feat: initial project setup

2026-10-08 09:46:47 +08:00
commit 5833a303bc
65 changed files with 11628 additions and 0 deletions
@@ -0,0 +1,85 @@
+from PIL import Image, ImageEnhance
+import random
+import numpy as np
+import random
+
+
+def preproc(image, label, preproc_methods=['flip']):
+    if 'flip' in preproc_methods:
+        image, label = cv_random_flip(image, label)
+    if 'crop' in preproc_methods:
+        image, label = random_crop(image, label)
+    if 'rotate' in preproc_methods:
+        image, label = random_rotate(image, label)
+    if 'enhance' in preproc_methods:
+        image = color_enhance(image)
+    if 'pepper' in preproc_methods:
+        label = random_pepper(label)
+    return image, label
+
+
+def cv_random_flip(img, label):
+    if random.random() > 0.5:
+        img = img.transpose(Image.FLIP_LEFT_RIGHT)
+        label = label.transpose(Image.FLIP_LEFT_RIGHT)
+    return img, label
+
+
+def random_crop(image, label):
+    border = 30
+    image_width = image.size[0]
+    image_height = image.size[1]
+    border = int(min(image_width, image_height) * 0.1)
+    crop_win_width = np.random.randint(image_width - border, image_width)
+    crop_win_height = np.random.randint(image_height - border, image_height)
+    random_region = (
+        (image_width - crop_win_width) >> 1, (image_height - crop_win_height) >> 1, (image_width + crop_win_width) >> 1,
+        (image_height + crop_win_height) >> 1)
+    return image.crop(random_region), label.crop(random_region)
+
+
+def random_rotate(image, label, angle=15):
+    mode = Image.BICUBIC
+    if random.random() > 0.8:
+        random_angle = np.random.randint(-angle, angle)
+        image = image.rotate(random_angle, mode)
+        label = label.rotate(random_angle, mode)
+    return image, label
+
+
+def color_enhance(image):
+    bright_intensity = random.randint(5, 15) / 10.0
+    image = ImageEnhance.Brightness(image).enhance(bright_intensity)
+    contrast_intensity = random.randint(5, 15) / 10.0
+    image = ImageEnhance.Contrast(image).enhance(contrast_intensity)
+    color_intensity = random.randint(0, 20) / 10.0
+    image = ImageEnhance.Color(image).enhance(color_intensity)
+    sharp_intensity = random.randint(0, 30) / 10.0
+    image = ImageEnhance.Sharpness(image).enhance(sharp_intensity)
+    return image
+
+
+def random_gaussian(image, mean=0.1, sigma=0.35):
+    def gaussianNoisy(im, mean=mean, sigma=sigma):
+        for _i in range(len(im)):
+            im[_i] += random.gauss(mean, sigma)
+        return im
+
+    img = np.asarray(image)
+    width, height = img.shape
+    img = gaussianNoisy(img[:].flatten(), mean, sigma)
+    img = img.reshape([width, height])
+    return Image.fromarray(np.uint8(img))
+
+
+def random_pepper(img, N=0.0015):
+    img = np.array(img)
+    noiseNum = int(N * img.shape[0] * img.shape[1])
+    for i in range(noiseNum):
+        randX = random.randint(0, img.shape[0] - 1)
+        randY = random.randint(0, img.shape[1] - 1)
+        if random.randint(0, 1) == 0:
+            img[randX, randY] = 0
+        else:
+            img[randX, randY] = 255
+    return Image.fromarray(img)