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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 |