Delete helper.py

helper.py

@@ -1,232 +0,0 @@
-import io
-import matplotlib.pyplot as plt
-import requests
-import inflect
-from PIL import Image
-import torch
-import numpy as np
-
-def load_image_from_url(url):
-    return Image.open(requests.get(url, stream=True).raw)
-
-def render_results_in_image(in_pil_img, in_results):
-    plt.figure(figsize=(16, 10))
-    plt.imshow(in_pil_img)
-
-    ax = plt.gca()
-
-    for prediction in in_results:
-
-        x, y = prediction['box']['xmin'], prediction['box']['ymin']
-        w = prediction['box']['xmax'] - prediction['box']['xmin']
-        h = prediction['box']['ymax'] - prediction['box']['ymin']
-
-        ax.add_patch(plt.Rectangle((x, y),
-                                   w,
-                                   h,
-                                   fill=False,
-                                   color="green",
-                                   linewidth=2))
-        ax.text(
-           x,
-           y,
-           f"{prediction['label']}: {round(prediction['score']*100, 1)}%",
-           color='red'
-        )
-
-    plt.axis("off")
-
-    # Save the modified image to a BytesIO object
-    img_buf = io.BytesIO()
-    plt.savefig(img_buf, format='png',
-                bbox_inches='tight',
-                pad_inches=0)
-    img_buf.seek(0)
-    modified_image = Image.open(img_buf)
-
-    # Close the plot to prevent it from being displayed
-    plt.close()
-
-    return modified_image
-
-def summarize_predictions_natural_language(predictions):
-    summary = {}
-    p = inflect.engine()
-
-    for prediction in predictions:
-        label = prediction['label']
-        if label in summary:
-            summary[label] += 1
-        else:
-            summary[label] = 1
-
-    result_string = "In this image, there are "
-    for i, (label, count) in enumerate(summary.items()):
-        count_string = p.number_to_words(count)
-        result_string += f"{count_string} {label}"
-        if count > 1:
-          result_string += "s"
-
-        result_string += " "
-
-        if i == len(summary) - 2:
-          result_string += "and "
-
-    # Remove the trailing comma and space
-    result_string = result_string.rstrip(', ') + "."
-
-    return result_string
-
-
-##### To ignore warnings #####
-import warnings
-import logging
-from transformers import logging as hf_logging
-
-def ignore_warnings():
-    # Ignore specific Python warnings
-    warnings.filterwarnings("ignore", message="Some weights of the model checkpoint")
-    warnings.filterwarnings("ignore", message="Could not find image processor class")
-    warnings.filterwarnings("ignore", message="The `max_size` parameter is deprecated")
-
-    # Adjust logging for libraries using the logging module
-    logging.basicConfig(level=logging.ERROR)
-    hf_logging.set_verbosity_error()
-
-########
-
-def show_mask(mask, ax, random_color=False):
-    if random_color:
-        color = np.concatenate([np.random.random(3),
-                                np.array([0.6])],
-                               axis=0)
-    else:
-        color = np.array([30/255, 144/255, 255/255, 0.6])
-    h, w = mask.shape[-2:]
-    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
-    ax.imshow(mask_image)
-
-
-def show_box(box, ax):
-    x0, y0 = box[0], box[1]
-    w, h = box[2] - box[0], box[3] - box[1]
-    ax.add_patch(plt.Rectangle((x0, y0),
-                               w,
-                               h, edgecolor='green',
-                               facecolor=(0,0,0,0),
-                               lw=2))
-
-def show_boxes_on_image(raw_image, boxes):
-    plt.figure(figsize=(10,10))
-    plt.imshow(raw_image)
-    for box in boxes:
-      show_box(box, plt.gca())
-    plt.axis('on')
-    plt.show()
-
-def show_points_on_image(raw_image, input_points, input_labels=None):
-    plt.figure(figsize=(10,10))
-    plt.imshow(raw_image)
-    input_points = np.array(input_points)
-    if input_labels is None:
-      labels = np.ones_like(input_points[:, 0])
-    else:
-      labels = np.array(input_labels)
-    show_points(input_points, labels, plt.gca())
-    plt.axis('on')
-    plt.show()
-
-def show_points_and_boxes_on_image(raw_image,
-                                   boxes,
-                                   input_points,
-                                   input_labels=None):
-    plt.figure(figsize=(10,10))
-    plt.imshow(raw_image)
-    input_points = np.array(input_points)
-    if input_labels is None:
-      labels = np.ones_like(input_points[:, 0])
-    else:
-      labels = np.array(input_labels)
-    show_points(input_points, labels, plt.gca())
-    for box in boxes:
-      show_box(box, plt.gca())
-    plt.axis('on')
-    plt.show()
-
-
-def show_points_and_boxes_on_image(raw_image,
-                                   boxes,
-                                   input_points,
-                                   input_labels=None):
-    plt.figure(figsize=(10,10))
-    plt.imshow(raw_image)
-    input_points = np.array(input_points)
-    if input_labels is None:
-      labels = np.ones_like(input_points[:, 0])
-    else:
-      labels = np.array(input_labels)
-    show_points(input_points, labels, plt.gca())
-    for box in boxes:
-      show_box(box, plt.gca())
-    plt.axis('on')
-    plt.show()
-
-
-def show_points(coords, labels, ax, marker_size=375):
-    pos_points = coords[labels==1]
-    neg_points = coords[labels==0]
-    ax.scatter(pos_points[:, 0],
-               pos_points[:, 1],
-               color='green',
-               marker='*',
-               s=marker_size,
-               edgecolor='white',
-               linewidth=1.25)
-    ax.scatter(neg_points[:, 0],
-               neg_points[:, 1],
-               color='red',
-               marker='*',
-               s=marker_size,
-               edgecolor='white',
-               linewidth=1.25)
-
-
-def fig2img(fig):
-    """Convert a Matplotlib figure to a PIL Image and return it"""
-    import io
-    buf = io.BytesIO()
-    fig.savefig(buf)
-    buf.seek(0)
-    img = Image.open(buf)
-    return img
-
-
-def show_mask_on_image(raw_image, mask, return_image=False):
-    if not isinstance(mask, torch.Tensor):
-      mask = torch.Tensor(mask)
-
-    if len(mask.shape) == 4:
-      mask = mask.squeeze()
-
-    fig, axes = plt.subplots(1, 1, figsize=(15, 15))
-
-    mask = mask.cpu().detach()
-    axes.imshow(np.array(raw_image))
-    show_mask(mask, axes)
-    axes.axis("off")
-    plt.show()
-
-    if return_image:
-      fig = plt.gcf()
-      return fig2img(fig)
-
-
-
-
-def show_pipe_masks_on_image(raw_image, outputs):
-  plt.imshow(np.array(raw_image))
-  ax = plt.gca()
-  for mask in outputs["masks"]:
-      show_mask(mask, ax=ax, random_color=True)
-  plt.axis("off")
-  plt.show()