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