import cv2 import numpy as np import pandas as pd from scipy import ndimage class ThermalProcessor: """ 热成像数据处理类 将CSV格式的热成像数据转换为热图 """ def __init__(self): """初始化热成像处理器""" # 定义默认的颜色映射 self.default_colors = [ (0, 0, 0), # 黑色 (0, 0, 0.5), # 深蓝 (0, 0.7, 1), # 亮蓝 (0.2, 1, 1), # 青蓝 (0.4, 1, 0.6), # 青绿 (0.6, 1, 0.2), # 黄绿 (1, 1, 0), # 黄色 (1, 0.7, 0), # 橙色 (0.8, 0.4, 0.2), # 棕色 (0.7, 0.2, 0.5), # 紫红 (1, 0, 0) # 红色 ] def read_csv(self, file_path): """ 读取CSV文件并解析数据 参数: file_path: CSV文件路径 返回: metadata: 元数据字典 data: 温度数据矩阵 """ with open(file_path, 'r', encoding='utf-8') as f: lines = f.readlines() metadata = {} start_index = 0 for i, line in enumerate(lines): line = line.strip() if line.startswith("#"): # 解析元数据 if "宽度" in line: metadata["width"] = int(line.split(":")[1].strip()) elif "高度" in line: metadata["height"] = int(line.split(":")[1].strip()) elif "最小温度" in line: metadata["min_temp"] = float(line.split(":")[1].strip().replace("°C", "")) elif "最大温度" in line: metadata["max_temp"] = float(line.split(":")[1].strip().replace("°C", "")) elif "中心温度" in line: metadata["center_temp"] = float(line.split(":")[1].strip().replace("°C", "")) else: start_index = i break # 找到数据部分 # 读取表格数据 df = pd.read_csv(file_path, skiprows=start_index) # 转换为numpy数组 data = df.to_numpy(dtype=np.float32) return metadata, data def enhance_continuity(self, data): """ 通过形态学操作增强区域连续性 参数: data: 热数据矩阵 返回: result: 增强后的热数据矩阵 """ # 先对数据进行中值滤波减少噪点 data_filtered = ndimage.median_filter(data, size=3) # 归一化处理 norm_data = cv2.normalize(data_filtered, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8) # 使用自适应阈值以获得更好的边缘细节 mask = cv2.adaptiveThreshold(norm_data, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2) # 使用核进行闭操作，更好地连接邻近区域 kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (7,7)) closed = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=3) # 增加开操作，消除小噪点 opened = cv2.morphologyEx(closed, cv2.MORPH_OPEN, kernel, iterations=1) # 保留原数据的连续性，使用形态学梯度增强边缘 result = cv2.bitwise_and(data, data, mask=opened) # 应用高斯滤波以平滑过渡 return cv2.GaussianBlur(result, (5,5), 0) def generate_temp_ranges(self, min_temp, max_temp, focus_temp): """ 生成温度区间 参数: min_temp: 最小温度值 max_temp: 最大温度值 focus_temp: 集中温度值，在该温度附近的区间将更精细 返回: temp_ranges: 温度区间列表 """ # 创建一个函数来计算到焦点的距离与区间大小的关系 def interval_size(distance): """根据到焦点的距离计算区间大小""" # 基准区间大小（最小值，在焦点处） base_size = 0.5 # 增长率 growth_rate = 3.0 # 计算区间大小 size = base_size * (1 + growth_rate * (abs(distance) ** 1.5)) # 限制最大区间大小 return min(size, (max_temp - min_temp) / 5) # 从焦点开始，向两边生成温度点 ranges = [focus_temp] # 向上生成温度点，直到超过最大温度 current = focus_temp while current < max_temp: distance = (current - focus_temp) / (max_temp - min_temp) step = interval_size(distance) current += step if current < max_temp: ranges.append(current) # 向下生成温度点，直到低于最小温度 current = focus_temp while current > min_temp: distance = (current - focus_temp) / (max_temp - min_temp) step = interval_size(distance) current -= step if current > min_temp: ranges.append(current) # 确保包含最小值和最大值 if min_temp not in ranges: ranges.append(min_temp) if max_temp not in ranges: ranges.append(max_temp) # 对温度点排序并去重 ranges = sorted(list(set(ranges))) # 需要的温度点数量（颜色数量-1，因为颜色数比区间数少1） required_points = 10 # 11个颜色需要10个温度点（不包括无穷值） # 确保生成的区间数量正确 while len(ranges) > required_points: # 如果温度点太多，移除距离焦点最远的点，但保留最小值和最大值 distances = [] for i, r in enumerate(ranges): if r == min_temp or r == max_temp: distances.append(float('-inf')) # 确保不会删除最小和最大值 else: distances.append(abs(r - focus_temp)) furthest_idx = distances.index(max(distances)) ranges.pop(furthest_idx) # 如果温度点太少，在最大间隔处插入新的点 while len(ranges) < required_points: intervals = [ranges[i+1] - ranges[i] for i in range(len(ranges)-1)] largest_interval_idx = intervals.index(max(intervals)) mid_point = (ranges[largest_interval_idx] + ranges[largest_interval_idx+1]) / 2 ranges.insert(largest_interval_idx + 1, mid_point) # 添加无穷区间 ranges = [-np.inf] + ranges + [np.inf] return ranges def create_heatmap(self, data, min_temp=None, max_temp=None, focus_temp=None, colors=None, scale_factor=6): """ 根据温度数据创建热图 参数: data: 温度数据矩阵 min_temp: 最小温度值，若为None则使用数据中的最小值 max_temp: 最大温度值，若为None则使用数据中的最大值 focus_temp: 集中温度值，若为None则使用max_temp的值 colors: 自定义颜色映射，若为None则使用默认颜色 scale_factor: 图像缩放倍数，默认为6 返回: heatmap: 生成的热图图像 """ # 确保数据为numpy数组 data = np.array(data, dtype=np.float32) # 对原始数据应用高斯平滑，减少离散温度值的跳变 data = cv2.GaussianBlur(data, (3,3), 0.8) # 增强数据连续性 data = self.enhance_continuity(data) # 如果未指定温度范围，则使用数据的实际范围 if min_temp is None: min_temp = np.min(data) if max_temp is None: max_temp = np.max(data) if focus_temp is None: focus_temp = max_temp # 使用自定义颜色或默认颜色 if colors is None: colors = self.default_colors # 根据参数动态生成温度区间 temp_ranges = self.generate_temp_ranges(min_temp, max_temp, focus_temp) # 实现平滑过渡 interpolated_colors = np.zeros((data.shape[0], data.shape[1], 3), dtype=np.float32) # 对每个像素计算相对距离，用于颜色插值 for i in range(len(temp_ranges) - 1): if i == 0 or i == len(temp_ranges) - 2: # 边界区域直接使用对应颜色 mask = (data > temp_ranges[i]) & (data <= temp_ranges[i+1]) if np.any(mask): interpolated_colors[mask] = colors[i] continue lower_bound = temp_ranges[i] upper_bound = temp_ranges[i+1] # 找出在当前区间内的所有像素 mask = (data > lower_bound) & (data <= upper_bound) if not np.any(mask): continue # 计算相对位置并插值 pixels_in_range = data[mask] relative_pos = (pixels_in_range - lower_bound) / (upper_bound - lower_bound) # 在两个颜色之间进行线性插值 for c in range(3): # RGB通道 color_values = colors[i][c] * (1 - relative_pos) + colors[i+1][c] * relative_pos interpolated_colors[mask, c] = color_values # 将插值后的颜色转换为字节类型 heatmap = (interpolated_colors * 255).astype(np.uint8) # 将RGB转换为BGR（OpenCV使用BGR格式） heatmap = cv2.cvtColor(heatmap, cv2.COLOR_RGB2BGR) # 图像放大和增强 original_height, original_width = heatmap.shape[:2] heatmap = cv2.resize(heatmap, (original_width * scale_factor, original_height * scale_factor), interpolation=cv2.INTER_LANCZOS4) # 应用滤波 heatmap = cv2.GaussianBlur(heatmap, (9, 9), 2.0) heatmap = cv2.bilateralFilter(heatmap, 11, 85, 85) heatmap = cv2.bilateralFilter(heatmap, 9, 60, 60) # 对比度增强 heatmap = cv2.convertScaleAbs(heatmap, alpha=1.08, beta=6) # 提高饱和度 hsv = cv2.cvtColor(heatmap, cv2.COLOR_BGR2HSV) hsv[:,:,1] = np.clip(hsv[:,:,1] * 1.1, 0, 255).astype(np.uint8) heatmap = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR) # 最终旋转 return cv2.rotate(heatmap, cv2.ROTATE_180) def process_csv(self, file_path, min_temp=None, max_temp=None, focus_temp=None, colors=None, scale_factor=6): """ 处理CSV文件并生成热图 参数: file_path: CSV文件路径 min_temp: 最小温度值，若为None则使用数据中的最小值 max_temp: 最大温度值，若为None则使用数据中的最大值 focus_temp: 集中温度值，若为None则使用max_temp的值 colors: 自定义颜色映射，若为None则使用默认颜色 scale_factor: 图像缩放倍数，默认为6 返回: heatmap: 生成的热图图像 metadata: 元数据字典 """ # 读取CSV数据 metadata, data = self.read_csv(file_path) # 使用元数据中的温度范围（如果未指定） if min_temp is None and "min_temp" in metadata: min_temp = metadata["min_temp"] if max_temp is None and "max_temp" in metadata: max_temp = metadata["max_temp"] if focus_temp is None and "center_temp" in metadata: focus_temp = metadata["center_temp"] # 如果仍然没有温度范围，使用数据中的实际范围 if min_temp is None: min_temp = np.min(data) if max_temp is None: max_temp = np.max(data) if focus_temp is None: focus_temp = max_temp # 生成热图 heatmap = self.create_heatmap( data, min_temp=min_temp, max_temp=max_temp, focus_temp=focus_temp, colors=colors, scale_factor=scale_factor ) return heatmap, metadata def save_image(self, image, file_path): """ 保存图像到文件 参数: image: 图像 file_path: 输出文件路径 返回: success: 是否保存成功 """ return cv2.imwrite(file_path, image) # 基础使用示例 if __name__ == "__main__": # 创建处理器实例 processor = ThermalProcessor() # 处理CSV文件 csv_path = "thermal_data_20250318_161047.csv" # 替换为你的CSV文件 heatmap, metadata = processor.process_csv(csv_path) # 显示元数据 print("文件元数据:") for key, value in metadata.items(): print(f" {key}: {value}") # 保存热图 output_path = "output_heatmap.png" processor.save_image(heatmap, output_path) print(f"热图已保存为: {output_path}")