ai_model = model(model_file_path, labels_file_path)
# Initialize video reader
video_file_path = '../Videos/01.mp4'
video_reader = videoReader(video_file_path)
# Detection and preview parameters
score_threshold = 0.4
delay_between_frames = 5
# Perform object detection in the video sequence
while (True):
# Get frame from the video file
frame = video_reader.read_next_frame()
# If frame is None, then break the loop
if (frame is None):
break
# Perform detection
results = ai_model.detect_people(frame, score_threshold)
# Get centers of the bounding boxes (rectangle centers)
rectangle_centers = analyzer.get_rectangle_centers(results)
# Draw centers before displaying results
imgHelper.draw_rectangle_centers(frame, rectangle_centers)
# Display detection results
imgHelper.display_image_with_detected_objects(frame, results,
delay_between_frames)
import common
from image_helper import ImageHelper as imgHelper
from inference import Inference as model
if __name__ == "__main__":
# Load and prepare model
model_file_path = '../Models/01_model.tflite'
labels_file_path = '../Models/02_labels.txt'
# Initialize model
ai_model = model(model_file_path, labels_file_path)
# Get input image
image = imgHelper.load_image('../Images/Lena.png')
# Detect objects
score_threshold = 0.5
results = ai_model.detect_objects(image, score_threshold)
# Display results
imgHelper.display_image_with_detected_objects(image, results)
# Add reference to Part_03 (assuming the code is executed from Part_04 folder)
import sys
sys.path.insert(1, '../Part_03/')
from inference import Inference as model
from image_helper import ImageHelper as imgHelper
from camera import Camera as camera
if __name__ == "__main__":
# Load and prepare model
model_file_path = '../Models/01_model.tflite'
labels_file_path = '../Models/02_labels.txt'
# Initialize model
ai_model = model(model_file_path, labels_file_path)
# Initialize camera
camera_capture = camera()
# Capture frame and perform inference
camera_frame = camera_capture.capture_frame(False)
score_threshold = 0.5
results = ai_model.detect_objects(camera_frame, score_threshold)
# Display results
imgHelper.display_image_with_detected_objects(camera_frame, results)
