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import numpy as np |
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import cv2 |
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import albumentations as A |
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from utils import * |
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import random |
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from albumentations.pytorch import ToTensorV2 |
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from yolov3 import YOLOV3_PL |
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from pytorch_grad_cam.utils.image import show_cam_on_image |
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from utils import YoloCAM, cells_to_bboxes, non_max_suppression |
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from model import YOLOv3 |
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def inference(image: np.ndarray, iou_thresh: float = 0.5, thresh: float = 0.5, transparency: float = 0.5): |
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model = YOLOV3_PL() |
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model.load_state_dict(torch.load("model.pth", map_location=torch.device('cpu')), strict=False) |
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scaled_anchors = config.SCALED_ANCHORS |
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transforms = A.Compose( |
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[ |
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A.LongestMaxSize(max_size=config.IMAGE_SIZE), |
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A.PadIfNeeded( |
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min_height=config.IMAGE_SIZE, min_width=config.IMAGE_SIZE, border_mode=cv2.BORDER_CONSTANT |
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), |
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A.Normalize(mean=[0, 0, 0], std=[1, 1, 1], max_pixel_value=255,), |
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ToTensorV2(), |
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], |
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) |
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with torch.no_grad(): |
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transformed_image = transforms(image=image)["image"].unsqueeze(0) |
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output = model(transformed_image) |
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bboxes = [[] for _ in range(1)] |
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for i in range(3): |
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batch_size, A1, S, _, _ = output[i].shape |
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anchor = scaled_anchors[i].to('cpu') |
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boxes_scale_i = cells_to_bboxes( |
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output[i].to('cpu'), anchor, S=S, is_preds=True |
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) |
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for idx, (box) in enumerate(boxes_scale_i): |
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bboxes[idx] += box |
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nms_boxes = non_max_suppression( |
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bboxes[0], iou_threshold=iou_thresh, threshold=thresh, box_format="midpoint", |
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) |
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plot_img = draw_predictions(image, nms_boxes, class_labels=config.PASCAL_CLASSES) |
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return [plot_img] |
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def draw_predictions(image: np.ndarray, boxes: list[list], class_labels: list[str]) -> np.ndarray: |
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"""Plots predicted bounding boxes on the image""" |
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colors = [[random.randint(0, 255) for _ in range(3)] for name in class_labels] |
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im = np.array(image) |
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height, width, _ = im.shape |
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bbox_thick = int((height + width) /500) |
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for box in boxes: |
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assert len(box) == 6, "box should contain class pred, confidence, x, y, width, height" |
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class_pred = box[0] |
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conf = box[1] |
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box = box[2:] |
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upper_left_x = box[0] - box[2] / 2 |
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upper_left_y = box[1] - box[3] / 2 |
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x1 = int(upper_left_x * width) |
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y1 = int(upper_left_y * height) |
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x2 = x1 + int(box[2] * width) |
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y2 = y1 + int(box[3] * height) |
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cv2.rectangle( |
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image, |
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(x1, y1), (x2, y2), |
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color=colors[int(class_pred)], |
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thickness=bbox_thick |
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) |
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text = f"{class_labels[int(class_pred)]}: {conf:.2f}" |
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t_size = cv2.getTextSize(text, 0, 0.7, thickness=bbox_thick // 2)[0] |
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c3 = (x1 + t_size[0], y1 - t_size[1] - 3) |
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cv2.rectangle(image, (x1, y1), c3, colors[int(class_pred)], -1) |
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cv2.putText( |
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image, |
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text, |
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(x1, y1 - 2), |
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cv2.FONT_HERSHEY_SIMPLEX, |
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0.7, |
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(0, 0, 0), |
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bbox_thick // 2, |
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lineType=cv2.LINE_AA, |
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) |
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return image |