def gradcam(model, img_orig, img_b, pred, conf, label_max):
img_encs = []
for lbl_index in range(label_max):
if pred[lbl_index] > conf:
cam = GradCAM(model, lbl_index)
heatmap = cam.compute_heatmap(np.array([img_b]))
heatmap = cv2.resize(heatmap,
(img_orig.shape[1], img_orig.shape[0]))
(heatmap, output) = cam.overlay_heatmap(heatmap,
img_orig,
alpha=0.5)
_, out_enc = cv2.imencode(".jpg", output)
out_enc = base64.b64encode(out_enc).decode('ascii')
img_encs.append(out_enc)
else:
img_encs.append(None)
return img_encs
preds = model.predict(image)
i = np.argmax(preds[0])
# decode the ImageNet predictions to obtain the human-readable label
decoded = imagenet_utils.decode_predictions(preds)
(imagenetID, label, prob) = decoded[0][0]
label = "{}: {:.2f}%".format(label, prob * 100)
print("[INFO] {}".format(label))
# initialize our gradient class activation map and build the heatmap
if args['layer'] == 'None':
cam = GradCAM(model, i)
else:
cam = GradCAM(model, i, args['layer'])
heatmap = cam.compute_heatmap(image)
# resize the resulting heatmap to the original input image dimensions
# and then overlay heatmap on top of the image
heatmap = cv2.resize(heatmap, (orig.shape[1], orig.shape[0]))
(heatmap, output) = cam.overlay_heatmap(heatmap, orig, alpha=0.5)
# draw the predicted label on the output image
cv2.rectangle(output, (0, 0), (340, 40), (0, 0, 0), -1)
cv2.putText(output, label, (10, 25), cv2.FONT_HERSHEY_SIMPLEX, 0.8,
(255, 255, 255), 2)
# display the original image and resulting heatmap and output image
# to our screen
output = np.hstack([orig, heatmap, output])
output = imutils.resize(output, height=400)
if FLAGS.show_model_summary:
visual_model.summary()
else:
visual_model = model_factory.get_model(FLAGS)
FLAGS.batch_size = 1
test_generator = get_generator(FLAGS.test_csv,FLAGS)
images_names = test_generator.get_images_names()
for batch_i in tqdm(range(test_generator.steps)):
batch, _ = test_generator.__getitem__(batch_i)
image_path = os.path.join(FLAGS.image_directory, images_names[batch_i])
original = cv2.imread(image_path)
preds = visual_model.predict(batch)
predicted_class = np.argmax(preds[0])
label = f"Birad-{predicted_class + 1}"
cam = GradCAM(visual_model, predicted_class)
heatmap = cam.compute_heatmap(batch)
heatmap = cv2.resize(heatmap, (original.shape[1], original.shape[0]))
(heatmap, output) = cam.overlay_heatmap(heatmap, original, alpha=0.5)
cv2.rectangle(output, (0, 0), (340, 40), (0, 0, 0), -1)
cv2.putText(output, label, (10, 25), cv2.FONT_HERSHEY_SIMPLEX,
0.8, (255, 255, 255), 2)
cv2.imwrite(os.path.join(write_path,images_names[batch_i]),output)
# use the network to make predictions on the input image and find
# the class label index with the largest corresponding probability
preds = model.predict(image)
i = np.argmax(preds[0])
class_names = ['Opencountry', 'coast', 'forest', 'highway', ',inside_city', 'mountain', 'street', 'tallbuilding']
label = class_names[i]
prob = np.max(preds[0])
label = "{}: {:.2f}%".format(label, prob * 100)
print("[INFO] {}".format(label))
# initialize our gradient class activation map and build the heatmap
cam = GradCAM(model, i)
heatmap = cam.compute_heatmap(image, normalize_grads=args["norm"])
# load the original image from disk (in OpenCV format)
orig = cv2.imread(args["image"])
# resize the resulting heatmap to the original input image dimensions
# and then overlay heatmap on top of the image
heatmap = cv2.resize(heatmap, (orig.shape[1], orig.shape[0]))
(heatmap, output) = cam.overlay_heatmap(heatmap, orig, alpha=0.5, colormap=color_maps[args["color"]])
# draw the predicted label on the output image
cv2.rectangle(output, (0, 0), (340, 40), (0, 0, 0), -1)
cv2.putText(output, label, (10, 25), cv2.FONT_HERSHEY_SIMPLEX,
0.8, (255, 255, 255), 2)
# display the original image and resulting heatmap and output image