utils.py

from PIL import Image, ImageDraw
import torch
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from constants import *

def visualize_bbox(image: Image, prediction):
    img = image.copy()
    draw = ImageDraw.Draw(img)
    for i, box in enumerate(prediction):
        x1, y1, x2, y2 = box.cpu()
        draw = ImageDraw.Draw(img)
        text_w, text_h = draw.textsize(str(i + 1))
        label_y = y1 if y1 <= text_h else y1 - text_h
        draw.rectangle((x1, y1, x2, y2), outline='red')
        draw.rectangle((x1, label_y, x1+text_w, label_y+text_h), outline='red', fill='red')
        draw.text((x1, label_y), str(i + 1), fill='white')
    return img

def xywh2xyxy(x):
    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
    y[..., 0] = x[..., 0]
    y[..., 1] = x[..., 1]
    y[..., 2] = x[..., 0] + x[..., 2] 
    y[..., 3] = x[..., 1] + x[..., 3] 
    return y

def output_tensor_to_boxes(boxes_tensor):
    """
        Converts the YOLO output tensor to list of boxes with probabilites.

        Arguments:
        boxes_tensor -- tensor of shape (S, S, BOX, 5)

        Returns:
        boxes -- list of shape (None, 5)

        Note: "None" is here because you don't know the exact number of selected boxes, as it depends on the threshold. 
        For example, the actual output size of scores would be (10, 5) if there are 10 boxes
    """
    cell_w, cell_h = W/S, H/S
    boxes = []
    
    for i in range(S):
        for j in range(S):
            for b in range(BOX):
                anchor_wh = torch.tensor(ANCHORS[b])
                data = boxes_tensor[i,j,b]
                xy = torch.sigmoid(data[:2])
                wh = torch.exp(data[2:4])*anchor_wh
                obj_prob = torch.sigmoid(data[4])
                
                if obj_prob > OUTPUT_THRESH:
                    x_center, y_center, w, h = xy[0], xy[1], wh[0], wh[1]
                    x, y = x_center+j-w/2, y_center+i-h/2
                    x,y,w,h = x*cell_w, y*cell_h, w*cell_w, h*cell_h
                    box = [x,y,w,h, obj_prob]
                    boxes.append(box)
    return boxes

def overlap(interval_1, interval_2):
    """
        Calculates length of overlap between two intervals.

        Arguments:
        interval_1 -- list or tuple of shape (2,) containing endpoints of the first interval
        interval_2 -- list or tuple of shape (2, 2) containing endpoints of the second interval

        Returns:
        overlap -- length of overlap
    """
    x1, x2 = interval_1
    x3, x4 = interval_2
    if x3 < x1:
        if x4 < x1:
            return 0
        else:
            return min(x2,x4) - x1
    else:
        if x2 < x3:
            return 0
        else:
            return min(x2,x4) - x3


def compute_iou(box1, box2):
    """
        Compute IOU between box1 and box2.

        Argmunets:
        box1 -- list of shape (5, ). Represents the first box
        box2 -- list of shape (5, ). Represents the second box
        Each box is [x, y, w, h, prob]

        Returns:
        iou -- intersection over union score between two boxes
    """
    x1,y1,w1,h1 = box1[0], box1[1], box1[2], box1[3]
    x2,y2,w2,h2 = box2[0], box2[1], box2[2], box2[3]

    area1, area2 = w1*h1, w2*h2
    intersect_w = overlap((x1,x1+w1), (x2,x2+w2))
    intersect_h = overlap((y1,y1+h1), (y2,y2+w2))
    if intersect_w == w1 and intersect_h == h1 or intersect_w == w2 and intersect_h == h2:
        return 1.
    intersect_area = intersect_w*intersect_h
    iou = intersect_area/(area1 + area2 - intersect_area)
    return iou


def nonmax_suppression(boxes, iou_thresh = IOU_THRESH):
    """
        Removes ovelap bboxes

        Arguments:
        boxes -- list of shape (None, 5)
        iou_thresh -- maximal value of iou when boxes are considered different
        Each box is [x, y, w, h, prob]

        Returns:
        boxes -- list of shape (None, 5) with removed overlapping boxes
    """
    boxes = sorted(boxes, key=lambda x: x[4], reverse=True)
    for i, current_box in enumerate(boxes):
        if current_box[4] <= 0:
            continue
        for j in range(i+1, len(boxes)):
            iou = compute_iou(current_box, boxes[j])
            if iou > iou_thresh:
                boxes[j][4] = 0
    boxes = [box for box in boxes if box[4] > 0]
    return boxes

def heatmap(data, row_labels, col_labels, ax=None,
            cbar_kw=None, cbarlabel="", **kwargs):
    """
    Create a heatmap from a numpy array and two lists of labels.

    Parameters
    ----------
    data
        A 2D numpy array of shape (M, N).
    row_labels
        A list or array of length M with the labels for the rows.
    col_labels
        A list or array of length N with the labels for the columns.
    ax
        A `matplotlib.axes.Axes` instance to which the heatmap is plotted.  If
        not provided, use current axes or create a new one.  Optional.
    cbar_kw
        A dictionary with arguments to `matplotlib.Figure.colorbar`.  Optional.
    cbarlabel
        The label for the colorbar.  Optional.
    **kwargs
        All other arguments are forwarded to `imshow`.
    """

    if ax is None:
        ax = plt.gca()

    if cbar_kw is None:
        cbar_kw = {}

    # Plot the heatmap
    im = ax.imshow(data, **kwargs)

    # Create colorbar
    cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)
    cbar.ax.set_ylabel(cbarlabel, rotation=-90, va="bottom")

    # Show all ticks and label them with the respective list entries.
    ax.set_xticks(np.arange(data.shape[1]), labels=col_labels)
    ax.set_yticks(np.arange(data.shape[0]), labels=row_labels)

    # Let the horizontal axes labeling appear on top.
    ax.tick_params(top=True, bottom=False,
                   labeltop=True, labelbottom=False)

    # Rotate the tick labels and set their alignment.
    plt.setp(ax.get_xticklabels(), rotation=-30, ha="right",
             rotation_mode="anchor")

    # Turn spines off and create white grid.
    ax.spines[:].set_visible(False)

    ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)
    ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)
    ax.grid(which="minor", color="w", linestyle='-', linewidth=3)
    ax.tick_params(which="minor", bottom=False, left=False)

    return im, cbar

def annotate_heatmap(im, data=None, valfmt="{x:.2f}",
                     textcolors=("black", "white"),
                     threshold=None, **textkw):
    """
    A function to annotate a heatmap.

    Parameters
    ----------
    im
        The AxesImage to be labeled.
    data
        Data used to annotate.  If None, the image's data is used.  Optional.
    valfmt
        The format of the annotations inside the heatmap.  This should either
        use the string format method, e.g. "$ {x:.2f}", or be a
        `matplotlib.ticker.Formatter`.  Optional.
    textcolors
        A pair of colors.  The first is used for values below a threshold,
        the second for those above.  Optional.
    threshold
        Value in data units according to which the colors from textcolors are
        applied.  If None (the default) uses the middle of the colormap as
        separation.  Optional.
    **kwargs
        All other arguments are forwarded to each call to `text` used to create
        the text labels.
    """

    if not isinstance(data, (list, np.ndarray)):
        data = im.get_array()

    # Normalize the threshold to the images color range.
    if threshold is not None:
        threshold = im.norm(threshold)
    else:
        threshold = im.norm(data.max())/2.

    # Set default alignment to center, but allow it to be
    # overwritten by textkw.
    kw = dict(horizontalalignment="center",
              verticalalignment="center")
    kw.update(textkw)

    # Get the formatter in case a string is supplied
    if isinstance(valfmt, str):
        valfmt = matplotlib.ticker.StrMethodFormatter(valfmt)

    # Loop over the data and create a `Text` for each "pixel".
    # Change the text's color depending on the data.
    texts = []
    for i in range(data.shape[0]):
        for j in range(data.shape[1]):
            kw.update(color=textcolors[int(im.norm(data[i, j]) > threshold)])
            text = im.axes.text(j, i, valfmt(data[i, j], None), **kw)
            texts.append(text)

    return texts