Add files using upload-large-folder tool

README.md

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+---
+license: other
+license_name: transparent-460-non-commercial
+license_link: >-
+  https://github.com/AnyiRao/segment-anything-with-clip/blob/main/LICENSE
+language:
+  - en
+tags:
+  - image-matting
+  - transparent-objects
+  - alpha-matte
+  - trimap
+  - computer-vision
+  - matting
+size_categories:
+  - n<1K
+task_categories:
+  - image-segmentation
+pretty_name: Transparent-460
+dataset_info:
+  features:
+    - name: fg
+      dtype: image
+    - name: alpha
+      dtype: image
+  splits:
+    - name: train
+      num_examples: 410
+    - name: test
+      num_examples: 50
+---
+
+# Transparent-460
+
+Transparent object matting dataset introduced in **TransMatting: Enhancing Transparent Objects Matting with Transformers** (ECCV 2022).
+
+Contains 460 transparent foreground images with corresponding alpha mattes. Foregrounds are composited onto background images to generate training/test pairs.
+
+## Dataset Structure
+
+```
+Transparent-460/
+├── Train/
+│   ├── fg/                              # 410 foreground images (transparent objects)
+│   ├── alpha/                           # 410 alpha mattes
+│   ├── Composition_code.py             # compositing script
+│   ├── transparent-460-train-fg-names.txt
+│   └── transparent-460-train-bg-names.txt   # 41,000 COCO BG filenames (100 per FG)
+└── Test/
+    ├── fg/                              # 50 foreground images
+    ├── alpha/                           # 50 alpha mattes
+    ├── trimap/                          # 50 trimap masks
+    ├── Composition_code.py             # compositing script
+    ├── metric_evaluation.py
+    ├── transparent-460-test-fg-names.txt
+    └── transparent-460-test-bg-names.txt    # 1,000 Pascal VOC BG filenames (20 per FG)
+```
+
+### Splits
+
+| Split | FG images | BG source | Composited pairs |
+|-------|-----------|-----------|-----------------|
+| Train | 410       | COCO train2014 | 41,000 |
+| Test  | 50        | Pascal VOC 2007 | 1,000 |
+
+### Data Fields
+
+- **fg** — foreground image of a transparent object (JPEG)
+- **alpha** — corresponding alpha matte (PNG, grayscale 0–255)
+- **trimap** — ternary region map for test set (PNG; 0=background, 128=unknown, 255=foreground)
+
+## Compositing
+
+Background images are **not** included (COCO / Pascal VOC must be downloaded separately). Use the provided `Composition_code.py` to composite FG images onto BG images:
+
+```python
+# Each train FG is composited onto 100 COCO BG images.
+# Each test FG is composited onto 20 Pascal VOC BG images.
+python Composition_code.py
+```
+
+## Example Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("ouh-7ho/transparent-460")
+
+# Train split
+train_sample = ds["train"][0]
+fg_image  = train_sample["fg"]    # PIL Image
+alpha_map = train_sample["alpha"] # PIL Image (grayscale)
+
+# Test split
+test_sample = ds["test"][0]
+fg_image   = test_sample["fg"]
+alpha_map  = test_sample["alpha"]
+trimap_map = test_sample["trimap"]
+```
+
+## Dataset Preview
+
+### Train samples
+
+| Foreground | Alpha |
+|-----------|-------|
+| Transparent glass, bottles, cups, plastic bags photographed against plain/studio BG | Grayscale matte encoding per-pixel opacity (0=fully transparent, 255=fully opaque) |
+
+### Test samples
+
+| Foreground | Alpha | Trimap |
+|-----------|-------|--------|
+| 50 held-out transparent objects | Ground-truth alpha matte | Ternary mask (black/gray/white) |
+
+> **Note:** Do not use the test set for training or validation — for final evaluation only.
+
+## License
+
+**Non-commercial research use only.**
+
+1. Available for non-commercial research purposes only.
+2. Images obtained from the Internet; authors not responsible for content.
+3. No reproduction, duplicate, copy, sell, trade, or resell for commercial purposes.
+4. No further public distribution except internal use within a single organization.
+5. Authors reserve the right to terminate access at any time.
+
+## Citation
+
+```bibtex
+@inproceedings{cai2022TransMatting,
+  title={TransMatting: Enhancing Transparent Objects Matting with Transformers},
+  author={Cai, Huanqia and Xue, Fanglei, and Xu, Lele and Guo, Lili},
+  booktitle={European Conference on Computer Vision (ECCV)},
+  year={2022},
+}
+```

README.txt

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+This is the Transparent-460 dataset, including 410 images for training and 50 images for testing.
+
+This dataset was introduced in "TransMatting: Enhancing Transparent Objects Matting with Transformers."
+
+Any use of the dataset constitutes the recipient's acceptance of the below conditions:
+
+1. The Transparent-460 database is available for non-commercial research purposes only.
+
+2. All images of the Transparent-460 database are obtained from the Internet and are not the authors' property. The authors are not responsible for these images' content or meaning.
+
+3. You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit any portion of the images and any portion of derived data for any commercial purposes.
+
+4. You agree not to further copy, publish or distribute any portion of the Transparent-460 database. Except for internal use at a single site within the same organization, it is allowed to make copies of the dataset.
+
+5. The authors reserve the right to terminate your access to the Transparent-460 database at any time.
+
+6. Any use of or reference to the dataset and any permitted distribution of the images or the Model must include the following attribution:
+
+@inproceedings{cai2022TransMatting,
+  title={TransMatting: Enhancing Transparent Objects Matting with Transformers},
+  author={Cai, Huanqia and Xue, Fanglei, and Xu, Lele and Guo, Lili},
+  booktitle={European Conference on Computer Vision (ECCV)},
+  year={2022},
+}
+

Test/Composition_code.py

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+##############################################################
+#Set your paths here
+
+#path to provided foreground images
+fg_path = "fg"
+#path to provided alpha mattes
+a_path = "alpha"
+#Path to background images
+bg_path = "bg"
+#Path to folder where you want the composited images to go
+out_path = "composited_images"
+
+fg_files = [line.rstrip('\n') for line in open('./transparent-460-test-fg-names.txt')]
+bg_files = [line.rstrip('\n') for line in open('./transparent-460-test-bg-names.txt')]
+##############################################################
+
+from PIL import Image
+import os 
+import math
+import time
+
+def composite4(fg, bg, a, w, h):
+    
+    bbox = fg.getbbox()
+    bg = bg.crop((0,0,w,h))
+    
+    fg_list = fg.load()
+    bg_list = bg.load()
+    a_list = a.load()
+    
+    for y in range(h):
+        for x in range (w):
+            alpha = a_list[x,y][0] / 255
+            t = fg_list[x,y][0]
+            t2 = bg_list[x,y][0]
+            if alpha >= 1:
+                r = int(fg_list[x,y][0])
+                g = int(fg_list[x,y][1])
+                b = int(fg_list[x,y][2])
+                bg_list[x,y] = (r, g, b, 255)
+            elif alpha > 0:
+                r = int(alpha * fg_list[x,y][0] + (1-alpha) * bg_list[x,y][0])
+                g = int(alpha * fg_list[x,y][1] + (1-alpha) * bg_list[x,y][1])
+                b = int(alpha * fg_list[x,y][2] + (1-alpha) * bg_list[x,y][2])
+                bg_list[x,y] = (r, g, b, 255)
+
+    return bg
+
+num_bgs = 20
+
+bg_iter = iter(bg_files)
+num = 0
+for im_name in fg_files:
+
+    im = Image.open(os.path.join(fg_path, im_name))
+    a = Image.open(os.path.join(a_path, im_name.split(".")[0]+".png"))
+    bbox = im.size
+    w = bbox[0]
+    h = bbox[1]
+    
+    if im.mode != 'RGB' and im.mode != 'RGBA':
+        im = im.convert('RGB')
+    
+    bcount = 0 
+    for i in range(num_bgs):
+
+        bg_name = next(bg_iter)
+        bg = Image.open(os.path.join(bg_path, bg_name))
+        if bg.mode != 'RGB':
+            bg = bg.convert('RGB')
+
+        bg_bbox = bg.size
+        bw = bg_bbox[0]
+        bh = bg_bbox[1]
+        wratio = w / bw
+        hratio = h / bh
+        ratio = wratio if wratio > hratio else hratio     
+        if ratio > 1:        
+            bg = bg.resize((math.ceil(bw*ratio),math.ceil(bh*ratio)), Image.BICUBIC)
+                     
+        out = composite4(im, bg, a, w, h)   
+
+        out.save(out_path + im_name[:len(im_name)-4] + '_' + str(bcount) + '.png', "PNG")        
+        print(out_path + im_name[:len(im_name)-4] + '_' + str(bcount) + '.png') 
+        bcount += 1
+
+
+print("Finished")

Test/README.txt

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+This is the test set of Transparent-460 with 50 images.
+
+The background images in transparent-460-test-bg-names.txt are from Pascal VOC.
+
+Here is an example of how the list works.  For the test set, each test fg is composited onto 20 test bg images.  So in the file transparent-460-test-fg-names.txt, the first entry (al-soot-1Q5LHnMalOM-unsplash.jpg) should be composited onto the first 20 images listed in transparent-460-test-bg-names.txt, the second entry in transparent-460-test-fg-names.txt should be composited onto the next 20 images in transparent-460-test-bg-names.txt., etc. Composition_code.py can be used to finish the above job.
+
+
+For academic integrity, please do not use the test set for training or validation.  Only use it for final testing.
+
+The metric_evaluation.py can be used to get metrics for the results.

Test/evaluate.py

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+
+import scipy.ndimage
+import numpy as np
+from skimage.measure import label
+import scipy.ndimage.morphology
+
+
+def gauss(x, sigma):
+    y = np.exp(-x ** 2 / (2 * sigma ** 2)) / (sigma * np.sqrt(2 * np.pi))
+    return y
+
+
+def dgauss(x, sigma):
+    y = -x * gauss(x, sigma) / (sigma ** 2)
+    return y
+
+
+def gaussgradient(im, sigma):
+    epsilon = 1e-2
+    halfsize = np.ceil(sigma * np.sqrt(-2 * np.log(np.sqrt(2 * np.pi) * sigma * epsilon))).astype(np.int32)
+    size = 2 * halfsize + 1
+    hx = np.zeros((size, size))
+    for i in range(0, size):
+        for j in range(0, size):
+            u = [i - halfsize, j - halfsize]
+            hx[i, j] = gauss(u[0], sigma) * dgauss(u[1], sigma)
+
+    hx = hx / np.sqrt(np.sum(np.abs(hx) * np.abs(hx)))
+    hy = hx.transpose()
+
+    gx = scipy.ndimage.convolve(im, hx, mode='nearest')
+    gy = scipy.ndimage.convolve(im, hy, mode='nearest')
+
+    return gx, gy
+
+
+def compute_gradient_loss(pred, target, trimap):
+
+    pred = pred / 255.0
+    target = target / 255.0
+
+    pred_x, pred_y = gaussgradient(pred, 1.4)
+    target_x, target_y = gaussgradient(target, 1.4)
+
+    pred_amp = np.sqrt(pred_x ** 2 + pred_y ** 2)
+    target_amp = np.sqrt(target_x ** 2 + target_y ** 2)
+
+    error_map = (pred_amp - target_amp) ** 2
+    loss = np.sum(error_map[trimap == 128])
+
+    return loss / 1000.
+
+
+def getLargestCC(segmentation):
+    labels = label(segmentation, connectivity=1)
+    largestCC = labels == np.argmax(np.bincount(labels.flat))
+    return largestCC
+
+
+def compute_connectivity_error(pred, target, trimap, step):
+    pred = pred / 255.0
+    target = target / 255.0
+    h, w = pred.shape
+
+    thresh_steps = list(np.arange(0, 1 + step, step))
+    l_map = np.ones_like(pred, dtype=np.float) * -1
+    for i in range(1, len(thresh_steps)):
+        pred_alpha_thresh = (pred >= thresh_steps[i]).astype(np.int)
+        target_alpha_thresh = (target >= thresh_steps[i]).astype(np.int)
+
+        omega = getLargestCC(pred_alpha_thresh * target_alpha_thresh).astype(np.int)
+        flag = ((l_map == -1) & (omega == 0)).astype(np.int)
+        l_map[flag == 1] = thresh_steps[i - 1]
+
+    l_map[l_map == -1] = 1
+
+    pred_d = pred - l_map
+    target_d = target - l_map
+    pred_phi = 1 - pred_d * (pred_d >= 0.15).astype(np.int)
+    target_phi = 1 - target_d * (target_d >= 0.15).astype(np.int)
+    loss = np.sum(np.abs(pred_phi - target_phi)[trimap == 128])
+
+    return loss / 1000.
+
+
+def compute_mse_loss(pred, target, trimap):
+    error_map = (pred - target) / 255.0
+    loss = np.sum((error_map ** 2) * (trimap == 128)) / (np.sum(trimap == 128) + 1e-8)
+
+    return loss
+
+
+def comput_sad_loss(pred, target, trimap):
+    error_map = np.abs((pred - target) / 255.0)
+    loss = np.sum(error_map * (trimap == 128))
+
+    return loss / 1000, np.sum(trimap == 128) / 1000
+

Test/metric_evaluation.py

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+import os
+import cv2
+import numpy as np
+from evaluate import comput_sad_loss, compute_mse_loss, compute_connectivity_error, compute_gradient_loss 
+import argparse
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--pred-dir', type=str, default='./predDIM/', help="pred alpha dir")
+    parser.add_argument('--label-dir', type=str, default='./Test_set/alpha_copy/', help="GT alpha dir")
+    parser.add_argument('--trimap-dir', type=str, default='./Test_set/trimaps/', help="trimap dir")
+
+    args = parser.parse_args()
+
+    mse_loss = []
+    sad_loss = []
+
+    ### loss_unknown only consider the unknown regions, i.e. trimap==128, as trimap-based methods do
+    mse_loss_unknown = []
+    sad_loss_unknown = []
+
+    grad_loss = []
+    compute_connectivity_loss = []
+
+    grad_loss_unknown = []
+    compute_connectivity_loss_unknown = []
+ 
+    for img in os.listdir(args.pred_dir):
+        label = cv2.imread(os.path.join(args.label_dir, img), 0).astype(np.float32)
+        pred = cv2.imread(os.path.join(args.pred_dir, img), 0).astype(np.float32)
+        trimap = cv2.imread(os.path.join(args.trimap_dir, img), 0).astype(np.float32)
+        if pred.shape != label.shape:
+            pred = cv2.resize(pred, (label.shape[1], label.shape[0]))
+
+        mse_loss_unknown_ = compute_mse_loss(pred, label, trimap)
+        sad_loss_unknown_ = comput_sad_loss(pred, label, trimap)[0]
+        gradient_loss_unknown = compute_gradient_loss(pred, label, trimap) 
+        connectivity_loss_unknown = compute_connectivity_error(pred, label, trimap, 0.1)
+
+        trimap[...] = 128
+        mse_loss_ = compute_mse_loss(pred, label, trimap)
+        sad_loss_ = comput_sad_loss(pred, label, trimap)[0]
+        gradient_loss = compute_gradient_loss(pred, label, trimap)
+        connectivity_loss = compute_connectivity_error(pred, label, trimap, 0.1)
+
+        print('Whole Image: MSE: ', mse_loss_, ' SAD:', sad_loss_, "GRAD:", gradient_loss, "Conn:", connectivity_loss)
+        print('Unknown Region: MSE:', mse_loss_unknown_, ' SAD:', sad_loss_unknown_, "GRAD:", gradient_loss_unknown, "Conn:", connectivity_loss_unknown)
+
+
+        mse_loss_unknown.append(mse_loss_unknown_)
+        sad_loss_unknown.append(sad_loss_unknown_)
+
+        mse_loss.append(mse_loss_)
+        sad_loss.append(sad_loss_)
+
+        grad_loss.append(gradient_loss)
+        compute_connectivity_loss.append(connectivity_loss)
+
+        grad_loss_unknown.append(gradient_loss_unknown)
+        compute_connectivity_loss_unknown.append(connectivity_loss_unknown)
+
+    print('Average:')
+    print('Whole Image: MSE:', np.array(mse_loss).mean(), ' SAD:', np.array(sad_loss).mean())
+    print('Unknown Region: MSE:', np.array(mse_loss_unknown).mean(), ' SAD:', np.array(sad_loss_unknown).mean())
+    print("whole Grad, Conn:", np.array(grad_loss).mean(), ' compute_connectivity_loss:', np.array(compute_connectivity_loss).mean())
+    print("Unknown GRAD, CONN:", np.array(grad_loss_unknown).mean(), ' CONN:', np.array(compute_connectivity_loss_unknown).mean())
+

Test/transparent-460-test-bg-names.txt

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Test/transparent-460-test-fg-names.txt

@@ -0,0 +1,50 @@
+al-soot-1Q5LHnMalOM-unsplash.jpg
+andrew-3vf0fBw15HY-unsplash.jpg
+andrew-ren-BhspCN17HT8-unsplash.jpg
+andriyko-podilnyk-tmOpbECoARc-unsplash.jpg
+ashley-whitlatch-YDj7S5iH1vY-unsplash.jpg
+behnam-norouzi-8MOL8cBGPdE-unsplash.jpg
+callum-shaw-bECXV0LLW5c-unsplash.jpg
+chuttersnap-rLm4Wq96h_0-unsplash.jpg
+colton-sturgeon-gnElhL7zlnY-unsplash.jpg
+corina-rainer-fUcpBApHI60-unsplash.jpg
+dario-mendez-SHSSaB4BLgc-unsplash.jpg
+devin-avery-I8oLKKQhyZA-unsplash.jpg
+guido-jansen-Nz-zAt4qiuU-unsplash.jpg
+hush-naidoo-jade-photography-rA6iFwKlGrY-unsplash.jpg
+izzy-gerosa-RgplfXbxLFs-unsplash.jpg
+mads-eneqvist-AFZDBU_kGeo-unsplash.jpg
+matthieu-joannon-QoX88h9Pzpw-unsplash.jpg
+nikita-tikhomirov-02reAwisPyk-unsplash.jpg
+oleksandr-sushko-ORt4YUFc_9E-unsplash.jpg
+pexels-adonyi-gaa-1582043.jpg
+pexels-anna-shvets-3683200.jpg
+pexels-anna-shvets-3683207.jpg
+pexels-ave-calvar-martinez-3934623.jpg
+pexels-cottonbro-3737629.jpg
+pexels-cottonbro-5538250.jpg
+pexels-cottonbro-9667638.jpg
+pexels-daniel-sperindeo-10025377.jpg
+pexels-ecuen-images-314296.jpg
+pexels-erin-profaci-1573240.jpg
+pexels-francesco-ungaro-3093100.jpg
+pexels-jessica-lewis-creative-189389.jpg
+pexels-karolina-grabowska-4711951.jpg
+pexels-koko-rahmadie-227906.jpg
+pexels-maria-orlova-4946713.jpg
+pexels-markus-spiske-1679719.jpg
+pexels-miriam-fischer-2650468.jpg
+pexels-noelle-otto-906056.jpg
+pexels-pixabay-276349.jpg
+pexels-pixabay-34487.jpg
+pexels-pixabay-39244.jpg
+pexels-scott-webb-137612.jpg
+pexels-skitterphoto-21462.jpg
+pexels-tara-winstead-7722861.jpg
+pexels-timo-volz-1771338.jpg
+pexels-tobias-bjorkli-1632242.jpg
+pexels-tuca-bianca-360177.jpg
+rafael-garcin-wlOv3cf41iE-unsplash.jpg
+redowan-dhrubo-BIjM08_RsBI-unsplash.jpg
+redowan-dhrubo-OWfBsDDOUlQ-unsplash.jpg
+zdenek-machacek-l1vfj2y13tk-unsplash.jpg

Train/Composition_code.py

@@ -0,0 +1,88 @@
+##############################################################
+#Set your paths here
+
+#path to provided fg images
+fg_path = "fg"
+#path to provided alpha mattes
+a_path = "alpha"
+#Path to background images
+bg_path = "bg"
+#Path to folder where you want the composited images to go
+out_path = "composited_images"
+
+fg_files = [line.rstrip('\n') for line in open('./transparent-460-train-fg-names.txt')]
+bg_files = [line.rstrip('\n') for line in open('./transparent-460-train-bg-names.txt')]
+
+##############################################################
+
+from PIL import Image
+import os 
+import math
+import time
+
+def composite4(fg, bg, a, w, h):
+    
+    bbox = fg.getbbox()
+    bg = bg.crop((0,0,w,h))
+    
+    fg_list = fg.load()
+    bg_list = bg.load()
+    a_list = a.load()
+    
+    for y in range(h):
+        for x in range (w):
+            alpha = a_list[x,y][0] / 255
+            t = fg_list[x,y][0]
+            t2 = bg_list[x,y][0]
+            if alpha >= 1:
+                r = int(fg_list[x,y][0])
+                g = int(fg_list[x,y][1])
+                b = int(fg_list[x,y][2])
+                bg_list[x,y] = (r, g, b, 255)
+            elif alpha > 0:
+                r = int(alpha * fg_list[x,y][0] + (1-alpha) * bg_list[x,y][0])
+                g = int(alpha * fg_list[x,y][1] + (1-alpha) * bg_list[x,y][1])
+                b = int(alpha * fg_list[x,y][2] + (1-alpha) * bg_list[x,y][2])
+                bg_list[x,y] = (r, g, b, 255)
+
+    return bg
+
+num_bgs = 100
+
+bg_iter = iter(bg_files)
+num = 0
+for im_name in fg_files:
+    im = Image.open(os.path.join(fg_path, im_name))
+    a = Image.open(os.path.join(a_path, im_name.split(".")[0]+".png"))
+    bbox = im.size
+    w = bbox[0]
+    h = bbox[1]
+    
+    if im.mode != 'RGB' and im.mode != 'RGBA':
+        im = im.convert('RGB')
+    
+    bcount = 0 
+    for i in range(num_bgs):
+
+        bg_name = next(bg_iter)
+        bg = Image.open(os.path.join(bg_path, bg_name))
+        if bg.mode != 'RGB':
+            bg = bg.convert('RGB')
+
+        bg_bbox = bg.size
+        bw = bg_bbox[0]
+        bh = bg_bbox[1]
+        wratio = w / bw
+        hratio = h / bh
+        ratio = wratio if wratio > hratio else hratio     
+        if ratio > 1:        
+            bg = bg.resize((math.ceil(bw*ratio),math.ceil(bh*ratio)), Image.BICUBIC)
+                     
+        out = composite4(im, bg, a, w, h)   
+
+        out.save(out_path + im_name[:len(im_name)-4] + '_' + str(bcount) + '.png', "PNG")        
+        print(out_path + im_name[:len(im_name)-4] + '_' + str(bcount) + '.png') 
+        bcount += 1
+
+
+print("Finished")

Train/README.txt

@@ -0,0 +1 @@
+The background images in transparent-460-train-bg-names.txt are from COCO.

Train/transparent-460-train-fg-names.txt

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