Update gradcam_utils.py

gradcam_utils.py

@@ -1,71 +1,144 @@
-import numpy as np
-import cv2
-import tensorflow as tf
-from tensorflow.keras.preprocessing.image import img_to_array, load_img
-import matplotlib.pyplot as plt
-from matplotlib.colors import LinearSegmentedColormap
-
-def preprocess_image(img_path, target_size):
-    img = load_img(img_path, target_size=target_size)
-    img = img_to_array(img)
-    img = np.expand_dims(img, axis=0)
-    img = img / 255.0  # Normalize
-    return img
-
-def make_gradcam_heatmap(model, img_tensor, last_conv_layer_name, classifier_layer_names):
-    grad_model = tf.keras.models.Model(
-        [model.inputs], [model.get_layer(last_conv_layer_name).output, model.output]
-    )
-    with tf.GradientTape() as tape:
-        conv_outputs, predictions = grad_model(img_tensor)
-        loss = predictions[:, 1]  # Targeting class 1 for pneumonia
-
-    grads = tape.gradient(loss, conv_outputs)
-    pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
-
-    conv_outputs = conv_outputs[0]
-    heatmap = conv_outputs @ pooled_grads[..., tf.newaxis]
-    heatmap = tf.squeeze(heatmap)
-    heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)
-    heatmap = tf.where(tf.math.is_nan(heatmap), tf.zeros_like(heatmap), heatmap)
-    return heatmap.numpy()
-
-def create_custom_colormap():
-    colors = ['blue', 'green', 'yellow', 'red']
-    n_bins = 256
-    cmap = LinearSegmentedColormap.from_list('custom', colors, N=n_bins)
-    return cmap
-
-def apply_custom_colormap(heatmap, cmap):
-    colored_heatmap = cmap(heatmap)
-    colored_heatmap = np.uint8(colored_heatmap * 255)
-    return colored_heatmap
-
-def enhance_heatmap(heatmap, gamma=0.7, percentile=99):
-    heatmap = np.power(heatmap, gamma)
-    heatmap = heatmap / np.percentile(heatmap, percentile)
-    heatmap = np.clip(heatmap, 0, 1)
-    return heatmap
-
-def generate_and_merge_heatmaps(img_path, vgg_model, efficientnet_model, densenet_model, img_size=(224, 224), output_size=(5, 5)):
-    img_tensor = preprocess_image(img_path, img_size)
-
-    vgg_heatmap = make_gradcam_heatmap(vgg_model, img_tensor, 'block5_conv4', ['flatten', 'dense'])
-    efficientnet_heatmap = make_gradcam_heatmap(efficientnet_model, img_tensor, 'top_conv', ['flatten', 'dense'])
-    densenet_heatmap = make_gradcam_heatmap(densenet_model, img_tensor, 'conv5_block16_concat', ['flatten', 'dense'])
-
-    vgg_heatmap_resized = cv2.resize(vgg_heatmap, img_size)
-    efficientnet_heatmap_resized = cv2.resize(efficientnet_heatmap, img_size)
-    densenet_heatmap_resized = cv2.resize(densenet_heatmap, img_size)
-
-    merged_heatmap = (vgg_heatmap_resized + efficientnet_heatmap_resized + densenet_heatmap_resized) / 3.0
-    enhanced_heatmap = enhance_heatmap(merged_heatmap)
-    custom_cmap = create_custom_colormap()
-    colored_heatmap = apply_custom_colormap(enhanced_heatmap, custom_cmap)
-
-    img = cv2.imread(img_path)
-    img = cv2.resize(img, img_size)
-    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-
-    superimposed_img = cv2.addWeighted(img, 0.6, colored_heatmap[:, :, :3], 0.4, 0)
-    return superimposed_img
+# import numpy as np
+# import cv2
+# import tensorflow as tf
+# from tensorflow.keras.preprocessing.image import img_to_array, load_img
+# import matplotlib.pyplot as plt
+# from matplotlib.colors import LinearSegmentedColormap
+
+# def preprocess_image(img_path, target_size):
+#     img = load_img(img_path, target_size=target_size)
+#     img = img_to_array(img)
+#     img = np.expand_dims(img, axis=0)
+#     img = img / 255.0  # Normalize
+#     return img
+
+# def make_gradcam_heatmap(model, img_tensor, last_conv_layer_name, classifier_layer_names):
+#     grad_model = tf.keras.models.Model(
+#         [model.inputs], [model.get_layer(last_conv_layer_name).output, model.output]
+#     )
+#     with tf.GradientTape() as tape:
+#         conv_outputs, predictions = grad_model(img_tensor)
+#         loss = predictions[:, 1]  # Targeting class 1 for pneumonia
+
+#     grads = tape.gradient(loss, conv_outputs)
+#     pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
+
+#     conv_outputs = conv_outputs[0]
+#     heatmap = conv_outputs @ pooled_grads[..., tf.newaxis]
+#     heatmap = tf.squeeze(heatmap)
+#     heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)
+#     heatmap = tf.where(tf.math.is_nan(heatmap), tf.zeros_like(heatmap), heatmap)
+#     return heatmap.numpy()
+
+# def create_custom_colormap():
+#     colors = ['blue', 'green', 'yellow', 'red']
+#     n_bins = 256
+#     cmap = LinearSegmentedColormap.from_list('custom', colors, N=n_bins)
+#     return cmap
+
+# def apply_custom_colormap(heatmap, cmap):
+#     colored_heatmap = cmap(heatmap)
+#     colored_heatmap = np.uint8(colored_heatmap * 255)
+#     return colored_heatmap
+
+# def enhance_heatmap(heatmap, gamma=0.7, percentile=99):
+#     heatmap = np.power(heatmap, gamma)
+#     heatmap = heatmap / np.percentile(heatmap, percentile)
+#     heatmap = np.clip(heatmap, 0, 1)
+#     return heatmap
+
+# def generate_and_merge_heatmaps(img_path, vgg_model, efficientnet_model, densenet_model, img_size=(224, 224), output_size=(5, 5)):
+#     img_tensor = preprocess_image(img_path, img_size)
+
+#     vgg_heatmap = make_gradcam_heatmap(vgg_model, img_tensor, 'block5_conv4', ['flatten', 'dense'])
+#     efficientnet_heatmap = make_gradcam_heatmap(efficientnet_model, img_tensor, 'top_conv', ['flatten', 'dense'])
+#     densenet_heatmap = make_gradcam_heatmap(densenet_model, img_tensor, 'conv5_block16_concat', ['flatten', 'dense'])
+
+#     vgg_heatmap_resized = cv2.resize(vgg_heatmap, img_size)
+#     efficientnet_heatmap_resized = cv2.resize(efficientnet_heatmap, img_size)
+#     densenet_heatmap_resized = cv2.resize(densenet_heatmap, img_size)
+
+#     merged_heatmap = (vgg_heatmap_resized + efficientnet_heatmap_resized + densenet_heatmap_resized) / 3.0
+#     enhanced_heatmap = enhance_heatmap(merged_heatmap)
+#     custom_cmap = create_custom_colormap()
+#     colored_heatmap = apply_custom_colormap(enhanced_heatmap, custom_cmap)
+
+#     img = cv2.imread(img_path)
+#     img = cv2.resize(img, img_size)
+#     img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+#     superimposed_img = cv2.addWeighted(img, 0.6, colored_heatmap[:, :, :3], 0.4, 0)
+#     return superimposed_img
+
+
+
+
+
+
+
+
+import numpy as np
+import cv2
+import tensorflow as tf
+from tensorflow.keras.preprocessing.image import img_to_array, load_img
+from matplotlib.colors import LinearSegmentedColormap
+
+def preprocess_image(img_path, target_size):
+    img = load_img(img_path, target_size=target_size)
+    img = img_to_array(img)
+    img = np.expand_dims(img, axis=0)
+    img = img / 255.0
+    return img
+
+def make_gradcam_heatmap(model, img_tensor):
+    grad_model = tf.keras.models.Model([model.input], [model.output])
+
+    with tf.GradientTape() as tape:
+        conv_outputs = model(img_tensor)
+        loss = conv_outputs[:, 1]  # class index 1 = pneumonia
+
+    grads = tape.gradient(loss, conv_outputs)
+    pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
+
+    conv_outputs = conv_outputs[0]
+    heatmap = conv_outputs @ pooled_grads[..., tf.newaxis]
+    heatmap = tf.squeeze(heatmap)
+    heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)
+    heatmap = tf.where(tf.math.is_nan(heatmap), tf.zeros_like(heatmap), heatmap)
+    return heatmap.numpy()
+
+def create_custom_colormap():
+    colors = ['blue', 'green', 'yellow', 'red']
+    cmap = LinearSegmentedColormap.from_list('custom', colors, N=256)
+    return cmap
+
+def apply_custom_colormap(heatmap, cmap):
+    colored_heatmap = cmap(heatmap)
+    return np.uint8(colored_heatmap * 255)
+
+def enhance_heatmap(heatmap, gamma=0.7, percentile=99):
+    heatmap = np.power(heatmap, gamma)
+    heatmap = heatmap / np.percentile(heatmap, percentile)
+    return np.clip(heatmap, 0, 1)
+
+def generate_and_merge_heatmaps(img_path, vgg_model, efficientnet_model, densenet_model, img_size=(224, 224)):
+    img_tensor = preprocess_image(img_path, img_size)
+
+    vgg_heatmap = make_gradcam_heatmap(vgg_model, img_tensor)
+    efficientnet_heatmap = make_gradcam_heatmap(efficientnet_model, img_tensor)
+    densenet_heatmap = make_gradcam_heatmap(densenet_model, img_tensor)
+
+    vgg_heatmap = cv2.resize(vgg_heatmap, img_size)
+    efficientnet_heatmap = cv2.resize(efficientnet_heatmap, img_size)
+    densenet_heatmap = cv2.resize(densenet_heatmap, img_size)
+
+    merged = (vgg_heatmap + efficientnet_heatmap + densenet_heatmap) / 3.0
+    enhanced = enhance_heatmap(merged)
+    colored = apply_custom_colormap(enhanced, create_custom_colormap())
+
+    original = cv2.imread(img_path)
+    original = cv2.resize(original, img_size)
+    original = cv2.cvtColor(original, cv2.COLOR_BGR2RGB)
+
+    superimposed_img = cv2.addWeighted(original, 0.6, colored[:, :, :3], 0.4, 0)
+    return superimposed_img