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xzvxzv.txt
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xzvxzv.txt
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API_BASE_URL = 'http://fireeye-test-backend-container:9090/api/'
TF_SERVING_BASE_URL = 'http://fireeye-test-model-container:8501/'
task_id='1ac1e8a095df4611af387d9934799251'
image_dir = './image'
id_code_mapping = {
'dbee3deebc5444f5b011da4e5518752c': '0',
'edb4cb51d54644c08aa122d3f041bb0a': '1'
}
num_images = requests.get(
url=API_BASE_URL+'image/count',
params=dict(
task_id=task_id,
has_truth=True
)
).json()
print('该任务类别下的图片数量：',num_images)
该任务类别下的图片数量： 409
import pprint
def get_image_records(task_id):
resp = requests.get(
url=API_BASE_URL+'image',
params=dict(
task_id=task_id,
has_truth=True
)
)
if resp.status_code == 200:
return resp.json()
else:
raise RuntimeError(resp.text)
image_records=get_image_records(task_id)
import requests
from PIL import Image, ImageOps, ImageEnhance
import numpy as np
from sklearn.model_selection import train_test_split
API_BASE_URL = 'http://fireeye-test-backend-container:9090/api/'
image_dir = "./images"
category0_dir = os.path.join(image_dir, 'Category0')
category1_dir = os.path.join(image_dir, 'Category1')
if not os.path.exists(category0_dir):
os.makedirs(category0_dir)
if not os.path.exists(category1_dir):
os.makedirs(category1_dir)
def split_data(directory):
all_files = [os.path.join(directory, fname) for fname in os.listdir(directory)]
train_files, test_files = train_test_split(all_files, test_size=0.2, random_state=42)
train_files, val_files = train_test_split(train_files, test_size=0.25, random_state=42) # 0.25 x 0.8 = 0.2
return train_files, val_files, test_files
def download_image(image_id):
response = requests.get(f"{API_BASE_URL}image/download/{image_id}")
return response.content
def vertical_flip(img: Image.Image) -> Image.Image:
return ImageOps.flip(img)
def horizontal_flip(img: Image.Image) -> Image.Image:
return ImageOps.mirror(img)
def clip_image(img: Image.Image, target_height: int, target_width: int) -> Image.Image:
width, height = img.size
left = (width - target_width) / 3
top = (height - target_height) / 3
right = (width + target_width) / 5
bottom = (height + target_height) / 2
return img.crop((left, top, right, bottom))
def normalize_image(img: Image.Image) -> np.ndarray:
img_array = np.array(img)
return img_array / 255.0
def get_image_by_id(id):
r = requests.get(url=API_BASE_URL+'image/'+id)
if r.status_code == 200:
return PIL.Image.open(io.BytesIO(r.content))
else:
raise RuntimeError(r.text)
img = get_image_by_id(image_records[0]['id'])
img_vertical_flipped = vertical_flip(img)
img_horizontal_flipped = horizontal_flip(img)
img_clipped = clip_image(img, 200, 200)
img_normalized = normalize_image(img)
for record in image_records:
image_content = download_image(record['id'])
truth_id = record['truth_id']
if id_code_mapping[truth_id] == '0':
file_path = os.path.join(category0_dir, f"{record['id']}.jpg")
else:
file_path = os.path.join(category1_dir, f"{record['id']}.jpg")
with open(file_path, 'wb') as f:
f.write(image_content)
img.save(file_path, "PNG")
category0_train, category0_val, category0_test = split_data(category0_dir)
category1_train, category1_val, category1_test = split_data(category1_dir)
png
img_vertical_flipped.save("path_to_save_vertical_flipped.png")
img_horizontal_flipped.save("path_to_save_horizontal_flipped.png")
img_clipped.save("path_to_save_clipped.png")
def preprocess_and_save_images(image_list, save_dir):
for image_path in image_list:
img = Image.open(image_path)
# Apply your preprocessing steps
img_vertical_flipped = vertical_flip(img)
img_horizontal_flipped = horizontal_flip(img)
img_clipped = clip_image(img, 200, 200)
img_normalized = normalize_image(img)
# Save the preprocessed images
base_name = os.path.basename(image_path).split('.')[0]
img_vertical_flipped.save(os.path.join(save_dir, f"{base_name}_vertical_flipped.png"))
img_horizontal_flipped.save(os.path.join(save_dir, f"{base_name}_horizontal_flipped.png"))
img_clipped.save(os.path.join(save_dir, f"{base_name}_clipped.png"))
# If you want to save the normalized image, you'll need to convert it back to a PIL Image
img_normalized_pil = Image.fromarray((img_normalized * 255).astype('uint8'))
img_normalized_pil.save(os.path.join(save_dir, f"{base_name}_normalized.png"))
# Get all image paths from each category
all_category0_images = [os.path.join(category0_dir, fname) for fname in os.listdir(category0_dir)]
all_category1_images = [os.path.join(category1_dir, fname) for fname in os.listdir(category1_dir)]
# Preprocess and save images for each category
preprocess_and_save_images(all_category0_images, category0_dir)
preprocess_and_save_images(all_category1_images, category1_dir)
# Split the data into training, validation, and test sets
category0_train, category0_val, category0_test = split_data(category0_dir)
category1_train, category1_val, category1_test = split_data(category1_dir)
---------------------------------------------------------------------------
UnidentifiedImageError Traceback (most recent call last)
/tmp/ipykernel_94/3528243212.py in <module>
25
26 # Preprocess and save images for each category
---> 27 preprocess_and_save_images(all_category0_images, category0_dir)
28 preprocess_and_save_images(all_category1_images, category1_dir)
29
/tmp/ipykernel_94/3528243212.py in preprocess_and_save_images(image_list, save_dir)
1 def preprocess_and_save_images(image_list, save_dir):
2 for image_path in image_list:
----> 3 img = Image.open(image_path)
4
5 # Apply your preprocessing steps
/opt/conda/lib/python3.7/site-packages/PIL/Image.py in open(fp, mode, formats)
3007 warnings.warn(message)
3008 raise UnidentifiedImageError(
-> 3009 "cannot identify image file %r" % (filename if filename else fp)
3010 )
3011
UnidentifiedImageError: cannot identify image file './images/Category0/65b78ef7dac0469e8807fbe502e8309a.png'