File size: 2,850 Bytes
9961486 | 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 | import cv2
import numpy as np
from PIL import Image
from torchvision import transforms
# ---------------------- CONSTANTS ----------------------
IMG_SIZE = 224
# ---------------------- CLAHE ----------------------
def apply_clahe(img):
img = np.array(img)
# Convert to grayscale if needed
if len(img.shape) == 3:
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
img = clahe.apply(img)
# Convert back to RGB
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
return Image.fromarray(img)
class CLAHETransform:
def __call__(self, img):
return apply_clahe(img)
# ---------------------- NOISE REDUCTION ----------------------
def apply_noise_reduction(img, method="gaussian"):
img_np = np.array(img)
if method == "gaussian":
img_np = cv2.GaussianBlur(img_np, (3, 3), sigmaX=0)
elif method == "median":
if len(img_np.shape) == 3:
img_np = np.stack(
[cv2.medianBlur(img_np[:, :, c], 3) for c in range(img_np.shape[2])],
axis=2
)
else:
img_np = cv2.medianBlur(img_np, 3)
return Image.fromarray(img_np)
class NoiseReductionTransform:
def __init__(self, method="gaussian"):
self.method = method
def __call__(self, img):
return apply_noise_reduction(img, method=self.method)
# ---------------------- GRAYSCALE HANDLING ----------------------
class GrayscaleToRGBTransform:
def __call__(self, img):
img = np.array(img)
if len(img.shape) == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
elif len(img.shape) == 3 and img.shape[2] == 1:
img = np.repeat(img, 3, axis=2)
return Image.fromarray(img)
# ---------------------- TRANSFORMS ----------------------
# ⚠️ Use this ONLY for training (NOT in Streamlit)
train_transforms = transforms.Compose([
GrayscaleToRGBTransform(),
CLAHETransform(),
NoiseReductionTransform(method="gaussian"),
transforms.Resize((IMG_SIZE, IMG_SIZE)),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(10),
transforms.RandomResizedCrop(IMG_SIZE, scale=(0.8, 1.0)),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)
])
# ✅ Use this for validation, testing, and Streamlit inference
val_test_transforms = transforms.Compose([
GrayscaleToRGBTransform(),
CLAHETransform(),
NoiseReductionTransform(method="gaussian"),
transforms.Resize((IMG_SIZE, IMG_SIZE)),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)
]) |