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import os |
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import requests |
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import random |
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import _thread as thread |
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from uuid import uuid4 |
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import urllib |
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import numpy as np |
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import skimage |
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from skimage.filters import gaussian |
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from PIL import Image |
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def compress_image(image, path_original): |
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size = 1920, 1080 |
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width = 1920 |
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height = 1080 |
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name = os.path.basename(path_original).split('.') |
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first_name = os.path.join(os.path.dirname(path_original), name[0] + '.jpg') |
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if image.size[0] > width and image.size[1] > height: |
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image.thumbnail(size, Image.ANTIALIAS) |
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image.save(first_name, quality=85) |
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elif image.size[0] > width: |
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wpercent = (width/float(image.size[0])) |
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height = int((float(image.size[1])*float(wpercent))) |
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image = image.resize((width,height), Image.ANTIALIAS) |
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image.save(first_name,quality=85) |
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elif image.size[1] > height: |
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wpercent = (height/float(image.size[1])) |
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width = int((float(image.size[0])*float(wpercent))) |
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image = image.resize((width,height), Image.ANTIALIAS) |
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image.save(first_name, quality=85) |
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else: |
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image.save(first_name, quality=85) |
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def convertToJPG(path_original): |
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img = Image.open(path_original) |
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name = os.path.basename(path_original).split('.') |
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first_name = os.path.join(os.path.dirname(path_original), name[0] + '.jpg') |
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if img.format == "JPEG": |
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image = img.convert('RGB') |
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compress_image(image, path_original) |
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img.close() |
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elif img.format == "GIF": |
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i = img.convert("RGBA") |
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bg = Image.new("RGBA", i.size) |
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image = Image.composite(i, bg, i) |
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compress_image(image, path_original) |
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img.close() |
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elif img.format == "PNG": |
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try: |
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image = Image.new("RGB", img.size, (255,255,255)) |
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image.paste(img,img) |
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compress_image(image, path_original) |
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except ValueError: |
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image = img.convert('RGB') |
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compress_image(image, path_original) |
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img.close() |
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elif img.format == "BMP": |
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image = img.convert('RGB') |
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compress_image(image, path_original) |
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img.close() |
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def blur(image, x0, x1, y0, y1, sigma=1, multichannel=True): |
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y0, y1 = min(y0, y1), max(y0, y1) |
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x0, x1 = min(x0, x1), max(x0, x1) |
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im = image.copy() |
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sub_im = im[y0:y1,x0:x1].copy() |
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blur_sub_im = gaussian(sub_im, sigma=sigma, multichannel=multichannel) |
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blur_sub_im = np.round(255 * blur_sub_im) |
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im[y0:y1,x0:x1] = blur_sub_im |
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return im |
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def download(url, filename): |
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data = requests.get(url).content |
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with open(filename, 'wb') as handler: |
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handler.write(data) |
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return filename |
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def generate_random_filename(upload_directory, extension): |
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filename = str(uuid4()) |
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filename = os.path.join(upload_directory, filename + "." + extension) |
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return filename |
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def clean_me(filename): |
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if os.path.exists(filename): |
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os.remove(filename) |
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def clean_all(files): |
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for me in files: |
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clean_me(me) |
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def create_directory(path): |
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os.makedirs(os.path.dirname(path), exist_ok=True) |
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def get_model_bin(url, output_path): |
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if not os.path.exists(output_path): |
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create_directory(output_path) |
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urllib.request.urlretrieve(url, output_path) |
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return output_path |
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def get_multi_model_bin(model_list): |
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for m in model_list: |
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thread.start_new_thread(get_model_bin, m) |
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