Upload display.py

display.py

@@ -0,0 +1,94 @@
+import numpy as np
+import cv2
+import albumentations as A
+from utils import *
+import random
+from albumentations.pytorch import ToTensorV2
+
+def inference(image: np.ndarray, iou_thresh: float = 0.5, thresh: float = 0.4, show_cam: bool = False, transparency: float = 0.5):
+
+    transforms =  A.Compose(
+    [
+        A.LongestMaxSize(max_size=config.IMAGE_SIZE),
+        A.PadIfNeeded(
+            min_height=config.IMAGE_SIZE, min_width=config.IMAGE_SIZE, border_mode=cv2.BORDER_CONSTANT
+        ),
+        A.Normalize(mean=[0, 0, 0], std=[1, 1, 1], max_pixel_value=255,),
+        ToTensorV2(),
+    ],
+    )
+    with torch.no_grad():
+        transformed_image = transforms(image=image)["image"].unsqueeze(0)
+        output = model(transformed_image)
+
+        bboxes = [[] for _ in range(1)]
+        for i in range(3):
+            batch_size, A1, S, _, _ = output[i].shape
+            anchor = scaled_anchors[i].to('cpu')
+            boxes_scale_i = cells_to_bboxes(
+                output[i].to('cpu'), anchor, S=S, is_preds=True
+            )
+            for idx, (box) in enumerate(boxes_scale_i):
+                bboxes[idx] += box
+
+    nms_boxes = non_max_suppression(
+        bboxes[0], iou_threshold=iou_thresh, threshold=thresh, box_format="midpoint",
+    )
+    plot_img = draw_predictions(image, nms_boxes, class_labels=config.PASCAL_CLASSES)
+    if not show_cam:
+        return [plot_img]
+
+    grayscale_cam = cam(transformed_image, scaled_anchors)[0, :, :]
+    img = cv2.resize(image, (416, 416))
+    img = np.float32(img) / 255
+    cam_image = show_cam_on_image(img, grayscale_cam, use_rgb=True, image_weight=transparency)
+    return [plot_img, cam_image]
+
+
+def draw_predictions(image: np.ndarray, boxes: list[list], class_labels: list[str]) -> np.ndarray:
+    """Plots predicted bounding boxes on the image"""
+
+    colors = [[random.randint(0, 255) for _ in range(3)] for name in class_labels]
+
+    im = np.array(image)
+    height, width, _ = im.shape
+    bbox_thick = int((height + width) /1000)
+
+    # Create a Rectangle patch
+    for box in boxes:
+        assert len(box) == 6, "box should contain class pred, confidence, x, y, width, height"
+        class_pred = box[0]
+        conf = box[1]
+        box = box[2:]
+        upper_left_x = box[0] - box[2] / 2
+        upper_left_y = box[1] - box[3] / 2
+
+        x1  = int(upper_left_x * width)
+        y1 = int(upper_left_y * height)
+
+        x2 = x1 + int(box[2] * width)
+        y2 = y1 + int(box[3] * height)
+
+        cv2.rectangle(
+            image,
+            (x1, y1), (x2, y2),
+            color=colors[int(class_pred)],
+            thickness=bbox_thick
+        )
+        text = f"{class_labels[int(class_pred)]}: {conf:.2f}"
+        t_size = cv2.getTextSize(text, 0, 0.7, thickness=bbox_thick // 2)[0]
+        c3 = (x1 + t_size[0], y1 - t_size[1] - 3)
+
+        cv2.rectangle(image, (x1, y1), c3, colors[int(class_pred)], -1)
+        cv2.putText(
+            image,
+            text,
+            (x1, y1 - 2),
+            cv2.FONT_ITALIC,
+            0.7,
+            (0, 0, 0),
+            bbox_thick // 2,
+            lineType=cv2.LINE_8,
+        )
+
+    return image