revert changes

app.py

@@ -1,74 +1,133 @@
 import os
 
+import cv2
 import gradio as gr
 import torch
 from basicsr.archs.srvgg_arch import SRVGGNetCompact
 from gfpgan.utils import GFPGANer
-from huggingface_hub import hf_hub_download
+from huggingface_hub import snapshot_download, hf_hub_download
 from realesrgan.utils import RealESRGANer
+import examples
 
 REALESRGAN_REPO_ID = 'leonelhs/realesrgan'
 GFPGAN_REPO_ID = 'leonelhs/gfpgan'
 
 os.system("pip freeze")
 
+examples.download()
+
 # background enhancer with RealESRGAN
 model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
 model_path = hf_hub_download(repo_id=REALESRGAN_REPO_ID, filename='realesr-general-x4v3.pth')
 half = True if torch.cuda.is_available() else False
 upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
 
+os.makedirs('output', exist_ok=True)
+
+
+# def inference(img, version, scale, weight):
+def predict(img, version, scale):
+    # weight /= 100
+    print(img, version, scale)
+    if scale > 4:
+        scale = 4  # avoid too large scale value
+    try:
+        extension = os.path.splitext(os.path.basename(str(img)))[1]
+        img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
+        if len(img.shape) == 3 and img.shape[2] == 4:
+            img_mode = 'RGBA'
+        elif len(img.shape) == 2:  # for gray inputs
+            img_mode = None
+            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+        else:
+            img_mode = None
 
-def download_model(file):
-    return hf_hub_download(repo_id=GFPGAN_REPO_ID, filename=file)
+        h, w = img.shape[0:2]
+        if h > 3500 or w > 3500:
+            print('too large size')
+            return None, None
 
+        if h < 300:
+            img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
 
-def predict(image, version, scale):
-    scale = int(scale)
-    face_enhancer = None
+        face_enhancer = None
+        snapshot_folder = snapshot_download(repo_id=GFPGAN_REPO_ID)
 
-    if version == 'v1.2':
-        path = download_model('GFPGANv1.2.pth')
-        face_enhancer = GFPGANer(
-            model_path=path, upscale=scale, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
-    elif version == 'v1.3':
-        path = download_model('GFPGANv1.3.pth')
-        face_enhancer = GFPGANer(
-            model_path=path, upscale=scale, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
-    elif version == 'v1.4':
-        path = download_model('GFPGANv1.4.pth')
-        face_enhancer = GFPGANer(
-            model_path=path, upscale=scale, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
-    elif version == 'RestoreFormer':
-        path = download_model('RestoreFormer.pth')
-        face_enhancer = GFPGANer(
-            model_path=path, upscale=scale, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler)
+        if version == 'v1.2':
+            path = os.path.join(snapshot_folder, 'GFPGANv1.2.pth')
+            face_enhancer = GFPGANer(
+                model_path=path, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+        elif version == 'v1.3':
+            path = os.path.join(snapshot_folder, 'GFPGANv1.3.pth')
+            face_enhancer = GFPGANer(
+                model_path=path, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+        elif version == 'v1.4':
+            path = os.path.join(snapshot_folder, 'GFPGANv1.4.pth')
+            face_enhancer = GFPGANer(
+                model_path=path, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+        elif version == 'RestoreFormer':
+            path = os.path.join(snapshot_folder, 'RestoreFormer.pth')
+            face_enhancer = GFPGANer(
+                model_path=path, upscale=2, arch='RestoreFormer', channel_multiplier=2,
+                bg_upsampler=upsampler)
 
-    _, _, output = face_enhancer.enhance(image, has_aligned=False, only_center_face=False, paste_back=True)
+        try:
+            _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
+        except RuntimeError as error:
+            print('Error', error)
 
-    return output
+        try:
+            if scale != 2:
+                interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
+                h, w = img.shape[0:2]
+                output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
+        except Exception as error:
+            print('wrong scale input.', error)
+        if img_mode == 'RGBA':  # RGBA images should be saved in png format
+            extension = 'png'
+        else:
+            extension = 'jpg'
+        save_path = f'output/out.{extension}'
+        cv2.imwrite(save_path, output)
 
+        output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
+        return output, save_path
+    except Exception as error:
+        print('global exception', error)
+        return None, None
 
-title = "GFPGAN"
-description = r"""
-<b>Practical Face Restoration Algorithm</b>
+
+title = "GFPGAN: Practical Face Restoration Algorithm"
+description = r"""Gradio demo for <a href='https://github.com/TencentARC/GFPGAN' target='_blank'><b>GFPGAN: Towards Real-World Blind Face Restoration with Generative Facial Prior</b></a>.<br>
+It can be used to restore your **old photos** or improve **AI-generated faces**.<br>
+To use it, simply upload your image.<br>
+If GFPGAN is helpful, please help to ⭐ the <a href='https://github.com/TencentARC/GFPGAN' target='_blank'>Github Repo</a> and recommend it to your friends 😊
 """
 article = r"""
-<center><span>[email protected] or [email protected]</span></center>
-</br>
-<center><a href='https://github.com/TencentARC/GFPGAN' target='_blank'>Github Repo ⭐ </a> are welcome</center>
-"""
 
+[![download](https://img.shields.io/github/downloads/TencentARC/GFPGAN/total.svg)](https://github.com/TencentARC/GFPGAN/releases)
+[![GitHub Stars](https://img.shields.io/github/stars/TencentARC/GFPGAN?style=social)](https://github.com/TencentARC/GFPGAN)
+[![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2101.04061)
+
+If you have any question, please email 📧 `[email protected]` or `[email protected]`.
+
+<center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_GFPGAN' alt='visitor badge'></center>
+<center><img src='https://visitor-badge.glitch.me/badge?page_id=Gradio_Xintao_GFPGAN' alt='visitor badge'></center>
+"""
 demo = gr.Interface(
     predict, [
-        gr.Image(type="numpy", label="Input"),
+        gr.Image(type="filepath", label="Input"),
         gr.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer'], type="value", value='v1.4', label='version'),
-        gr.Dropdown(["1", "2", "3", "4"], value="2", label="Rescaling factor")
+        gr.Number(label="Rescaling factor", value=2),
     ], [
-        gr.Image(type="numpy", label="Output", interactive=False)
+        gr.Image(type="numpy", label="Output (The whole image)"),
+        gr.File(label="Download the output image")
     ],
     title=title,
     description=description,
-    article=article)
-
+    article=article,
+    examples=[['AI-generate.jpg', 'v1.4', 2],
+              ['lincoln.jpg', 'v1.4', 2],
+              ['Blake_Lively.jpg', 'v1.4', 2],
+              ['10045.png', 'v1.4', 2]])
 demo.queue().launch()

examples.py

@@ -0,0 +1,25 @@
+import torch
+
+examples = [
+    {
+        'name': 'lincoln.jpg',
+        'url': 'https://upload.wikimedia.org/wikipedia/commons/thumb/a/ab/Abraham_Lincoln_O-77_matte_collodion_print.jpg/1024px-Abraham_Lincoln_O-77_matte_collodion_print.jpg'
+    },
+    {
+        'name': 'AI-generate.jpg',
+        'url': 'https://user-images.githubusercontent.com/17445847/187400315-87a90ac9-d231-45d6-b377-38702bd1838f.jpg'
+    },
+    {
+        'name': 'Blake_Lively.jpg',
+        'url': 'https://user-images.githubusercontent.com/17445847/187400981-8a58f7a4-ef61-42d9-af80-bc6234cef860.jpg'
+    },
+    {
+        'name': '10045.png',
+        'url': 'https://user-images.githubusercontent.com/17445847/187401133-8a3bf269-5b4d-4432-b2f0-6d26ee1d3307.png'
+    }
+]
+
+
+def download():
+    for example in examples:
+        torch.hub.download_url_to_file(example['url'], example['name'])