Firsh Push at 20241207

draw_tools/Grad_cam.py

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+from Load_process.file_processing import Process_File
+from keras.models import Model
+from matplotlib import pyplot as plt
+import cv2
+import numpy as np
+from keras import backend as K
+from keras.preprocessing import image
+import tensorflow as tf
+import datetime
+
+class Grad_CAM:
+    def __init__(self, Label, One_Hot, Experiment_Name, Layer_Name) -> None:
+        self.experiment_name = Experiment_Name
+        self.Layer_Name = Layer_Name
+        self.Label = Label
+        self.One_Hot_Label = One_Hot
+        self.Save_File_Name = self.Convert_One_Hot_To_int()
+
+        pass
+
+    def process_main(self, model, index, images):
+        for i in range(len(images)):
+            array = np.expand_dims(images[i], axis=0) # 替圖片增加一個維度，代表他的數量
+            heatmap = self.gradcam(array, model)
+            self.plot_heatmap(heatmap, images[i], self.Save_File_Name[i], index, i)
+
+        pass
+
+    def Convert_One_Hot_To_int(self):
+        return [np.argmax(Label)for Label in self.One_Hot_Label]
+
+
+    def gradcam(self, Image, model, pred_index = None):
+        # 首先，我們創建了一個模型，將輸入圖像映射為最後一個卷積層的激活值以及輸出的預測結果。
+        grad_model = Model(
+            [model.inputs], [model.get_layer(self.Layer_Name).output, model.output]
+        )
+
+        # 然後，我們計算了對於輸入圖像而言預測類別的最高梯度，並相對於最後一個卷積層的激活值進行了計算。
+        with tf.GradientTape() as tape: # 創建一個梯度紀錄器，並做前向傳播
+            last_conv_layer_output, preds = grad_model(Image)
+            if pred_index is None:
+                pred_index = tf.argmax(preds[0])
+            class_channel = preds[:, pred_index]
+
+        # 這是輸出神經元（預測的頂部或所選的）對於最後一個卷積層的輸出特徵圖的梯度。
+        grads = tape.gradient(class_channel, last_conv_layer_output)
+
+        # 這是一個向量，其中每個項目是在特定特徵圖通道上梯度的平均強度。
+        pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
+
+        # 我們將特徵圖陣列中的每個通道乘以「這個通道相對於頂部預測類別的重要性」，然後將所有通道加總起來，以獲得熱圖類別激活。n
+        last_conv_layer_output = last_conv_layer_output[0]
+        heatmap = last_conv_layer_output @ pooled_grads[..., tf.newaxis]
+        heatmap = tf.squeeze(heatmap)
+
+        # For visualization purpose, we will also normalize the heatmap between 0 & 1
+        heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)
+        return heatmap.numpy()
+
+    def plot_heatmap(self, heatmap, img, Label, index, Title):
+        File = Process_File()
+
+        # ReLU
+        heatmap = np.maximum(heatmap, 0)
+        # 正規化
+        heatmap /= np.max(heatmap)
+        
+        # 讀取影像
+        # img = cv2.imread(img)
+        fig, ax = plt.subplots()
+        # im = cv2.resize(cv2.cvtColor(cv2.imread(img_path), cv2.COLOR_BGR2RGB), (img.shape[1], img.shape[0]))
+
+        # 拉伸 heatmap
+        img_path = cv2.resize(img, (512, 512))
+        heatmap = cv2.resize(heatmap, (512, 512))
+        heatmap = np.uint8(255 * heatmap)
+
+        img_path = cv2.cvtColor(img_path, cv2.COLOR_BGR2RGB)
+        
+        # 以 0.6 透明度繪製原始影像
+        ax.imshow(img_path, alpha=1)
+        # 以 0.4 透明度繪製熱力圖
+        ax.imshow(heatmap, cmap='jet', alpha=0.3)
+
+        save_root = '../Result/CNN_result_of_reading('+ str(datetime.date.today()) + " )/" + str(Label)
+        File.JudgeRoot_MakeDir(save_root)
+        save_root = File.Make_Save_Root(self.experiment_name + "-" + str(index) + "-" + str(Title) + ".png", save_root)
+        # 存plt檔
+        plt.savefig(save_root)
+        plt.close("all")      # 關閉圖表
+        pass