MLECO-2493 Add python OD example with TFLite delegate
Signed-off-by: Raviv Shalev <raviv.shalev@arm.com>
Change-Id: I25fcccbf912be0c5bd4fbfd2e97552341958af35
diff --git a/python/pyarmnn/examples/common/cv_utils.py b/python/pyarmnn/examples/common/cv_utils.py
index e12ff50..36d1039 100644
--- a/python/pyarmnn/examples/common/cv_utils.py
+++ b/python/pyarmnn/examples/common/cv_utils.py
@@ -1,4 +1,4 @@
-# Copyright © 2020-2021 Arm Ltd and Contributors. All rights reserved.
+# Copyright © 2020-2022 Arm Ltd and Contributors. All rights reserved.
# SPDX-License-Identifier: MIT
"""
@@ -11,29 +11,35 @@
import cv2
import numpy as np
-import pyarmnn as ann
-
-def preprocess(frame: np.ndarray, input_binding_info: tuple, is_normalised: bool):
+def preprocess(frame: np.ndarray, input_data_type, input_data_shape: tuple, is_normalised: bool,
+ keep_aspect_ratio: bool=True):
"""
Takes a frame, resizes, swaps channels and converts data type to match
- model input layer. The converted frame is wrapped in a const tensor
- and bound to the input tensor.
+ model input layer.
Args:
frame: Captured frame from video.
- input_binding_info: Contains shape and data type of model input layer.
+ input_data_type: Contains data type of model input layer.
+ input_data_shape: Contains shape of model input layer.
is_normalised: if the input layer expects normalised data
+ keep_aspect_ratio: Network executor's input data aspect ratio
Returns:
Input tensor.
"""
- # Swap channels and resize frame to model resolution
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
- resized_frame = resize_with_aspect_ratio(frame, input_binding_info)
+ if keep_aspect_ratio:
+ # Swap channels and resize frame to model resolution
+ resized_frame = resize_with_aspect_ratio(frame, input_data_shape)
+ else:
+ # select the height and width from input_data_shape
+ frame_height = input_data_shape[1]
+ frame_width = input_data_shape[2]
+ resized_frame = cv2.resize(frame, (frame_width, frame_height))
# Expand dimensions and convert data type to match model input
- if input_binding_info[1].GetDataType() == ann.DataType_Float32:
+ if np.float32 == input_data_type:
data_type = np.float32
if is_normalised:
resized_frame = resized_frame.astype("float32")/255
@@ -41,26 +47,24 @@
data_type = np.uint8
resized_frame = np.expand_dims(np.asarray(resized_frame, dtype=data_type), axis=0)
- assert resized_frame.shape == tuple(input_binding_info[1].GetShape())
+ assert resized_frame.shape == input_data_shape
- input_tensors = ann.make_input_tensors([input_binding_info], [resized_frame])
- return input_tensors
+ return resized_frame
-
-def resize_with_aspect_ratio(frame: np.ndarray, input_binding_info: tuple):
+def resize_with_aspect_ratio(frame: np.ndarray, input_data_shape: tuple):
"""
Resizes frame while maintaining aspect ratio, padding any empty space.
Args:
frame: Captured frame.
- input_binding_info: Contains shape of model input layer.
+ input_data_shape: Contains shape of model input layer.
Returns:
Frame resized to the size of model input layer.
"""
aspect_ratio = frame.shape[1] / frame.shape[0]
- model_height, model_width = list(input_binding_info[1].GetShape())[1:3]
+ _, model_height, model_width, _ = input_data_shape
if aspect_ratio >= 1.0:
new_height, new_width = int(model_width / aspect_ratio), model_width
@@ -173,14 +177,14 @@
# Create label for detected object class
label = f'{label} {confidence * 100:.1f}%'
- label_color = (0, 0, 0) if sum(color)>200 else (255, 255, 255)
+ label_color = (0, 0, 0) if sum(color) > 200 else (255, 255, 255)
# Make sure label always stays on-screen
x_text, y_text = cv2.getTextSize(label, cv2.FONT_HERSHEY_DUPLEX, 1, 1)[0][:2]
lbl_box_xy_min = (x_min, y_min if y_min<25 else y_min - y_text)
lbl_box_xy_max = (x_min + int(0.55 * x_text), y_min + y_text if y_min<25 else y_min)
- lbl_text_pos = (x_min + 5, y_min + 16 if y_min<25 else y_min - 5)
+ lbl_text_pos = (x_min + 5, y_min + 16 if y_min < 25 else y_min - 5)
# Add label and confidence value
cv2.rectangle(frame, lbl_box_xy_min, lbl_box_xy_max, color, -1)
@@ -190,3 +194,19 @@
def get_source_encoding_int(video_capture):
return int(video_capture.get(cv2.CAP_PROP_FOURCC))
+
+
+def crop_bounding_box_object(input_frame: np.ndarray, x_min: float, y_min: float, x_max: float, y_max: float):
+ """
+ Creates a cropped image based on x and y coordinates.
+
+ Args:
+ input_frame: Image to crop
+ x_min, y_min, x_max, y_max: Coordinates of the bounding box
+
+ Returns:
+ Cropped image
+ """
+ # Adding +1 to exclude the bounding box pixels.
+ cropped_image = input_frame[int(y_min) + 1:int(y_max), int(x_min) + 1:int(x_max)]
+ return cropped_image