import os, oss2, sys, time import cv2 import numpy as np from tqdm import tqdm import matplotlib.pyplot as plt from PIL import Image, ImageEnhance sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) import config, libs def ps_color_scale_adjustment(image, shadow=0, highlight=255, midtones=1): ''' 模拟 PS 的色阶调整； 0 <= Shadow < Highlight <= 255 :param image: 传入的图片 :param shadow: 黑场(0-Highlight) :param highlight: 白场(Shadow-255) :param midtones: 灰场(9.99-0.01) :return: 图片 ''' if highlight > 255: highlight = 255 if shadow < 0: shadow = 0 if shadow >= highlight: shadow = highlight - 2 if midtones > 9.99: midtones = 9.99 if midtones < 0.01: midtones = 0.01 image = np.array(image, dtype=np.float16) # 计算白场 黑场离差 Diff = highlight - shadow image = image - shadow image[image < 0] = 0 image = (image / Diff) ** (1 / midtones) * 255 image[image > 255] = 255 image = np.array(image, dtype=np.uint8) return image def show_histogram(image, image_id, save_hist_dir, min_threshold, max_threshold): ''' 画出直方图展示 :param image: 导入图片 :param image_id: 图片id编号 :param save_hist_dir: 保存路径 :param min_threshold: 最小阈值 :param max_threshold: 最大阈值 :return: 原图image，和裁剪原图直方图高低阈值后的图片image_change ''' plt.rcParams['font.family'] = 'SimHei' plt.rcParams['axes.unicode_minus'] = False plt.hist(image.ravel(), 254, range=(2, 256), density=False) plt.hist(image.ravel(), 96, range=(2, 50), density=False) # 放大 range(0, 50),bins值最好是range的两倍，显得更稀疏，便于对比 plt.hist(image.ravel(), 110, range=(200, 255), density=False) # 放大 range(225, 255) plt.annotate('thresh1=' + str(min_threshold), # 文本内容 xy=(min_threshold, 0), # 箭头指向位置 # 阈值设定值！ xytext=(min_threshold, 500000), # 文本位置 # 阈值设定值！ arrowprops=dict(facecolor='black', width=1, shrink=5, headwidth=2)) # 箭头 plt.annotate('thresh2=' + str(max_threshold), # 文本内容 xy=(max_threshold, 0), # 箭头指向位置 # 阈值设定值！ xytext=(max_threshold, 500000), # 文本位置 # 阈值设定值！ arrowprops=dict(facecolor='black', width=1, shrink=5, headwidth=2)) # 箭头 # 在y轴上绘制一条直线 # plt.axhline(y=10000, color='r', linestyle='--', linewidth=0.5) plt.title(str(image_id)) # plt.show() # 保存直方图 save_hist_name = os.path.join(save_hist_dir, f'{image_id}_{min_threshold}&{max_threshold}.jpg') plt.savefig(save_hist_name) # 清空画布， 防止重叠展示 plt.clf() def low_find_histogram_range(image, target_frequency): ''' 循环查找在 target_frequency (y)频次限制下的直方图区间值(x) :param image: 导入图片 :param target_frequency: 直方图 y 频次限制条件 :return: 直方图区间 x，和 该区间频次 y ''' # 计算灰度直方图 hist, bins = np.histogram(image, bins=256, range=[0, 256]) # 初始化区间和频次 interval = 2 frequency = hist[255] while frequency < target_frequency: # 更新区间和频次 interval += 1 # 检查直方图的频次是否为None，如果频次是None，则将其设为0，这样可以避免将None和int进行比较报错。 frequency = hist[interval] if hist[interval] is not None else 0 frequency += hist[interval] if hist[interval] is not None else 0 print(f'x={interval}, y={frequency}') # 如果频次接近10000则停止循环 if target_frequency - 2000 <= frequency <= target_frequency + 1000: break return interval, frequency def high_find_histogram_range(image, target_frequency): ''' 循环查找在 target_frequency (y)频次限制下的直方图区间值(x) :param image: 导入图片 :param target_frequency: 直方图 y 频次限制条件 :return: 直方图区间 x，和 该区间频次 y ''' # 计算灰度直方图 hist, bins = np.histogram(image, bins=256, range=[0, 256]) # 初始化区间和频次 interval = 255 frequency = hist[255] while frequency < target_frequency: # 更新区间和频次 interval -= 1 # 检查直方图的频次是否为None，如果频次是None，则将其设为0，这样可以避免将None和int进行比较报错。 frequency = hist[interval] if hist[interval] is not None else 0 frequency += hist[interval] if hist[interval] is not None else 0 # 如果频次接近10000则停止循环 if target_frequency - 2000 <= frequency <= target_frequency + 2000: break return interval, frequency def sharpening_filter(image): ''' 锐化滤波器对图片进行锐化，增强图像中的边缘和细节 :param image: 导入图片 :return: 锐化后的图片 ''' # sharp_kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]]) sharp_kernel = np.array([[0, -0.5, 0], [-0.5, 3, -0.5], [0, -0.5, 0]]) sharpened_image = cv2.filter2D(image, -1, sharp_kernel) return sharpened_image def reduce_sharpness(image, factor): ''' 使用PIL库减弱图像锐度 :param image: 图像 :param factor: 锐度因子，0表示最大程度减弱锐度，1表示原始图像 :return: 减弱锐度后的图像 ''' # OpenCV 格式的图像转换为 PIL 的 Image 对象 image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) pil_image = Image.fromarray(image_rgb) enhancer = ImageEnhance.Sharpness(pil_image) reduced_image = enhancer.enhance(factor) # PIL 的 Image 对象转换为 OpenCV 的图像格式 image_array = np.array(reduced_image) sharpened_image = cv2.cvtColor(image_array, cv2.COLOR_RGB2BGR) return sharpened_image def find_last_x(image, slope_threshold = 1000): x = [] y = [] hist, bins = np.histogram(image, bins=256, range=[0, 256]) for i in range(2, 50): x.append(i) y.append(hist[i]) slopes = [abs(y[i + 1] - y[i]) for i in range(len(x) - 1)] current_interval = [] max_interval = [] max_x = {} for i, slope in enumerate(slopes): current_interval.append(slope) if slope >= slope_threshold: if len(current_interval) > len(max_interval): max_interval = current_interval.copy() max_x[x[i]] = slope current_interval = [] print(max_x) last_x = list(max_x)[-1] last_y = max_x[last_x] return last_x, last_y def main(pid, print_id): texture_filename = f'{input_dir}{pid}Tex1.{print_id}.jpg' input_image = cv2.imread(texture_filename) # low_x_thresh, low_y_frequency = low_find_histogram_range(input_image, low_y_limit) low_x_thresh, low_y_frequency = find_last_x(input_image, 1000) high_x_thresh, high_y_frequency = high_find_histogram_range(input_image, high_y_limit) print(f"{low_x_thresh} 区间, {low_y_frequency} 频次") print(f"{high_x_thresh} 区间, {high_y_frequency} 频次") high_output_image = ps_color_scale_adjustment(input_image, shadow=low_x_thresh, highlight=high_x_thresh, midtones=1) # high_output_image = ps_color_scale_adjustment(low_ouput_image, shadow=0, highlight=high_x_thresh, midtones=1) # 人体贴图和黑色背景交界处不进行锐化 gray = cv2.cvtColor(input_image, cv2.COLOR_BGR2GRAY) _, thresh = cv2.threshold(gray, 2, 255, cv2.THRESH_BINARY) kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 7)) gradient = cv2.morphologyEx(thresh, cv2.MORPH_GRADIENT, kernel) roi_gradient = cv2.bitwise_and(high_output_image, high_output_image, mask=gradient) # 锐化滤波器 # sharpened_image = sharpening_filter(high_output_image) sharpened_image = reduce_sharpness(high_output_image, factor=4) # 将原图边界替换锐化后的图片边界 sharpened_image[gradient != 0] = roi_gradient[gradient != 0] # 直方图标记并保存 # show_histogram(input_image, img_id, low_x_thresh, high_x_thresh) cv2.imwrite(texture_filename, sharpened_image, [cv2.IMWRITE_JPEG_QUALITY, 95]) # 保存图片的质量是原图的 95% if __name__ == "__main__": input_dir = "D:\\AI_pycharm\\Change_grayscale\\Texture_photos\\original_images\\" save_dir = "D:\\AI_pycharm\\Change_grayscale\\Texture_photos\\test\\" low_y_limit = 48000 high_y_limit = 13000 input_dir = '/data/datasets/texure_photos/' pids = '99724,99747,99762,99763,99777,99778,99807,99812,99823,99843,99405,99416,97984,97662,86153' for pid in pids.split(','): pid, print_id = pid.split('_') # 根据前缀获取文件列表 path = f'/data/datasets/texure_photos/' texture_filename = f'{path}{pid}Tex1.{print_id}.jpg' prefix = f'objs/print/{pid}/' if config.oss_bucket.object_exists(f'{prefix}{pid}Tex1.{print_id}.jpg'): print(f'{pid}Tex1.{print_id}.jpg 处理中...') if not os.path.exists(texture_filename): os.makedirs(path, exist_ok=True) config.oss_bucket.get_object_to_file(f'{prefix}{pid}Tex1.{print_id}.jpg', texture_filename) main(pid, print_id) else: print(f'文件已存在，直接上传') config.oss_bucket.put_object_from_file(f'objs/print/{pid}/{pid}Tex1.{print_id}.jpg', texture_filename) print(f'{pid}Tex1.{print_id}.jpg 处理完成')