Python get_image_as_arrayの例

コード例 #1

ファイル: data viewer.py プロジェクト: Guluna/Abnormality-Detection-in-Chest-X-rays

               13: ("Pulmonary fibrosis", "#e75480"),
               14: ("No finding", "#ffffff")
               }

# %% --------------------
# IMAGE DIR
img_dir = "D:/GWU/4 Spring 2021/6501 Capstone/VBD CXR/PyCharm " \
          "Workspace/vbd_cxr/9_data/512/transformed_data/train"

# %% --------------------
# ANNOTATION DIR
train_data = pd.read_csv(
    "D:/GWU/4 Spring 2021/6501 Capstone/VBD CXR/PyCharm "
    "Workspace/vbd_cxr/1_merger/wbf_merged/90_percent_train/object_detection/95_percent"
    "/80_percent/train_df_0.csv")

# %% --------------------
# image_ids = train_data["image_id"].unique()
image_ids = ["e1a4353d3e747a7150cb06cac73f4d6f"]
# shuffle is inplace operation
random.shuffle(image_ids)

for img in image_ids[:10]:
    img_array = get_image_as_array(f"{img_dir}/{img}.jpeg")

    # get bounding box info
    img_bb_info = get_bb_info(train_data, img, ['x_min', 'y_min', 'x_max', 'y_max', "class_id"])

    # plot image with bounding boxes
    bounding_box_plotter(img_array, img, img_bb_info, label2color, save_title_or_plot="plot")

コード例 #2

ファイル: Visualizer.py プロジェクト: Guluna/Abnormality-Detection-in-Chest-X-rays

gt = pd.read_csv(
    "D:/GWU/4 Spring 2021/6501 Capstone/VBD CXR/PyCharm Workspace/vbd_cxr/2_data_split/512/unmerged/10_percent_holdout/holdout_df.csv"
)

predictions = pd.read_csv(
    "D:/GWU/4 Spring 2021/6501 Capstone/VBD CXR/PyCharm Workspace/vbd_cxr/5_inference_on_holdout_10_percent/0_predictions/holdout_ensemble_classification_object_detection.csv"
)

# %% --------------------
label2color = get_label_2_color_dict()

# %% --------------------
original_image_ids = gt["image_id"].unique()

# %% --------------------
for image_id in original_image_ids[:10]:
    img_as_arr = get_image_as_array(
        f"D:/GWU/4 Spring 2021/6501 Capstone/VBD CXR/PyCharm Workspace/vbd_cxr/input_data/512x512/train/{image_id}.png"
    )

    # %% --------------------
    left = get_bb_info(gt, image_id,
                       ['x_min', 'y_min', 'x_max', 'y_max', "class_id"])
    right = get_bb_info(predictions, image_id,
                        ['x_min', 'y_min', 'x_max', 'y_max', "label"])

    # %% --------------------
    bounding_box_plotter_side_to_side(img_as_arr, image_id, left, right,
                                      "Ground Truth", "Predictions",
                                      label2color)

コード例 #3

# %% --------------------
gt = pd.read_csv(
    f"{BASE_DIR}/2_data_split/512/unmerged/10_percent_holdout/holdout_df.csv")

predictions = pd.read_csv(
    f"{BASE_DIR}/5_inference_on_holdout_10_percent/0_predictions/holdout_ensemble_classification_object_detection.csv"
)

# %% --------------------
label2color = get_label_2_color_dict()

# %% --------------------
original_image_ids = gt["image_id"].unique()

# %% --------------------
for image_id in original_image_ids[0:20]:
    img_as_arr = get_image_as_array(
        f"{BASE_DIR}/input_data/512x512/train/{image_id}.png")

    # %% --------------------
    left = get_bb_info(gt, image_id,
                       ['x_min', 'y_min', 'x_max', 'y_max', "class_id"])
    right = get_bb_info(predictions, image_id,
                        ['x_min', 'y_min', 'x_max', 'y_max', "label"])

    # %% --------------------
    bounding_box_plotter_side_to_side(img_as_arr, image_id, left, right,
                                      "Ground Truth", "Predictions",
                                      label2color)

コード例 #4

ファイル: manual rescaler toy.py プロジェクト: Guluna/Abnormality-Detection-in-Chest-X-rays

# %% --------------------
train_data = pd.read_csv("/transformed_data/transformed_train.csv")

# %% --------------------
train_data.head()

# %% --------------------
train_data.columns

# %% --------------------
# img = "9a5094b2563a1ef3ff50dc5c7ff71345"
img = "001d127bad87592efe45a5c7678f8b8d"

# %% --------------------
img_array = get_image_as_array(f"{train_dir_path}/{img}.jpeg")

# %% --------------------
# plot original image
plot_img(img_array, "Original")

# %% --------------------
# get bounding box info
img_bb_info = get_bb_info(train_data, img,
                          ['x_min', 'y_min', 'x_max', 'y_max', "class_id"])

# %% --------------------
# plot image with bounding boxes
bounding_box_plotter(img_array, img, img_bb_info, label2color)

# %% --------------------

コード例 #5

# %% --------------------
import matplotlib.pyplot as plt
import numpy as np
from skimage import exposure

from common.utilities import get_image_as_array

# %% --------------------

img = get_image_as_array(
    'D:/GWU/4 Spring 2021/6501 Capstone/VBD CXR/PyCharm '
    'Workspace/vbd_cxr/9_data/512/transformed_data/train/0c4a6bc602d1d207f217212c68a7131b.jpeg'
)
img = np.asarray(img)
plt.figure(figsize=(12, 12))
plt.imshow(img, 'gray')
plt.show()

# %% --------------------
img_hist = exposure.equalize_hist(img)
plt.figure(figsize=(12, 12))
plt.imshow(img_hist, 'gray')
plt.show()
# %% --------------------
img_clahe = exposure.equalize_adapthist(img / np.max(img))
plt.figure(figsize=(12, 12))
plt.imshow(img_clahe, 'gray')
plt.show()