237 lines
9.7 KiB
Python
237 lines
9.7 KiB
Python
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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