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Arnav Raina

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# Utilities

import cv2
import numpy as np
from PIL import Image

def load_img(img_path, input_shape):
    # Loading image
    image = cv2.imread(img_path)
    image_height, image_width = image.shape[:2]
    Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

    input_height, input_width = input_shape[2:]
    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    resized = cv2.resize(image_rgb, (input_width, input_height)) # image resized as req by onnx model

    input_image = resized / 255.0 # scaling image
    input_image = input_image.transpose(2,0,1) # dim rearranged as req by onnx (batch_size, channel, height, width)
    input_tensor = input_image[np.newaxis, :, :, :].astype(np.float32)

    return image, image_height, image_width, input_height, input_width, input_tensor

def nms(boxes, scores, iou_threshold):
    # Sort by score
    sorted_indices = np.argsort(scores)[::-1]

    keep_boxes = []
    while sorted_indices.size > 0:
        # Pick the last box
        box_id = sorted_indices[0]
        keep_boxes.append(box_id)

        # Compute IoU of the picked box with the rest
        ious = compute_iou(boxes[box_id, :], boxes[sorted_indices[1:], :])

        # Remove boxes with IoU over the threshold
        keep_indices = np.where(ious < iou_threshold)[0]

        # print(keep_indices.shape, sorted_indices.shape)
        sorted_indices = sorted_indices[keep_indices + 1]

    return keep_boxes

def compute_iou(box, boxes):
    # Compute xmin, ymin, xmax, ymax for both boxes
    xmin = np.maximum(box[0], boxes[:, 0])
    ymin = np.maximum(box[1], boxes[:, 1])
    xmax = np.minimum(box[2], boxes[:, 2])
    ymax = np.minimum(box[3], boxes[:, 3])

    # Compute intersection area
    intersection_area = np.maximum(0, xmax - xmin) * np.maximum(0, ymax - ymin)

    # Compute union area
    box_area = (box[2] - box[0]) * (box[3] - box[1])
    boxes_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
    union_area = box_area + boxes_area - intersection_area

    # Compute IoU
    iou = intersection_area / union_area

    return iou


def xywh2xyxy(x):
    # Convert bounding box (x, y, w, h) to bounding box (x1, y1, x2, y2)
    y = np.copy(x)
    y[..., 0] = x[..., 0] - x[..., 2] / 2
    y[..., 1] = x[..., 1] - x[..., 3] / 2
    y[..., 2] = x[..., 0] + x[..., 2] / 2
    y[..., 3] = x[..., 1] + x[..., 3] / 2
    return y

	# Utilities

	import cv2
	import numpy as np
	from PIL import Image

	def load_img(img_path, input_shape):
	# Loading image
	image = cv2.imread(img_path)
	image_height, image_width = image.shape[:2]
	Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

	input_height, input_width = input_shape[2:]
	image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
	resized = cv2.resize(image_rgb, (input_width, input_height)) # image resized as req by onnx model

	input_image = resized / 255.0 # scaling image
	input_image = input_image.transpose(2,0,1) # dim rearranged as req by onnx (batch_size, channel, height, width)
	input_tensor = input_image[np.newaxis, :, :, :].astype(np.float32)

	return image, image_height, image_width, input_height, input_width, input_tensor

	def nms(boxes, scores, iou_threshold):
	# Sort by score
	sorted_indices = np.argsort(scores)[::-1]

	keep_boxes = []
	while sorted_indices.size > 0:
	# Pick the last box
	box_id = sorted_indices[0]
	keep_boxes.append(box_id)

	# Compute IoU of the picked box with the rest
	ious = compute_iou(boxes[box_id, :], boxes[sorted_indices[1:], :])

	# Remove boxes with IoU over the threshold
	keep_indices = np.where(ious < iou_threshold)[0]

	# print(keep_indices.shape, sorted_indices.shape)
	sorted_indices = sorted_indices[keep_indices + 1]

	return keep_boxes

	def compute_iou(box, boxes):
	# Compute xmin, ymin, xmax, ymax for both boxes
	xmin = np.maximum(box[0], boxes[:, 0])
	ymin = np.maximum(box[1], boxes[:, 1])
	xmax = np.minimum(box[2], boxes[:, 2])
	ymax = np.minimum(box[3], boxes[:, 3])

	# Compute intersection area
	intersection_area = np.maximum(0, xmax - xmin) * np.maximum(0, ymax - ymin)

	# Compute union area
	box_area = (box[2] - box[0]) * (box[3] - box[1])
	boxes_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
	union_area = box_area + boxes_area - intersection_area

	# Compute IoU
	iou = intersection_area / union_area

	return iou


	def xywh2xyxy(x):
	# Convert bounding box (x, y, w, h) to bounding box (x1, y1, x2, y2)
	y = np.copy(x)
	y[..., 0] = x[..., 0] - x[..., 2] / 2
	y[..., 1] = x[..., 1] - x[..., 3] / 2
	y[..., 2] = x[..., 0] + x[..., 2] / 2
	y[..., 3] = x[..., 1] + x[..., 3] / 2
	return y