test.py

# import tempfile
# from roboflow import Roboflow
# from PIL import Image
# import json

# def set_image_dpi(file_path):

#     im = Image.open(file_path)

#     length_x, width_y = im.size

#     factor = min(1, float(1024.0 / length_x))

#     size = int(factor * length_x), int(factor * width_y)

#     im_resized = im.resize(size, Image.LANCZOS)

#     temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.png')
#     i = 0
#     i =+ 1
#     temp_filename = f'enhanced{i}.jpg'

#     im_resized.save(temp_filename, dpi=(300, 300))

#     return temp_filename

# def crop_images_from_detections(detections):
#     for detection in detections:
#         image_path = detection["image_path"]
#         image = Image.open(image_path)
        
#         x1 = detection['x'] - detection['width'] / 2
#         x2 = detection['x'] + detection['width'] / 2
#         y1 = detection['y'] - detection['height'] / 2
#         y2 = detection['y'] + detection['height'] / 2
#         box = (x1, y1, x2, y2)
        
#         cropped_image = image.crop(box)
#         path= f"{detection['class']}.jpg"
        
#         cropped_image.save(path)  # Or save the image using cropped_image.save('path_to_save_image')
#         set_image_dpi(path)



# rf = Roboflow(api_key="P4usj8uPwcbnflvyJIAB")

# project = rf.workspace("ntchindagiscard").project("id_card_annotation")


# model = project.version(1).model

# model.predict("2ed1bdb5-5c09-40a0-a39f-9ff6c15380bf-front.jpg", confidence=20, overlap=50).save('prediction.jpg')
# result = model.predict("2ed1bdb5-5c09-40a0-a39f-9ff6c15380bf-front.jpg", confidence=20, overlap=50)
# crop_images_from_detections(result)
# print(result)