video rendering

00cfc9e3 · 朱婉杰 · 00cfc9e3 · 00cfc9e3 · 00cfc9e3 · 00cfc9e3
Commit 00cfc9e3 authored May 30, 2025 by 朱婉杰
30 changed files
--- a/color.py
+++ b/color.py
+import cv2
+import numpy as np
+
+def fit_ellipse_to_mask(mask, min_area=1000):
+    """对掩码进行椭圆拟合，返回拟合后的掩码"""
+    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    if not contours: return mask
+    largest_contour = max(contours, key=lambda c: cv2.contourArea(c))
+    if cv2.contourArea(largest_contour) < min_area: return mask
+    if len(largest_contour) >= 5:
+        ellipse = cv2.fitEllipse(largest_contour)
+        fitted_mask = np.zeros_like(mask)
+        cv2.ellipse(fitted_mask, ellipse, 255, -1)
+        return fitted_mask
+    else:
+        return mask
+
+def keep_center_region(mask, min_area=500):
+    """保留掩码中最靠近中心的区域"""
+    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    if not contours: return mask
+    h, w = mask.shape
+    img_center = (w//2, h//2)
+    best_contour = None
+    min_distance = float('inf')
+    for cnt in contours:
+        if cv2.contourArea(cnt) < min_area: continue
+        M = cv2.moments(cnt)
+        if M["m00"] == 0: continue
+        cx, cy = int(M["m10"]/M["m00"]), int(M["m01"]/M["m00"])
+        distance = np.sqrt((cx-img_center[0])**2 + (cy-img_center[1])**2)
+        if distance < min_distance:
+            min_distance = distance
+            best_contour = cnt
+    if best_contour is None: return mask
+    filtered_mask = np.zeros_like(mask)
+    cv2.drawContours(filtered_mask, [best_contour], -1, 255, -1)
+    return filtered_mask
+
+def process_frame_without_eggwhite(frame, 
+                                  yolk_thresh=130, 
+                                  germ_thresh=130,
+                                  brightness_weight=0.8,
+                                  alpha=0.5):
+    """无蛋清状态处理函数（恢复原始逻辑）"""
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    mask_background = (gray == 0).astype(np.uint8) * 255
+    
+    # 动态阈值调整
+    non_background_gray = gray[gray != 0]
+    if non_background_gray.size == 0:
+        avg_brightness = 0
+    else:
+        avg_brightness = np.mean(non_background_gray)
+    
+    brightness_ratio = avg_brightness / 128
+    adjusted_yolk_thresh = int(yolk_thresh * brightness_ratio * brightness_weight)
+    adjusted_germ_thresh = int(germ_thresh * brightness_ratio * brightness_weight)
+    
+    yolk_thresh = max(50, min(200, adjusted_yolk_thresh))
+    germ_thresh = max(80, min(250, adjusted_germ_thresh))
+    
+    # 掩码生成（无蛋清，仅蛋黄和胚盘）
+    mask_yolk = cv2.inRange(gray, 1, yolk_thresh)
+    mask_germ = cv2.threshold(gray, germ_thresh, 255, cv2.THRESH_BINARY)[1]
+    
+    # 形态学优化
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+    mask_yolk = cv2.morphologyEx(mask_yolk, cv2.MORPH_CLOSE, kernel)
+    mask_germ = cv2.morphologyEx(mask_germ, cv2.MORPH_CLOSE, kernel)
+    
+    # 椭圆拟合（仅蛋黄）
+    mask_yolk_fit = fit_ellipse_to_mask(mask_yolk, min_area=2000)
+    
+    # 胚盘处理
+    mask_germ_filtered = keep_center_region(mask_germ, min_area=500)
+    
+    # 创建彩色掩码（无蛋清，无红色区域）
+    color_mask = np.zeros_like(frame)
+    color_mask[mask_yolk_fit > 0] = (0, 255, 0)          # 绿色蛋黄
+    color_mask[mask_germ_filtered > 0] = (0, 255, 255)  # 黄色胚盘
+    color_mask[mask_background > 0] = (0, 0, 0)         # 黑色背景
+    
+    # 应用透明度
+    result = cv2.addWeighted(frame, 1 - alpha, color_mask, alpha, 0)
+    
+    return result
+
+def process_frame_with_eggwhite(frame, 
+                               egg_white_thresh=80, 
+                               yolk_thresh=130, 
+                               germ_thresh=130,
+                               alpha=0.5):
+    """有蛋清状态处理函数（保持原始逻辑）"""
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    mask_background = (gray == 0).astype(np.uint8) * 255
+    
+    # 固定阈值
+    mask_egg_white = cv2.inRange(gray, 1, egg_white_thresh)
+    mask_yolk = cv2.inRange(gray, egg_white_thresh + 1, yolk_thresh)
+    mask_germ = cv2.threshold(gray, germ_thresh, 255, cv2.THRESH_BINARY)[1]
+    
+    # 形态学优化
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+    mask_egg_white = cv2.morphologyEx(mask_egg_white, cv2.MORPH_CLOSE, kernel)
+    mask_yolk = cv2.morphologyEx(mask_yolk, cv2.MORPH_CLOSE, kernel)
+    mask_germ = cv2.morphologyEx(mask_germ, cv2.MORPH_CLOSE, kernel)
+    
+    # 椭圆拟合
+    mask_egg_white_fit = fit_ellipse_to_mask(mask_egg_white, min_area=5000)
+    mask_yolk_fit = fit_ellipse_to_mask(mask_yolk, min_area=2000)
+    
+    # 胚盘处理
+    mask_germ_filtered = keep_center_region(mask_germ, min_area=500)
+    
+    # 创建彩色掩码
+    color_mask = np.zeros_like(frame)
+    color_mask[mask_egg_white_fit > 0] = (0, 0, 255)  # 红色蛋清
+    color_mask[mask_yolk_fit > 0] = (0, 255, 0)      # 绿色蛋黄
+    color_mask[mask_germ_filtered > 0] = (0, 255, 255)  # 黄色胚盘
+    color_mask[mask_background > 0] = (0, 0, 0)      # 黑色背景
+    
+    # 应用透明度
+    result = cv2.addWeighted(frame, 1 - alpha, color_mask, alpha, 0)
+    
+    return result
+
+def process_frame(frame, 
+                  egg_white_thresh=65, 
+                  yolk_thresh=128, 
+                  germ_thresh=128,
+                  brightness_weight=0.8,
+                  alpha=0.3):
+    """主处理函数：根据蛋清状态选择不同处理路径"""
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    
+    # 仅使用原始固定阈值检测蛋清
+    mask_egg_white = cv2.inRange(gray, 1, egg_white_thresh)
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+    mask_egg_white = cv2.morphologyEx(mask_egg_white, cv2.MORPH_CLOSE, kernel)
+    
+    # 检测蛋清存在
+    egg_white_exists = np.sum(mask_egg_white) > 30000000  # 原始检测阈值
+    
+    # 根据状态选择处理函数
+    if egg_white_exists:
+        return process_frame_with_eggwhite(frame, egg_white_thresh, yolk_thresh, germ_thresh, alpha), True
+    else:
+        return process_frame_without_eggwhite(frame, yolk_thresh, germ_thresh, brightness_weight, alpha), False
+
+def main():
+    cap = cv2.VideoCapture("20250522-162702.mp4")
+    if not cap.isOpened():
+        print("Error: 无法打开视频文件")
+        return
+    
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    out = cv2.VideoWriter("output_colored.avi", cv2.VideoWriter_fourcc(*'XVID'), fps, (width, height))
+    
+    frame_count = 0
+    alpha = 0.2  # 设置固定透明度值
+    
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        
+        # 处理帧并获取蛋清状态
+        processed_frame, egg_white_exists = process_frame(frame, alpha=alpha)
+        
+        out.write(processed_frame)
+        cv2.imshow("Processed Frame", processed_frame)
+        
+        if cv2.waitKey(1) & 0xFF == 27:  # ESC退出
+            break
+        
+        frame_count += 1
+        if frame_count % 10 == 0:
+            print(f"已处理 {frame_count} 帧，状态: {'有蛋清' if egg_white_exists else '无蛋清'}")
+    
+    cap.release()
+    out.release()
+    cv2.destroyAllWindows()
+    print(f"处理完成，共处理 {frame_count} 帧")
+
+if __name__ == "__main__":
+    main()
--- a/color_manual.py
+++ b/color_manual.py
--- a/color_test.py
+++ b/color_test.py
+import cv2
+import numpy as np
+import os
+def fit_ellipse_to_mask(mask, min_area=1000):
+    """对掩码进行椭圆拟合，返回拟合后的掩码"""
+    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    if not contours: return mask
+    largest_contour = max(contours, key=lambda c: cv2.contourArea(c))
+    if cv2.contourArea(largest_contour) < min_area: return mask
+    if len(largest_contour) >= 5:
+        ellipse = cv2.fitEllipse(largest_contour)
+        fitted_mask = np.zeros_like(mask)
+        cv2.ellipse(fitted_mask, ellipse, 255, -1)
+        return fitted_mask
+    else:
+        return mask
+def contour_to_mask(contour, shape, fill_value=255):
+    """
+    将轮廓内所有点归为掩码
+    
+    参数:
+        contour: numpy数组，形状为(N, 1, 2)，表示轮廓点集
+        shape: 元组 (height, width)，指定输出掩码的大小
+        fill_value: 掩码填充值，默认为255（白色）
+        
+    返回:
+        numpy数组，二值掩码图像
+    """
+    # 创建一个全0的单通道图像（黑色背景）
+    mask = np.zeros(shape, dtype=np.uint8)
+    
+    # 在掩码上绘制轮廓并填充内部区域
+    cv2.drawContours(mask, [contour], -1, fill_value, -1)
+    
+    return mask
+def filter_small_regions(mask, min_area=1000):
+    """过滤掩码中面积小于指定阈值的区域"""
+    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    filtered_mask = np.zeros_like(mask)
+    
+    for contour in contours:
+        area = cv2.contourArea(contour)
+        if area >= min_area:
+            cv2.drawContours(filtered_mask, [contour], -1, 255, -1)
+    
+    return filtered_mask
+
+def keep_center_region(mask, min_area=1800):
+    """保留掩码中最靠近中心的区域"""
+    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    if not contours: return mask
+    h, w = mask.shape
+    img_center = (w//2, h//2)
+    best_contour = None
+    min_distance = float('inf')
+    for cnt in contours:
+        if cv2.contourArea(cnt) < min_area: continue
+        M = cv2.moments(cnt)
+        if M["m00"] == 0: continue
+        cx, cy = int(M["m10"]/M["m00"]), int(M["m01"]/M["m00"])
+        distance = np.sqrt((cx-img_center[0])**2 + (cy-img_center[1])**2)
+        if distance < min_distance:
+            min_distance = distance
+            best_contour = cnt
+    if best_contour is None: return mask
+    filtered_mask = np.zeros_like(mask)
+    cv2.drawContours(filtered_mask, [best_contour], -1, 255, -1)
+    return filtered_mask
+
+def detect_egg_region(frame, min_area=10000, max_area=100000, visualize=True):
+    """选择最大轮廓并进行椭圆拟合，将椭圆外像素置为30并可视化"""
+    # 图像预处理（保持原有逻辑不变）
+    lab = cv2.cvtColor(frame, cv2.COLOR_BGR2LAB)
+    l, a, b = cv2.split(lab)
+    clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
+    cl = clahe.apply(l)
+    limg = cv2.merge((cl,a,b))
+    enhanced_frame = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR)
+    
+    gray = cv2.cvtColor(enhanced_frame, cv2.COLOR_BGR2GRAY)
+    thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
+                                 cv2.THRESH_BINARY_INV, 11, 2)
+    blur = cv2.GaussianBlur(thresh, (5, 5), 0)
+    edges = cv2.Canny(blur, 20, 80)
+    
+    # 形态学操作闭合边缘
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (7, 7))
+    closed_edges = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, kernel)
+    
+    # 寻找轮廓并筛选最大轮廓
+    contours, _ = cv2.findContours(closed_edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    if not contours:
+        return np.full_like(frame, 37), None, edges  # 返回全30图像
+    
+    egg_contour = max(contours, key=lambda c: cv2.contourArea(c))
+    area = cv2.contourArea(egg_contour)
+    
+    if not (min_area < area < max_area):
+        return np.full_like(frame, 37), None, edges  # 返回全30图像
+    
+    # ------------------- 新增椭圆拟合逻辑 -------------------
+    # 对最大轮廓进行椭圆拟合（需至少5个顶点）
+    if len(egg_contour) >= 5:
+        ellipse = cv2.fitEllipse(egg_contour)  # 拟合椭圆
+        # 创建椭圆掩码
+        egg_mask = np.zeros_like(gray)
+        cv2.ellipse(egg_mask, ellipse, 255, -1)  # -1 表示填充椭圆内部
+    else:
+        # 轮廓顶点不足时，使用原轮廓创建掩码
+        egg_mask = np.zeros_like(gray)
+        cv2.drawContours(egg_mask, [egg_contour], -1, 255, -1)
+    
+    # 形态学闭操作优化掩码（可选，根据需要调整核大小）
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (15, 15))
+    egg_mask = cv2.morphologyEx(egg_mask, cv2.MORPH_CLOSE, kernel)
+    # ------------------------------------------------------
+    
+    # 将椭圆外的像素置为30
+    masked_frame = frame.copy()
+    masked_frame[egg_mask == 0] = 37
+    
+    # 可视化：显示椭圆拟合结果
+    if visualize:
+        cv2.imshow("Egg Ellipse Mask", egg_mask)  # 显示椭圆掩码
+        cv2.imshow("Masked Frame with Ellipse", masked_frame)
+    
+    return masked_frame
+
+def process_frame(frame, 
+                  background_thresh=40,  
+                  egg_white_thresh=120,   
+                  yolk_thresh=200,      
+                  germ_thresh=180,      
+                  alpha=0.2,
+                  visualize=True):  # 添加visualize参数
+    """使用固定阈值处理有蛋清的图像"""
+    # 转换为灰度图
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    
+    # 预处理：高斯模糊减少噪点
+    gray = cv2.GaussianBlur(gray, (5, 5), 0)
+    
+    # 增强对比度
+    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
+    gray = clahe.apply(gray)
+    
+    # 创建背景掩码（最暗区域）
+    mask_background = cv2.threshold(gray, background_thresh, 255, cv2.THRESH_BINARY_INV)[1]
+    
+    # 固定阈值分割（严格按照：背景 < 蛋清 < 蛋黄 < 胚盘）
+    mask_egg_white = cv2.inRange(gray, background_thresh + 1, egg_white_thresh)
+    mask_yolk = cv2.inRange(gray, egg_white_thresh + 1, yolk_thresh)
+    mask_germ = cv2.threshold(gray, germ_thresh, 255, cv2.THRESH_BINARY)[1]
+    mask_germ=filter_small_regions(mask_germ)
+    # 形态学优化
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (7, 7))
+    mask_egg_white = cv2.morphologyEx(mask_egg_white, cv2.MORPH_CLOSE, kernel, iterations=2)
+    mask_yolk = cv2.morphologyEx(mask_yolk, cv2.MORPH_CLOSE, kernel)
+    #mask_germ = cv2.morphologyEx(mask_germ, cv2.MORPH_CLOSE, kernel)
+    cv2.imshow("mask_germ", mask_germ)
+    # 确保掩码之间没有重叠区域
+    mask_yolk = cv2.bitwise_and(mask_yolk, cv2.bitwise_not(mask_egg_white))
+    mask_yolk = cv2.bitwise_and(mask_yolk, cv2.bitwise_not(mask_germ))
+    #mask_germ = cv2.bitwise_and(mask_germ, cv2.bitwise_not(mask_yolk))
+    
+    #mask_germ = cv2.bitwise_and(mask_germ, cv2.bitwise_not(mask_egg_white))
+    
+    # 新增：只保留蛋清中心区域
+    #mask_egg_white_center = keep_center_region(mask_egg_white, min_area=5000)
+    mask_egg_white_center = mask_egg_white
+    # 椭圆拟合（使用处理后的蛋清掩码）
+    mask_egg_white_fit = fit_ellipse_to_mask(mask_egg_white_center, min_area=10000)  # 增大最小面积
+    mask_yolk_fit = fit_ellipse_to_mask(mask_yolk, min_area=4000)
+    # 胚盘处理
+    #mask_germ_filtered = keep_center_region(mask_germ, min_area=300)
+    mask_germ_filtered =mask_germ
+    
+    # 创建彩色掩码
+    color_mask = np.zeros_like(frame)
+    color_mask[mask_egg_white_fit > 0] = (0, 0, 255)  # 红色蛋清
+    color_mask[mask_yolk_fit > 0] = (0, 255, 0)      # 绿色蛋黄
+    color_mask[mask_germ_filtered > 0] = (0, 255, 255)  # 黄色胚盘
+    
+    # 确保背景区域完全为黑色（最后应用背景掩码）
+    color_mask[mask_background > 0] = (0, 0, 0)
+    
+    # 应用透明度
+    result = cv2.addWeighted(frame, 1 - alpha, color_mask, alpha, 0)
+    
+    # 可视化
+    if visualize:
+        cv2.imshow("Processed Frame", result)
+    
+    return result
+
+def main():
+    input_path = "no\W-7.gif"
+    cap = cv2.VideoCapture(input_path)
+    
+    if not cap.isOpened():
+        print(f"Error: 无法打开文件 {input_path}")
+        return
+    
+    # 获取原始文件名和扩展名
+    base_name = os.path.basename(input_path)
+    file_name, file_ext = os.path.splitext(base_name)
+    
+    # 构建输出文件名
+    output_name = f"processed_{file_name}.mp4"
+    
+    fps = cap.get(cv2.CAP_PROP_FPS) or 10
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    
+    out = cv2.VideoWriter(output_name, cv2.VideoWriter_fourcc(*'mp4v'), fps, (width, height))
+    
+    frame_count = 0
+    
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        cv2.imshow("Original Frame", frame)
+        new_frame = detect_egg_region(frame, min_area=10000, max_area=100000, visualize=True)
+        
+        processed_frame = process_frame(new_frame, 
+                                        background_thresh=40,
+                                        egg_white_thresh=120,
+                                        yolk_thresh=200,
+                                        germ_thresh=190,
+                                        alpha=0.2,
+                                        visualize=True)
+        
+        out.write(processed_frame)
+        
+        if cv2.waitKey(1) & 0xFF == 27:
+            break
+        
+        frame_count += 1
+        if frame_count % 10 == 0:
+            print(f"已处理 {frame_count} 帧")
+    
+    cap.release()
+    out.release()
+    cv2.destroyAllWindows()
+    print(f"处理完成，输出文件: {output_name}")
+
+if __name__ == "__main__":
+    main()
--- a/gif/W-2_x.gif
+++ b/gif/W-2_x.gif
--- a/gif/W-2_z.gif
+++ b/gif/W-2_z.gif
--- a/gif/W-3_x.gif
+++ b/gif/W-3_x.gif
--- a/gif/W-3_z.gif
+++ b/gif/W-3_z.gif
--- a/gif/W-4_x.gif
+++ b/gif/W-4_x.gif
--- a/gif/W-4_z.gif
+++ b/gif/W-4_z.gif
--- a/gif/W-5_x.gif
+++ b/gif/W-5_x.gif
--- a/gif/W-5_z.gif
+++ b/gif/W-5_z.gif
--- a/gif/W-7.gif
+++ b/gif/W-7.gif
--- a/gif/Y-2-横.gif
+++ b/gif/Y-2-横.gif
--- a/gif/Y-2-竖.gif
+++ b/gif/Y-2-竖.gif
--- a/gif/Y-4-z.gif
+++ b/gif/Y-4-z.gif
--- a/gif/Y-4.gif
+++ b/gif/Y-4.gif
--- a/gif/Y-6.gif
+++ b/gif/Y-6.gif
--- a/gif/Y-6——z.gif
+++ b/gif/Y-6——z.gif
--- a/无精/processed_W-2_横.mp4
+++ b/无精/processed_W-2_横.mp4
--- a/无精/processed_W-2_竖.mp4
+++ b/无精/processed_W-2_竖.mp4
--- a/无精/processed_W-3_横.mp4
+++ b/无精/processed_W-3_横.mp4
--- a/无精/processed_W-3_竖.mp4
+++ b/无精/processed_W-3_竖.mp4
--- a/无精/processed_W-5_横.mp4
+++ b/无精/processed_W-5_横.mp4
--- a/无精/processed_W-5_竖.mp4
+++ b/无精/processed_W-5_竖.mp4
--- a/有精/processed_Y-2-横.mp4
+++ b/有精/processed_Y-2-横.mp4
--- a/有精/processed_Y-2-竖.mp4
+++ b/有精/processed_Y-2-竖.mp4
--- a/有精/processed_Y-4-横.mp4
+++ b/有精/processed_Y-4-横.mp4
--- a/有精/processed_Y-4-竖.mp4
+++ b/有精/processed_Y-4-竖.mp4
--- a/有精/processed_Y-6-横.mp4
+++ b/有精/processed_Y-6-横.mp4
--- a/有精/processed_Y-6-竖.mp4
+++ b/有精/processed_Y-6-竖.mp4