My OS is Raspberry Pi OS (Legacy) with desktop Bullseye 64 bit, python version is Python 3.9.2, and pip version is pip 23.3.1.
I am attempting to run this sample code in order to execute and test object detection with TensorFlow Lite. It instructs me to run the line below.
pip3 install opencv-python==4.4.0.46
However, I encounter the following error.
ERROR: Failed building wheel for opencv-python
Failed to build opencv-python
ERROR: Could not build wheels for opencv-python which use PEP 517 and cannot be installed directly
I then tried
pip install --upgrade pip setuptools wheel
But got the error
TypeError: _classify_installed_files() got an unexpected keyword argument 'cmake_install_dir'
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
ERROR: Failed building wheel for opencv-python
Failed to build opencv-python
ERROR: Could not build wheels for opencv-python, which is required to install pyproject.toml-based projects
I tried to run
sudo pip install opencv-python==4.4.0.46
with error
ERROR: Failed building wheel for opencv-python
Failed to build opencv-python
ERROR: Could not build wheels for opencv-python which use PEP 517 and cannot be installed directly
I am attempting to run this sample code in order to execute and test object detection with TensorFlow Lite. With the code and instructions given below.
#!/usr/bin/python3
# Copyright (c) 2022 Raspberry Pi Ltd
# Author: Alasdair Allan <alasdair@raspberrypi.com>
# SPDX-License-Identifier: BSD-3-Clause
# A TensorFlow Lite example for Picamera2 on Raspberry Pi OS Bullseye
#
# Install necessary dependences before starting,
#
# $ sudo apt update
# $ sudo apt install build-essential
# $ sudo apt install libatlas-base-dev
# $ sudo apt install python3-pip
# $ pip3 install tflite-runtime
# $ pip3 install opencv-python==4.4.0.46
# $ pip3 install pillow
# $ pip3 install numpy
#
# and run from the command line,
#
# $ python3 real_time_with_labels.py --model mobilenet_v2.tflite --label coco_labels.txt
import argparse
import cv2
import numpy as np
import tflite_runtime.interpreter as tflite
from picamera2 import MappedArray, Picamera2, Preview
normalSize = (640, 480)
lowresSize = (320, 240)
rectangles = []
def ReadLabelFile(file_path):
with open(file_path, 'r') as f:
lines = f.readlines()
ret = {}
for line in lines:
pair = line.strip().split(maxsplit=1)
ret[int(pair[0])] = pair[1].strip()
return ret
def DrawRectangles(request):
with MappedArray(request, "main") as m:
for rect in rectangles:
print(rect)
rect_start = (int(rect[0] * 2) - 5, int(rect[1] * 2) - 5)
rect_end = (int(rect[2] * 2) + 5, int(rect[3] * 2) + 5)
cv2.rectangle(m.array, rect_start, rect_end, (0, 255, 0, 0))
if len(rect) == 5:
text = rect[4]
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(m.array, text, (int(rect[0] * 2) + 10, int(rect[1] * 2) + 10),
font, 1, (255, 255, 255), 2, cv2.LINE_AA)
def InferenceTensorFlow(image, model, output, label=None):
global rectangles
if label:
labels = ReadLabelFile(label)
else:
labels = None
interpreter = tflite.Interpreter(model_path=model, num_threads=4)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
height = input_details[0]['shape'][1]
width = input_details[0]['shape'][2]
floating_model = False
if input_details[0]['dtype'] == np.float32:
floating_model = True
rgb = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
initial_h, initial_w, channels = rgb.shape
picture = cv2.resize(rgb, (width, height))
input_data = np.expand_dims(picture, axis=0)
if floating_model:
input_data = (np.float32(input_data) - 127.5) / 127.5
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
detected_boxes = interpreter.get_tensor(output_details[0]['index'])
detected_classes = interpreter.get_tensor(output_details[1]['index'])
detected_scores = interpreter.get_tensor(output_details[2]['index'])
num_boxes = interpreter.get_tensor(output_details[3]['index'])
rectangles = []
for i in range(int(num_boxes)):
top, left, bottom, right = detected_boxes[0][i]
classId = int(detected_classes[0][i])
score = detected_scores[0][i]
if score > 0.5:
xmin = left * initial_w
ymin = bottom * initial_h
xmax = right * initial_w
ymax = top * initial_h
box = [xmin, ymin, xmax, ymax]
rectangles.append(box)
if labels:
print(labels[classId], 'score = ', score)
rectangles[-1].append(labels[classId])
else:
print('score = ', score)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model', help='Path of the detection model.', required=True)
parser.add_argument('--label', help='Path of the labels file.')
parser.add_argument('--output', help='File path of the output image.')
args = parser.parse_args()
if (args.output):
output_file = args.output
else:
output_file = 'out.jpg'
if (args.label):
label_file = args.label
else:
label_file = None
picam2 = Picamera2()
picam2.start_preview(Preview.QTGL)
config = picam2.create_preview_configuration(main={"size": normalSize},
lores={"size": lowresSize, "format": "YUV420"})
picam2.configure(config)
stride = picam2.stream_configuration("lores")["stride"]
picam2.post_callback = DrawRectangles
picam2.start()
while True:
buffer = picam2.capture_buffer("lores")
grey = buffer[:stride * lowresSize[1]].reshape((lowresSize[1], stride))
_ = InferenceTensorFlow(grey, args.model, output_file, label_file)
if __name__ == '__main__':
main()
I have been looking through google with no luck, and am asking as many forums as I can in hopes of finding a solution. Help would be greatly appreciated!