Update app.py

app.py

@@ -1,137 +1,56 @@
 import os
-
 import cv2
-import gradio as gr
 import torch
-from basicsr.archs.srvgg_arch import SRVGGNetCompact
-from gfpgan.utils import GFPGANer
-from realesrgan.utils import RealESRGANer
+from flask import Flask, request, jsonify, send_file
+
+# Importe as classes e funções necessárias para seus modelos aqui
+
+# Carregue os modelos
+model_realesr = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
+model_path_realesr = 'realesr-general-x4v3.pth'
 
-os.system("pip freeze")
-# download weights
-if not os.path.exists('realesr-general-x4v3.pth'):
-    os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .")
-if not os.path.exists('GFPGANv1.2.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P .")
-if not os.path.exists('GFPGANv1.3.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P .")
-if not os.path.exists('GFPGANv1.4.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P .")
-if not os.path.exists('RestoreFormer.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -P .")
-if not os.path.exists('CodeFormer.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/CodeFormer.pth -P .")
+model_gfpgan_1_2 = GFPGANer(model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+model_gfpgan_1_3 = GFPGANer(model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+model_gfpgan_1_4 = GFPGANer(model_path='GFPGANv1.4.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
 
-torch.hub.download_url_to_file(
-    'https://thumbs.dreamstime.com/b/tower-bridge-traditional-red-bus-black-white-colors-view-to-tower-bridge-london-black-white-colors-108478942.jpg',
-    'a1.jpg')
-torch.hub.download_url_to_file(
-    'https://media.istockphoto.com/id/523514029/photo/london-skyline-b-w.jpg?s=612x612&w=0&k=20&c=kJS1BAtfqYeUDaORupj0sBPc1hpzJhBUUqEFfRnHzZ0=',
-    'a2.jpg')
-torch.hub.download_url_to_file(
-    'https://i.guim.co.uk/img/media/06f614065ed82ca0e917b149a32493c791619854/0_0_3648_2789/master/3648.jpg?width=700&quality=85&auto=format&fit=max&s=05764b507c18a38590090d987c8b6202',
-    'a3.jpg')
-torch.hub.download_url_to_file(
-    'https://i.pinimg.com/736x/46/96/9e/46969eb94aec2437323464804d27706d--victorian-london-victorian-era.jpg',
-    'a4.jpg')
+# Defina o modelo RestoreFormer se necessário
+# model_restoreformer = ...
 
-# 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 = '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)
+# Defina o modelo CodeFormer se necessário
+# model_codeformer = ...
 
-os.makedirs('output', exist_ok=True)
+# Defina o modelo RealESR-General-x4v3 se necessário
+# model_realesr_general = ...
 
+app = Flask(__name__)
 
-# def inference(img, version, scale, weight):
-def inference(img, version, scale):
-    # weight /= 100
-    print(img, version, scale)
+@app.route('/reconstruir', methods=['POST'])
+def reconstruir_imagem():
     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
+        version = request.form.get('version', 'v1.4')
+        scale = int(request.form.get('scale', 2))
+        img_file = request.files['imagem']
 
-        h, w = img.shape[0:2]
-        if h < 300:
-            img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
+        temp_filename = 'temp.jpg'
+        img_file.save(temp_filename)
 
         if version == 'v1.2':
-            face_enhancer = GFPGANer(
-            model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+            face_enhancer = model_gfpgan_1_2
         elif version == 'v1.3':
-            face_enhancer = GFPGANer(
-            model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+            face_enhancer = model_gfpgan_1_3
         elif version == 'v1.4':
-            face_enhancer = GFPGANer(
-            model_path='GFPGANv1.4.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
-        # elif version == 'RestoreFormer':
-        #     face_enhancer = GFPGANer(
-        #     model_path='RestoreFormer.pth', upscale=2, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler)
-        # elif version == 'CodeFormer':
-        #      face_enhancer = GFPGANer(
-        #      model_path='CodeFormer.pth', upscale=2, arch='CodeFormer', channel_multiplier=2, bg_upsampler=upsampler)
-        # elif version == 'RealESR-General-x4v3':
-        #      face_enhancer = GFPGANer(
-        #      model_path='realesr-general-x4v3.pth', upscale=2, arch='realesr-general', channel_multiplier=2, bg_upsampler=upsampler)
+            face_enhancer = model_gfpgan_1_4
+        # Adicione mais condições para outros modelos, se necessário
 
-        try:
-            # _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True, weight=weight)
-            _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
-        except RuntimeError as error:
-            print('Error', error)
+        output, save_path = inference(temp_filename, version, scale)
 
-        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'
+        if output is not None:
+            return send_file(save_path, mimetype='image/jpeg')
         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
-
+            return jsonify({'error': 'Falha na reconstrução da imagem'})
 
-title = "JSPhoto Restauration"
-description = r"""Restaure suas fotos desfocadas e de péssima qualidade.<br>
-"""
-article = r"""
+    except Exception as e:
+        return jsonify({'error': str(e)})
 
-"""
-demo = gr.Interface(
-    inference, [
-        gr.inputs.Image(type="filepath", label="Input"),
-        # gr.inputs.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer', 'CodeFormer'], type="value", default='v1.4', label='version'),
-        gr.inputs.Radio(['v1.2', 'v1.3', 'v1.4'], type="value", default='v1.4', label='version'),
-        gr.inputs.Number(label="Rescaling factor", default=2),
-        # gr.Slider(0, 100, label='Weight, only for CodeFormer. 0 for better quality, 100 for better identity', default=50)
-    ], [
-        gr.outputs.Image(type="numpy", label="Output (The whole image)"),
-        gr.outputs.File(label="Download the output image")
-    ],
-    title=title,
-    description=description,
-    article=article,
-    # examples=[['AI-generate.jpg', 'v1.4', 2, 50], ['lincoln.jpg', 'v1.4', 2, 50], ['Blake_Lively.jpg', 'v1.4', 2, 50],
-    #           ['10045.png', 'v1.4', 2, 50]]).launch()
-    examples=[])
-    
-demo.queue(concurrency_count=4)
-demo.launch()
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=80)