Spaces:
Build error
Build error
File size: 5,518 Bytes
50695f9 |
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 |
import glob
import os
import io
import ffmpeg
import requests
from PIL import Image
import shutil
import concurrent.futures
import gradio as gr
import cv2
import re
def process_image(mask_data, image_path):
image = Image.open(image_path)
image_data = io.BytesIO()
image.save(image_data, format=image.format)
image_data = image_data.getvalue()
# Prepare form data
form_data = {
'ldmSteps': 25,
'ldmSampler': 'plms',
'zitsWireframe': True,
'hdStrategy': 'Original',
'hdStrategyCropMargin': 196,
'hdStrategyCropTrigerSize': 1280,
'hdStrategyResizeLimit': 2048,
'prompt': '',
'negativePrompt': '',
'croperX': -24,
'croperY': -23,
'croperHeight': 512,
'croperWidth': 512,
'useCroper': False,
'sdMaskBlur': 5,
'sdStrength': 0.75,
'sdSteps': 50,
'sdGuidanceScale': 7.5,
'sdSampler': 'pndm',
'sdSeed': 42,
'sdMatchHistograms': False,
'sdScale': 1,
'cv2Radius': 5,
'cv2Flag': 'INPAINT_NS',
'paintByExampleSteps': 50,
'paintByExampleGuidanceScale': 7.5,
'paintByExampleSeed': 42,
'paintByExampleMaskBlur': 5,
'paintByExampleMatchHistograms': False,
'sizeLimit': 1024,
}
files_data = {
'image': (os.path.basename(image_path), image_data),
'mask': ('mask.png', mask_data)
}
response = requests.post('https://ahmedghani-lama-cleaner-lama.hf.space/inpaint', data=form_data, files=files_data)
if response.headers['Content-Type'] == 'image/jpeg' or response.headers['Content-Type'] == 'image/png':
output_image_path = os.path.join('output_images', os.path.splitext(os.path.basename(image_path))[0] + '_inpainted' + os.path.splitext(image_path)[1])
with open(output_image_path, 'wb') as output_image_file:
output_image_file.write(response.content)
else:
print(f"Error processing {image_path}: {response.text}")
def remove_watermark(sketch, images_path='frames', output_path='output_images'):
if os.path.exists('output_images'):
shutil.rmtree('output_images')
os.makedirs('output_images')
mask_data = io.BytesIO()
sketch["mask"].save(mask_data, format=sketch["mask"].format)
mask_data = mask_data.getvalue()
image_paths = glob.glob(f'{images_path}/*.*')
with concurrent.futures.ThreadPoolExecutor() as executor:
executor.map(lambda image_path: process_image(mask_data, image_path), image_paths)
return gr.Video.update(value=convert_frames_to_video('output_images'), visible=True), gr.Button.update(value='Done!')
# def convert_video_to_frames(video):
# print(f" input video is : {video}")
# if os.path.exists('input_video.mp4'):
# os.remove('input_video.mp4')
# ffmpeg.input(video).output('input_video.mp4').run()
# video_path = 'input_video.mp4'
# if os.path.exists('frames'):
# shutil.rmtree('frames')
# os.makedirs('frames')
# video_name = os.path.splitext(os.path.basename(video_path))[0]
# ffmpeg.input(video_path).output(f'frames/{video_name}_%d.jpg', qscale=2).run()
# return gr.Image.update(value=f"{os.getcwd()}/frames/{video_name}_1.jpg", interactive=True), gr.Button.update(interactive=True)
# def convert_frames_to_video(frames_path):
# if os.path.exists('output_video.mp4'):
# os.remove('output_video.mp4')
# (
# ffmpeg
# .input(f'{frames_path}/*.jpg', pattern_type='glob', framerate=25)
# .output('output_video.mp4')
# .run()
# )
# return gr.Video.update(value='output_video.mp4', visible=True, interactive=True), gr.Button.update(interactive=False)
def convert_video_to_frames(video):
if os.path.exists('input_video.mp4'):
os.remove('input_video.mp4')
os.system(f"ffmpeg -i {video} input_video.mp4")
video_path = 'input_video.mp4'
if os.path.exists('frames'):
shutil.rmtree('frames')
os.makedirs('frames')
video_name = os.path.splitext(os.path.basename(video_path))[0]
vidcap = cv2.VideoCapture(video_path)
success, image = vidcap.read()
count = 1
while success:
cv2.imwrite(f"frames/{video_name}_{count}.jpg", image)
success, image = vidcap.read()
count += 1
return gr.Image.update(value=f"{os.getcwd()}/frames/{video_name}_1.jpg", interactive=True), gr.Button.update(interactive=True)
def convert_frames_to_video(frames_path):
if os.path.exists('output_video.mp4'):
os.remove('output_video.mp4')
img_array = []
filelist = glob.glob(f"{frames_path}/*.jpg")
# Sort frames by number
frame_numbers = [int(re.findall(r'\d+', os.path.basename(frame))[0]) for frame in filelist]
sorted_frames = [frame for _, frame in sorted(zip(frame_numbers, filelist), key=lambda pair: pair[0])]
for filename in sorted_frames:
img = cv2.imread(filename)
height, width, layers = img.shape
size = (width, height)
img_array.append(img)
out = cv2.VideoWriter('output_video.mp4', cv2.VideoWriter_fourcc(*'mp4v'), 25, size)
for i in range(len(img_array)):
out.write(img_array[i])
out.release()
return gr.Video.update(value='output_video.mp4', visible=True, interactive=True), gr.Button.update(interactive=False) |