STRUCTURE

• 1. Set up

• 2. Use case 1: One Image

• 3. Use case 2: Batch of Images

A. SET UP

#ADD NEURALDE MODULES TO PYTHON PATH
import sys
import time
sys.path.append("..")

#IMPORT NEURALDE TRANSFORMATION CLASS
from neural_de.transformations.centered_zoom import CenteredZoom

#IMPORT UTILITY CLASSES
import matplotlib.pyplot as plt
from pathlib import Path
import cv2
import os

#SET IMAGE DATA DIR
start=time.time()
ROOT_DIR = "../examples/images"

B. USE CASE 1: ONE IMAGE

# PREPARE ABSOLUTE PATHS FOR IMAGES
image_path_list_0 = []
image_path_list_0.append(ROOT_DIR +  "/tulip-under-rain.jpg")
print(image_path_list_0)
# READ AND SHOW IMAGES
image_list_0 = []
for image_path_str in image_path_list_0:
    input_path = Path(image_path_str)
    image = cv2.imread(str(input_path))
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    image_list_0.append(image)
    plt.figure(figsize=(6,4))
    plt.title(image.shape)
    plt.imshow(image)

['../examples/images/tulip-under-rain.jpg']

# SET ZOOM RATIO
TEST_RATIO_0 = 0.6
# INIT TRANSFORMER OBJECT
center_zoom_transformer_0 = CenteredZoom(keep_ratio=TEST_RATIO_0)

[03-20 09:01:29] {/home/jovyan/Maturation/NeuralDE/examples/../neural_de/utils/twe_logger.py:123} INFO - Logger: name: neural_de_logger, handlers: [<StreamHandler stdout (DEBUG)>]

# APPLY TRANSFORMATION TO INPUT IMAGE
transformed_images_0 = center_zoom_transformer_0.transform(image_list_0)

# COMPARE IMAGES BEFORE AND AFTER TRANSFORMATION
for image_index_ in range(len(image_list_0)):
    plt.figure(figsize=(8,6))
    plt.subplot(1,2,1)
    plt.title(f'''Before {image_list_0[image_index_].shape}''')
    plt.imshow(image_list_0[image_index_])
    plt.subplot(1,2,2)
    plt.title(f'''After {transformed_images_0[image_index_].shape}''')
    plt.imshow(transformed_images_0[image_index_])
    plt.show()

C. USE CASE 2: BATCH OF IMAGES

image_path_list_1 = []

image_path_list_1.append(ROOT_DIR +  "/tulip-under-rain.jpg")
image_path_list_1.append(ROOT_DIR +  "/test_snow.webp")
image_path_list_1.append(ROOT_DIR +  "/test_brightness_car.jpg")
image_path_list_1.append(ROOT_DIR +  "/street-rain-men.jpeg")

print(image_path_list_1)
image_list_1 = []
for image_path_str in image_path_list_1:
    input_path = Path(image_path_str)
    image = cv2.imread(str(input_path))
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    image_list_1.append(image)
    plt.figure(figsize=(6,4))
    plt.title(image.shape)
    plt.imshow(image)

['../examples/images/tulip-under-rain.jpg', '../examples/images/test_snow.webp', '../examples/images/test_brightness_car.jpg', '../examples/images/street-rain-men.jpeg']

# SET ZOOM RATIO
TEST_RATIO_1 = 0.4
# INIT TRANSFORMER OBJECT
center_zoom_transformer_1 = CenteredZoom(keep_ratio=TEST_RATIO_1)

[03-20 09:01:31] {/home/jovyan/Maturation/NeuralDE/examples/../neural_de/utils/twe_logger.py:123} INFO - Logger: name: neural_de_logger, handlers: [<StreamHandler stdout (DEBUG)>]

# APPLY TRANSFORMATION TO INPUT IMAGES
transformed_images_1 = center_zoom_transformer_1.transform(image_list_1)

# COMPARE IMAGES BEFORE AND AFTER TRANSFORMATION
for image_index_ in range(len(image_list_1)):
    plt.figure(figsize=(8,6))
    plt.subplot(1,2,1)
    plt.title(f'''Before {image_list_1[image_index_].shape}''')
    plt.imshow(image_list_1[image_index_])
    plt.subplot(1,2,2)
    plt.title(f'''After {transformed_images_1[image_index_].shape}''')
    plt.imshow(transformed_images_1[image_index_])
    plt.show()

end=time.time()
print("temps final : ",end-start)

temps final :  3.04654598236084