MLECO-955: Added python object detection example for PyArmNN
Change-Id: I1344c027f4cc70520b7846b34dfbc2abf399d10a
Signed-off-by: Jakub Sujak <jakub.sujak@arm.com>
diff --git a/python/pyarmnn/examples/object_detection/yolo.py b/python/pyarmnn/examples/object_detection/yolo.py
new file mode 100644
index 0000000..1748d15
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+++ b/python/pyarmnn/examples/object_detection/yolo.py
@@ -0,0 +1,98 @@
+# Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+# SPDX-License-Identifier: MIT
+
+"""
+Contains functions specific to decoding and processing inference results for YOLO V3 Tiny models.
+"""
+
+import cv2
+import numpy as np
+
+
+def iou(box1: list, box2: list):
+ """
+ Calculates the intersection-over-union (IoU) value for two bounding boxes.
+
+ Args:
+ box1: Array of positions for first bounding box
+ in the form [x_min, y_min, x_max, y_max].
+ box2: Array of positions for second bounding box.
+
+ Returns:
+ Calculated intersection-over-union (IoU) value for two bounding boxes.
+ """
+ area_box1 = (box1[2] - box1[0]) * (box1[3] - box1[1])
+ area_box2 = (box2[2] - box2[0]) * (box2[3] - box2[1])
+
+ if area_box1 <= 0 or area_box2 <= 0:
+ iou_value = 0
+ else:
+ y_min_intersection = max(box1[1], box2[1])
+ x_min_intersection = max(box1[0], box2[0])
+ y_max_intersection = min(box1[3], box2[3])
+ x_max_intersection = min(box1[2], box2[2])
+
+ area_intersection = max(0, y_max_intersection - y_min_intersection) *\
+ max(0, x_max_intersection - x_min_intersection)
+ area_union = area_box1 + area_box2 - area_intersection
+
+ try:
+ iou_value = area_intersection / area_union
+ except ZeroDivisionError:
+ iou_value = 0
+
+ return iou_value
+
+
+def yolo_processing(output: np.ndarray, confidence_threshold=0.40, iou_threshold=0.40):
+ """
+ Performs non-maximum suppression on input detections. Any detections
+ with IOU value greater than given threshold are suppressed.
+
+ Args:
+ output: Vector of outputs from network.
+ confidence_threshold: Selects only strong detections above this value.
+ iou_threshold: Filters out boxes with IOU values above this value.
+
+ Returns:
+ A list of detected objects in the form [class, [box positions], confidence]
+ """
+ if len(output) != 1:
+ raise RuntimeError('Number of outputs from YOLO model does not equal 1')
+
+ # Find the array index of detections with confidence value above threshold
+ confidence_det = output[0][:, :, 4][0]
+ detections = list(np.where(confidence_det > confidence_threshold)[0])
+ all_det, nms_det = [], []
+
+ # Create list of all detections above confidence threshold
+ for d in detections:
+ box_positions = list(output[0][:, d, :4][0])
+ confidence_score = output[0][:, d, 4][0]
+ class_idx = np.argmax(output[0][:, d, 5:])
+ all_det.append((class_idx, box_positions, confidence_score))
+
+ # Suppress detections with IOU value above threshold
+ while all_det:
+ element = int(np.argmax([all_det[i][2] for i in range(len(all_det))]))
+ nms_det.append(all_det.pop(element))
+ all_det = [*filter(lambda x: (iou(x[1], nms_det[-1][1]) <= iou_threshold), [det for det in all_det])]
+ return nms_det
+
+
+def yolo_resize_factor(video: cv2.VideoCapture, input_binding_info: tuple):
+ """
+ Gets a multiplier to scale the bounding box positions to
+ their correct position in the frame.
+
+ Args:
+ video: Video capture object, contains information about data source.
+ input_binding_info: Contains shape of model input layer.
+
+ Returns:
+ Resizing factor to scale box coordinates to output frame size.
+ """
+ frame_height = video.get(cv2.CAP_PROP_FRAME_HEIGHT)
+ frame_width = video.get(cv2.CAP_PROP_FRAME_WIDTH)
+ model_height, model_width = list(input_binding_info[1].GetShape())[1:3]
+ return max(frame_height, frame_width) / max(model_height, model_width)