y_offset = (new_height - target_height) // 2 + cropped_img = resized_img.crop((x_offset, y_offset, x_offset + target_width, y_offset + target_height)) + + # 生成mask + mask = calculate_mask((img_width, img_height), (target_width, target_height), method) + return np.array(cropped_img).astype(np.float32)/255.0, mask, target_width, target_height def pad_image(image, target_width, target_height, position='center', background_color='#000000'): """Pad an image to the target dimensions with specified background color.""" @@ -247,72 +258,97 @@ def create_outline(image, background_color): return np.array(outlined).astype(np.float32) / 255.0 def calculate_mask(original_size, target_size, extend_mode, feather=0): - """计算填充区域的mask + """计算填充区域的mask，白色(1)表示图像区域，黑色(0)表示背景/填充区域 Args: original_size: (width, height) 原始图像尺寸 target_size: (width, height) 目标尺寸 extend_mode: 扩展模式 feather: 羽化程度 Returns: - torch.Tensor: mask张量 + torch.Tensor: mask张量，1表示图像区域，0表示背景/填充区域 """ orig_w, orig_h = original_size target_w, target_h = target_size - # 创建目标尺寸的mask，初始化为0（黑色，表示填充区域） + # 创建目标尺寸的mask（默认全黑，表示背景） mask = torch.zeros((target_h, target_w)) - if extend_mode in ["contain", "inside"]: - # 计算缩放后的尺寸 + if extend_mode == "fill": + mask.fill_(1.0) + + elif extend_mode in ["cover", "outside"]: + mask.fill_(1.0) + + elif extend_mode in ["contain", "inside"]: ratio = min(target_w/orig_w, target_h/orig_h) new_w = int(orig_w * ratio) new_h = int(orig_h * ratio) - # 计算偏移量（居中） x_offset = (target_w - new_w) // 2 y_offset = (target_h - new_h) // 2 - # 设置非填充区域为1（白色） - mask[y_offset:y_offset + new_h, x_offset:x_offset + new_w] = 1 + mask[y_offset:y_offset + new_h, x_offset:x_offset + new_w] = 1.0 elif extend_mode in ["top", "bottom", "left", "right", "center"]: - # 计算偏移量 - if extend_mode == "top": - x_offset = (target_w - orig_w) // 2 + # 1. 首先判断原始图像是否需要缩放 + if orig_w <= target_w and orig_h <= target_h: + # 图像小于或等于目标尺寸，不需要缩放 + new_w = orig_w + new_h = orig_h + else: + # 需要缩放，根据对齐方式选择缩放策略 + if extend_mode in ["left", "right"]: + # 左右对齐优先考虑高度 + ratio = target_h / orig_h + new_h = target_h + new_w = int(orig_w * ratio) + # 如果宽度超出，则按宽度重新计算 + if new_w > target_w: + ratio = target_w / orig_w + new_w = target_w + new_h = int(orig_h * ratio) + elif extend_mode in ["top", "bottom"]: + # 上下对齐优先考虑宽度 + ratio = target_w / orig_w + new_w = target_w + new_h = int(orig_h * ratio) + # 如果高度超出，则按高度重新计算 + if new_h > target_h: + ratio = target_h / orig_h + new_h = target_h + new_w = int(orig_w * ratio) + else: # center + # 居中对齐选择最适合的缩放比例 + ratio = min(target_w / orig_w, target_h / orig_h) + new_w = int(orig_w * ratio) + new_h = int(orig_h * ratio) + + # 2. 根据对齐方式计算偏移量 + if extend_mode == "center": + x_offset = (target_w - new_w) // 2 + y_offset = (target_h - new_h) // 2 + elif extend_mode == "top": + x_offset = (target_w - new_w) // 2 y_offset = 0 elif extend_mode == "bottom": - x_offset = (target_w - orig_w) // 2 - y_offset = target_h - orig_h + x_offset = (target_w - new_w) // 2 + y_offset = target_h - new_h elif extend_mode == "left": x_offset = 0 - y_offset = (target_h - orig_h) // 2 - elif extend_mode == "right": - x_offset = target_w - orig_w - y_offset = (target_h - orig_h) // 2 - else: # center - x_offset = (target_w - orig_w) // 2 - y_offset = (target_h - orig_h) // 2 - - # 设置非填充区域为1（白色） - mask[y_offset:y_offset + orig_h, x_offset:x_offset + orig_w] = 1 - - # 应用羽化效果 - if feather > 0: - # 将mask转换为适合卷积的格式 [B, C, H, W] - mask = mask.unsqueeze(0).unsqueeze(0) - - # 创建高斯核进行羽化 - kernel_size = 2 * feather + 1 - sigma = feather / 3 - - # 应用高斯模糊 - mask = F.gaussian_blur(mask, kernel_size=(kernel_size, kernel_size), sigma=(sigma, sigma)) - - # 转换回原始格式 - mask = mask.squeeze(0).squeeze(0) - - # 确保mask值在0-1范围内 - mask = torch.clamp(mask, 0, 1) + y_offset = (target_h - new_h) // 2 + else: # right + x_offset = target_w - new_w + y_offset = (target_h - new_h) // 2 + + # 3. 确保所有值都在有效范围内 + x_offset = max(0, min(x_offset, target_w - new_w)) + y_offset = max(0, min(y_offset, target_h - new_h)) + new_w = min(new_w, target_w) + new_h = min(new_h, target_h) + + # 4. 生成mask + if new_w > 0 and new_h > 0: + mask[y_offset:y_offset + new_h, x_offset:x_offset + new_w] = 1.0 return mask @@ -356,7 +392,7 @@ class ImageResolutionAdjuster: "min_height": ("INT", {"default": 640, "min": 1, "max": 8192, "step": 1}), "background_color": ("STRING", {"default": "#000000", "multiline": False}), "add_outline": ("BOOLEAN", {"default": False}), - "feather": ("INT", {"default": 0, "min": 0, "max": 10, "step": 1}), + # "feather": ("INT", {"default": 0, "min": 0, "max": 10, "step": 1}), }, "hidden": {"color_widget": "COMBO"} }