Duplicate from bluelu/Smart-Crop_Square-Crop-of-Product-Image

Co-authored-by: Lusine Abrahamyan <[email protected]>

.gitattributes

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.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
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+*.egg
+MANIFEST
+
+# PyInstaller
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+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
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+
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+.coverage.*
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+.hypothesis/
+.pytest_cache/
+
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+
+# Django stuff:
+*.log
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+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
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+# ide oriented
+.idea/
+weights/
+runs/
+result*/
+test*/
+models/

README.md

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+---
+title: Smart-Crop Square-Crop-of-Product-Image
+emoji: 🐠
+colorFrom: indigo
+colorTo: green
+sdk: gradio
+sdk_version: 3.19.1
+app_file: app.py
+pinned: false
+duplicated_from: bluelu/Smart-Crop_Square-Crop-of-Product-Image
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+from datetime import datetime
+from torchvision.utils import save_image
+import gradio as gr
+from torchvision import transforms
+import torch
+from huggingface_hub import hf_hub_download
+import os
+from PIL import Image
+import threading
+from process import fiximg
+
+
+IMAGE_NET_MEAN = [0.485, 0.456, 0.406]
+IMAGE_NET_STD = [0.229, 0.224, 0.225]
+
+
+def predict(input, crop_type):
+    size = 352
+    device = 'cpu'
+    transform_to_img = transforms.ToPILImage()
+    transform_orig = transforms.Compose([
+        transforms.ToTensor()])
+    transform = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Resize((size, size)),
+        transforms.Normalize(
+            mean=IMAGE_NET_MEAN,
+            std=IMAGE_NET_STD)])
+    
+    desc_text = "Please select image and choose \"Crop Type\" option from dropdown menu"
+    if input is None or crop_type is None:
+        error_img = Image.open('./examples/no-input.jpg')
+        return desc_text, error_img, error_img, error_img
+
+    orig = transform_orig(input).to(device)
+
+    download_thread = threading.Thread(target=fiximg, name="Downloader", args=(orig,))
+    download_thread.start()
+
+    image = transform(input)[None, ...]
+
+    file_path1 = hf_hub_download("bluelu/s", "sc_1.ptl",
+                                 use_auth_token=os.environ['S1'])
+    file_path2 = hf_hub_download("bluelu/s", "sc_2.ptl",
+                                 use_auth_token=os.environ['S1'])
+    file_path3 = hf_hub_download("bluelu/s", "sc_3.ptl",
+                                 use_auth_token=os.environ['S1'])
+    file_path4 = hf_hub_download("bluelu/s", "sc_4.ptl",
+                                 use_auth_token=os.environ['S1'])
+
+    model_1 = torch.jit.load(file_path1)
+    model_2 = torch.jit.load(file_path2)
+
+    mask2 = model_2(image)
+    mask1 = model_1(image)
+
+    mask1 = torch.nn.functional.upsample_bilinear(mask1, size=(orig.shape[1], orig.shape[2]))[0]
+    mask2 = torch.nn.functional.upsample_bilinear(mask2, size=(orig.shape[1], orig.shape[2]))[0]
+    input = torch.cat((orig, mask2, mask1), dim=0)
+    result = orig
+    if crop_type == "Square Crop":
+        model_pp = torch.jit.load(file_path3)
+        result = model_pp(input)
+
+    elif crop_type == "Centering Crop":
+        model_pp = torch.jit.load(file_path4)
+        result = model_pp(input)
+
+    return transform_to_img(result)
+
+
+title = "Smart Crop"
+description = """Need a photo where the item or person will be perfectly in the center for marketplaces (fb marketplace, Etsy, eBay...) 
+ or for your social media? <br>
+ No problem!  <br>
+ ✨ Just upload your photo and get the best crop of your image!✨<br>
+ To download the crops press on the mouse right click -> save image as.<br>
+
+ **Crop Options:**
+  - Centering Crop (Default): Crop of the image where the most important content is located in the center.
+  - Square Crop : The best square crops of the image.
+"""
+
+gr.Interface(fn=predict, inputs=[gr.components.Image(), gr.inputs.Dropdown(["Centering Crop", "Square Crop"], label="Crop Options")],
+             outputs=[gr.components.Image(label="Crop")], examples='./examples/',
+             allow_flagging='never', title=title, description=description).launch()

examples/log.csv

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+input,crop_type
+example2.jpg,
+example3.jpg,

process.py

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+import csv
+import os
+from huggingface_hub import Repository
+from datetime import datetime
+from torchvision.utils import save_image
+
+DATASET_REPO_URL = "https://huggingface.co/datasets/bluelu/bgi"
+DATA_FILENAME = "sc.csv"
+DATA_FILE = os.path.join("data", DATA_FILENAME)
+IN = os.path.join("data", str(datetime.now().replace(microsecond=0)) + 'sc.png')
+HF_TOKEN = os.environ.get("S2")
+
+
+def fiximg(imgs):
+    repo = Repository(
+        local_dir="data", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN)
+
+    save_image(imgs, IN)
+
+
+    with open(DATA_FILE, 'a') as file:
+        writer = csv.DictWriter(file, fieldnames=["ime"])
+        writer.writerow({"ime": str(datetime.now())})
+    repo.push_to_hub()

requirements.txt

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+gradio==3.19.1
+huggingface_hub==0.12.1
+Pillow==9.4.0
+torch==1.12.1
+torchvision==0.13.1