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166 lines (123 loc) · 5.4 KB
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import asyncio
import os
import time
from concurrent.futures import ProcessPoolExecutor
from pathlib import Path
import aiofiles
import httpx
from PIL import Image
DOWNLOAD_LIMIT = 4
CPU_WORKERS = os.cpu_count()
IMAGE_URLS = [
"https://images.unsplash.com/photo-1516117172878-fd2c41f4a759?w=1920&h=1080&fit=crop",
"https://images.unsplash.com/photo-1532009324734-20a7a5813719?w=1920&h=1080&fit=crop",
"https://images.unsplash.com/photo-1524429656589-6633a470097c?w=1920&h=1080&fit=crop",
"https://images.unsplash.com/photo-1530224264768-7ff8c1789d79?w=1920&h=1080&fit=crop",
"https://images.unsplash.com/photo-1564135624576-c5c88640f235?w=1920&h=1080&fit=crop",
"https://images.unsplash.com/photo-1541698444083-023c97d3f4b6?w=1920&h=1080&fit=crop",
"https://images.unsplash.com/photo-1522364723953-452d3431c267?w=1920&h=1080&fit=crop",
"https://images.unsplash.com/photo-1493976040374-85c8e12f0c0e?w=1920&h=1080&fit=crop",
"https://images.unsplash.com/photo-1530122037265-a5f1f91d3b99?w=1920&h=1080&fit=crop",
"https://images.unsplash.com/photo-1516972810927-80185027ca84?w=1920&h=1080&fit=crop",
"https://images.unsplash.com/photo-1550439062-609e1531270e?w=1920&h=1080&fit=crop",
"https://images.unsplash.com/photo-1549692520-acc6669e2f0c?w=1920&h=1080&fit=crop",
]
ORIGINAL_DIR = Path("original_images")
PROCESSED_DIR = Path("processed_images")
async def download_single_image(
client: httpx.AsyncClient,
url: str,
img_num: int,
semaphore: asyncio.Semaphore,
) -> Path:
async with semaphore:
print(f"Downloading {url}...")
ts = int(time.time())
url = f"{url}?ts={ts}" # Add timestamp to avoid caching issues
response = await client.get(url, timeout=10, follow_redirects=True)
response.raise_for_status()
filename = f"image_{img_num}.jpg"
download_path = ORIGINAL_DIR / filename
async with aiofiles.open(download_path, "wb") as f:
async for chunk in response.aiter_bytes(chunk_size=8192):
await f.write(chunk)
print(f"Downloaded and saved to: {download_path}")
return download_path
async def download_images(urls: list) -> list[Path]:
dl_semaphore = asyncio.Semaphore(DOWNLOAD_LIMIT)
async with httpx.AsyncClient() as client:
async with asyncio.TaskGroup() as tg:
tasks = [
tg.create_task(
download_single_image(client, url, img_num, dl_semaphore)
)
for img_num, url in enumerate(urls, start=1)
]
img_paths = [task.result() for task in tasks]
return img_paths
def process_single_image(orig_path: Path) -> Path:
save_path = PROCESSED_DIR / orig_path.name
with Image.open(orig_path) as img:
data = list(img.getdata())
width, height = img.size
new_data = []
for i in range(len(data)):
current_r, current_g, current_b = data[i]
total_diff = 0
neighbor_count = 0
for dx, dy in [(1, 0), (0, 1)]:
x = (i % width) + dx
y = (i // width) + dy
if 0 <= x < width and 0 <= y < height:
neighbor_r, neighbor_g, neighbor_b = data[y * width + x]
diff = (
abs(current_r - neighbor_r)
+ abs(current_g - neighbor_g)
+ abs(current_b - neighbor_b)
)
total_diff += diff
neighbor_count += 1
if neighbor_count > 0:
edge_strength = total_diff // neighbor_count
if edge_strength > 30:
new_data.append((255, 255, 255))
else:
new_data.append((0, 0, 0))
else:
new_data.append((0, 0, 0))
edge_img = Image.new("RGB", (width, height))
edge_img.putdata(new_data)
edge_img.save(save_path)
print(f"Processed {orig_path} and saved to {save_path}")
return save_path
async def process_images(orig_paths: list[Path]) -> list[Path]:
loop = asyncio.get_running_loop()
with ProcessPoolExecutor(max_workers=CPU_WORKERS) as executor:
tasks = [
loop.run_in_executor(executor, process_single_image, orig_path)
for orig_path in orig_paths
]
processed_paths = await asyncio.gather(*tasks)
return processed_paths
async def main():
ORIGINAL_DIR.mkdir(parents=True, exist_ok=True)
PROCESSED_DIR.mkdir(parents=True, exist_ok=True)
start_time = time.perf_counter()
img_paths = await download_images(IMAGE_URLS)
proc_start_time = time.perf_counter()
processed_paths = await process_images(img_paths)
finished_time = time.perf_counter()
dl_total_time = proc_start_time - start_time
proc_total_time = finished_time - proc_start_time
total_time = finished_time - start_time
print(
f"\nDownloaded {len(img_paths)} images in: {dl_total_time:.2f} seconds. {(dl_total_time / total_time) * 100:.2f}% of total time",
)
print(
f"Processed {len(processed_paths)} images in: {proc_total_time:.2f} seconds. {(proc_total_time / total_time) * 100:.2f}% of total time",
)
print(
f"\nTotal execution time: {total_time:.2f} seconds. {(total_time / total_time) * 100:.2f}% of total time",
)
if __name__ == "__main__":
asyncio.run(main())