File size: 6,895 Bytes
7b80802 9182215 7b80802 1edc7c3 7b80802 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 |
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 zeroscratches import EraseScratches
os.system("pip freeze")
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 .")
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')
# 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)
os.makedirs('output', exist_ok=True)
# def inference(img, version, scale, weight):
def enhance_image(img, version, scale):
# weight /= 100
print(img, version, scale)
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
h, w = img.shape[0:2]
if h < 300:
img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
if version == 'M1':
face_enhancer = GFPGANer(
model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
elif version == 'M2':
face_enhancer = GFPGANer(
model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
elif version == 'M3':
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)
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)
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
# Function to remove scratches from an image
def remove_scratches(img):
scratch_remover = EraseScratches()
img_without_scratches = scratch_remover.erase(img)
return img_without_scratches
import tempfile
# Function for performing operations sequentially
def process_image(img):
try:
# Create a unique temporary directory for each request
temp_dir = tempfile.mkdtemp()
# Generate a unique filename for the temporary file
unique_filename = 'temp_image.jpg'
temp_file_path = os.path.join(temp_dir, unique_filename)
# Remove scratches from the input image
img_without_scratches = remove_scratches(img)
# Save the image without scratches to the temporary file
cv2.imwrite(temp_file_path, cv2.cvtColor(img_without_scratches, cv2.COLOR_BGR2RGB))
# Enhance the image using the saved file path
enhanced_img, save_path = enhance_image(temp_file_path, version='M2', scale=2)
# Convert the enhanced image to RGB format
enhanced_img_rgb = cv2.cvtColor(enhanced_img, cv2.COLOR_BGR2RGB)
# Delete the temporary file and directory
os.remove(temp_file_path)
os.rmdir(temp_dir)
# Return the enhanced image in RGB format and the path where it's saved
return enhanced_img, save_path
except Exception as e:
print('Error processing image:', e)
return None, None
# Gradio interface
title = "<span style='color: crimson;'>Aiconvert.online</span>"
description = r"""
"""
article = r"""
"""
demo = gr.Interface(
process_image, [
gr.Image(type="pil", label="Input"),
], [
gr.Image(type="numpy", label="Result Image"),
gr.File(label="Download the output image")
],
theme="syddharth/gray-minimal",
title=title,
description=description,
article=article)
demo.queue().launch()
|