326 lines
9.6 KiB
Python
326 lines
9.6 KiB
Python
"""CCT / 色度一致性绘制。
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Step 2 重构：从 pqAutomationApp.PQAutomationApp.plot_cct 原样搬迁。
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"""
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import numpy as np
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def plot_cct(app, test_type):
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"""绘制 x 和 y 坐标分离图 - 每个点标注纵坐标值"""
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self = app
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self.cct_fig.clear()
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gray_data = self.results.get_intermediate_data("shared", "gray")
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if not gray_data:
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gray_data = self.results.get_intermediate_data("cct", "gray")
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if not gray_data or len(gray_data) < 2:
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self.log_gui.log("⚠️ 无 xy 数据可用")
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ax = self.cct_fig.add_subplot(111)
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ax.text(
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0.5,
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0.5,
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"无可用数据",
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ha="center",
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va="center",
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fontsize=14,
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color="red",
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)
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ax.axis("off")
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self.cct_canvas.draw()
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return
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x_measured = [data[0] for data in gray_data]
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y_measured = [data[1] for data in gray_data]
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# 反转数据顺序（从暗到亮）
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x_measured = x_measured[::-1]
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y_measured = y_measured[::-1]
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# 去掉第一个点
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x_measured = x_measured[1:]
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y_measured = y_measured[1:]
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# 重新生成灰阶坐标
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total_points = len(gray_data)
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grayscale = np.linspace(100 / total_points, 100, len(x_measured))
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self.log_gui.log(f"✓ 已移除第一个数据点，当前数据点数: {len(x_measured)}")
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self.log_gui.log(f" x范围: {min(x_measured):.6f} - {max(x_measured):.6f}")
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self.log_gui.log(f" y范围: {min(y_measured):.6f} - {max(y_measured):.6f}")
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# ========== 根据测试类型读取对应参数 ==========
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if test_type == "sdr_movie":
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try:
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x_ideal = float(self.sdr_cct_x_ideal_var.get())
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x_tolerance = float(self.sdr_cct_x_tolerance_var.get())
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y_ideal = float(self.sdr_cct_y_ideal_var.get())
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y_tolerance = float(self.sdr_cct_y_tolerance_var.get())
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self.log_gui.log("✓ 使用 SDR 色度参数")
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except:
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x_ideal = 0.3127
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x_tolerance = 0.003
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y_ideal = 0.3290
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y_tolerance = 0.003
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self.log_gui.log("⚠️ SDR 参数读取失败，使用默认值")
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elif test_type == "hdr_movie":
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try:
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x_ideal = float(self.hdr_cct_x_ideal_var.get())
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x_tolerance = float(self.hdr_cct_x_tolerance_var.get())
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y_ideal = float(self.hdr_cct_y_ideal_var.get())
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y_tolerance = float(self.hdr_cct_y_tolerance_var.get())
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self.log_gui.log("✓ 使用 HDR 色度参数")
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except:
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x_ideal = 0.3127
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x_tolerance = 0.003
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y_ideal = 0.3290
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y_tolerance = 0.003
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self.log_gui.log("⚠️ HDR 参数读取失败，使用默认值")
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else: # screen_module
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try:
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x_ideal = float(self.cct_x_ideal_var.get())
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x_tolerance = float(self.cct_x_tolerance_var.get())
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y_ideal = float(self.cct_y_ideal_var.get())
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y_tolerance = float(self.cct_y_tolerance_var.get())
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self.log_gui.log("✓ 使用屏模组色度参数")
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except:
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x_ideal = 0.306
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x_tolerance = 0.003
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y_ideal = 0.318
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y_tolerance = 0.003
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self.log_gui.log("⚠️ 屏模组参数读取失败，使用默认值")
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x_low = x_ideal - x_tolerance
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x_high = x_ideal + x_tolerance
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y_low = y_ideal - y_tolerance
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y_high = y_ideal + y_tolerance
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self.log_gui.log(f"✓ 用户设置参数:")
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self.log_gui.log(f" x-ideal={x_ideal:.4f}, tolerance={x_tolerance:.4f}")
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self.log_gui.log(f" x范围: [{x_low:.4f}, {x_high:.4f}]")
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self.log_gui.log(f" y-ideal={y_ideal:.4f}, tolerance={y_tolerance:.4f}")
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self.log_gui.log(f" y范围: [{y_low:.4f}, {y_high:.4f}]")
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# 为所有测试类型创建子图
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ax1 = self.cct_fig.add_subplot(211)
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ax2 = self.cct_fig.add_subplot(212)
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# ========== 上图：x coordinates ==========
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ax1.plot(
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grayscale,
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x_measured,
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"b-o",
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label="屏本体",
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linewidth=2,
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markersize=4,
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zorder=5,
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)
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# 为每个点添加数值标注（x 坐标）
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for i, (gs, x_val) in enumerate(zip(grayscale, x_measured)):
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ax1.annotate(
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f"{x_val:.5f}",
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xy=(gs, x_val),
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xytext=(0, 8),
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textcoords="offset points",
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ha="center",
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va="bottom",
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fontsize=7,
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color="blue",
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bbox=dict(
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boxstyle="round,pad=0.2",
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facecolor="white",
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edgecolor="blue",
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alpha=0.8,
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linewidth=0.5,
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),
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)
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# 绘制完整的参考线
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full_grayscale = np.linspace(0, 100, 100)
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ax1.axhline(
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y=x_ideal,
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color="green",
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linestyle="--",
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linewidth=1.5,
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label=f"x-ideal ({x_ideal:.4f})",
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zorder=3,
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)
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ax1.axhline(
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y=x_low,
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color="red",
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linestyle=":",
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linewidth=1,
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alpha=0.7,
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label=f"x-low ({x_low:.4f})",
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zorder=2,
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)
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ax1.axhline(
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y=x_high,
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color="red",
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linestyle=":",
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linewidth=1,
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alpha=0.7,
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label=f"x-high ({x_high:.4f})",
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zorder=2,
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)
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ax1.fill_between(
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full_grayscale, x_low, x_high, alpha=0.15, color="blue", zorder=1
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)
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ax1.set_xlabel("灰阶 (%)", fontsize=9)
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ax1.set_ylabel("CIE x", fontsize=9)
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ax1.grid(True, linestyle="--", alpha=0.3)
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ax1.tick_params(labelsize=8)
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ax1.set_xlim(0, 105)
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# 纵坐标范围由用户参数控制
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x_min_data = min(x_measured)
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x_max_data = max(x_measured)
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data_range_x = x_max_data - x_min_data
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self.log_gui.log(f" x数据波动: {data_range_x:.6f}")
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range_span = x_tolerance * 2
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margin_ratio = 0.20
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extra_margin = range_span * margin_ratio
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final_y_min = min(x_min_data, x_low) - extra_margin
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final_y_max = max(x_max_data, x_high) + extra_margin
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if x_min_data >= x_low and x_max_data <= x_high:
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self.log_gui.log(f" x数据在tolerance范围内，使用tolerance范围显示")
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final_y_min = x_low - extra_margin
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final_y_max = x_high + extra_margin
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else:
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self.log_gui.log(f" x数据超出tolerance范围，扩展显示范围")
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ax1.set_ylim(final_y_min, final_y_max)
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self.log_gui.log(
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f" x轴显示范围: {final_y_min:.6f} - {final_y_max:.6f} (跨度: {final_y_max - final_y_min:.6f})"
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)
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# ========== 下图：y coordinates ==========
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ax2.plot(
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grayscale,
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y_measured,
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"r-o",
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label="屏本体",
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linewidth=2,
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markersize=4,
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zorder=5,
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)
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# 为每个点添加数值标注（y 坐标）
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for i, (gs, y_val) in enumerate(zip(grayscale, y_measured)):
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ax2.annotate(
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f"{y_val:.5f}",
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xy=(gs, y_val),
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xytext=(0, 8),
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textcoords="offset points",
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ha="center",
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va="bottom",
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fontsize=7,
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color="red",
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bbox=dict(
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boxstyle="round,pad=0.2",
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facecolor="white",
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edgecolor="red",
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alpha=0.8,
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linewidth=0.5,
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),
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)
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ax2.axhline(
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y=y_ideal,
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color="green",
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linestyle="--",
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linewidth=1.5,
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label=f"y-ideal ({y_ideal:.4f})",
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zorder=3,
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)
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ax2.axhline(
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y=y_low,
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color="orange",
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linestyle=":",
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linewidth=1,
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alpha=0.7,
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label=f"y-low ({y_low:.4f})",
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zorder=2,
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)
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ax2.axhline(
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y=y_high,
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color="orange",
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linestyle=":",
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linewidth=1,
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alpha=0.7,
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label=f"y-high ({y_high:.4f})",
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zorder=2,
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)
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ax2.fill_between(
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full_grayscale, y_low, y_high, alpha=0.15, color="orange", zorder=1
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)
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ax2.set_xlabel("灰阶 (%)", fontsize=9)
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ax2.set_ylabel("CIE y", fontsize=9)
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ax2.grid(True, linestyle="--", alpha=0.3)
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ax2.tick_params(labelsize=8)
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ax2.set_xlim(0, 105)
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# 纵坐标范围由用户参数控制
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y_min_data = min(y_measured)
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y_max_data = max(y_measured)
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data_range_y = y_max_data - y_min_data
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self.log_gui.log(f" y数据波动: {data_range_y:.6f}")
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range_span = y_tolerance * 2
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extra_margin = range_span * margin_ratio
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final_y_min = min(y_min_data, y_low) - extra_margin
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final_y_max = max(y_max_data, y_high) + extra_margin
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if y_min_data >= y_low and y_max_data <= y_high:
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self.log_gui.log(f" y数据在tolerance范围内，使用tolerance范围显示")
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final_y_min = y_low - extra_margin
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final_y_max = y_high + extra_margin
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else:
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self.log_gui.log(f" y数据超出tolerance范围，扩展显示范围")
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ax2.set_ylim(final_y_min, final_y_max)
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self.log_gui.log(
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f" y轴显示范围: {final_y_min:.6f} - {final_y_max:.6f} (跨度: {final_y_max - final_y_min:.6f})"
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)
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# ========== 总标题 - 统一格式（去掉统计信息）==========
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test_type_name = self.get_test_type_name(test_type)
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self.cct_fig.suptitle(
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f"{test_type_name} - 色度一致性测试",
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fontsize=12,
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y=0.98,
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fontweight="bold",
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)
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self.cct_fig.subplots_adjust(
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left=0.12,
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right=0.82,
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top=0.92,
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bottom=0.08,
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hspace=0.30,
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)
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ax1.legend(
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fontsize=7, loc="center left", bbox_to_anchor=(1.05, 0.5), framealpha=1.0
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)
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ax2.legend(
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fontsize=7, loc="center left", bbox_to_anchor=(1.05, 0.5), framealpha=1.0
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)
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self.cct_canvas.draw()
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self.chart_notebook.select(2)
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self.log_gui.log("✓ xy 色度坐标图绘制完成")
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