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March 1, 2024 03:38
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tflite python opencv demo
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import cv2 | |
import numpy as np | |
import tensorflow as tf | |
class_id_to_name = { | |
1: 'cardboard', | |
2: 'glass', | |
3: 'metal', | |
4: 'papaer', # Assuming 'papaer' was a typo. | |
5: 'paper', | |
6: 'plastic', | |
} | |
# Path to the .tflite model file | |
model_path = '../customTF2/data/tflite/tflite_with_metadata/detect_aug_29_feb.tflite' | |
# Load the TFLite model | |
interpreter = tf.lite.Interpreter(model_path=model_path) | |
interpreter.allocate_tensors() | |
# Get model input and output details | |
input_details = interpreter.get_input_details() | |
output_details = interpreter.get_output_details() | |
# Function to preprocess frame | |
def preprocess_frame(frame): | |
# Resize frame to model input size | |
frame_resized = cv2.resize(frame, (input_details[0]['shape'][2], input_details[0]['shape'][1])) | |
# Convert frame to float32 and normalize (if required by your model) | |
input_data = np.expand_dims(frame_resized, axis=0).astype(np.float32) / 255.0 | |
return input_data | |
def postprocess_results(frame, output_data, threshold=0.5): | |
# Retrieve outputs for the number of detections, detection boxes, detection classes, and detection scores | |
num_detections = int(output_data[2][0]) # 'number of detections' is typically the 3rd output | |
detection_boxes = output_data[1][0][:num_detections] # 'location' | |
detection_classes = output_data[3][0][:num_detections] # 'category' | |
detection_scores = output_data[0][0][:num_detections] # 'score' | |
# Scale box coordinates to frame dimensions. | |
height, width, _ = frame.shape | |
for i in range(num_detections): | |
# Skip detections with a score below the threshold. | |
if detection_scores[i] < threshold: | |
# print(f'Skipping detection {i} with score {detection_scores[i]:.2f}') | |
continue | |
# Get bounding box coordinates. | |
box = detection_boxes[i] | |
ymin, xmin, ymax, xmax = box | |
# Convert to absolute coordinates. | |
xmin = int(xmin * width) | |
xmax = int(xmax * width) | |
ymin = int(ymin * height) | |
ymax = int(ymax * height) | |
# Draw the bounding box on the frame. | |
cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color=(0, 255, 0), thickness=2) | |
# Get class ID and score for display. | |
class_id = int(detection_classes[i]) | |
score = detection_scores[i] | |
# Map class ID to class name | |
class_name = class_id_to_name.get(class_id, "Unknown") | |
print(f'Detected class: {class_name} with score {score:.2f}') | |
# Draw label and score below the bounding box. | |
label = f'{class_name}: {score:.2f}' | |
cv2.putText(frame, label, (xmin, ymax + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2) | |
# Open a handle to the default webcam | |
cap = cv2.VideoCapture(0) | |
if not cap.isOpened(): | |
print("Error: Could not open webcam.") | |
exit() | |
while True: | |
# Capture frame-by-frame | |
ret, frame = cap.read() | |
if not ret: | |
break | |
# Preprocess the frame | |
input_data = preprocess_frame(frame) | |
# Perform the actual detection by running the model with the image as input | |
interpreter.set_tensor(input_details[0]['index'], input_data) | |
interpreter.invoke() | |
# Before your loop, get all output details | |
output_details = interpreter.get_output_details() | |
# Then, in your loop after invoking the interpreter | |
results = [interpreter.get_tensor(output_detail['index']) for output_detail in output_details] | |
# Now call the postprocess_results function with the frame and results | |
# Postprocess the results | |
postprocess_results(frame, results) | |
# Display the resulting frame | |
cv2.imshow('Webcam Feed', frame) | |
# Press 'q' to exit the loop | |
if cv2.waitKey(1) & 0xFF == ord('q'): | |
break | |
# When everything is done, release the capture | |
cap.release() | |
cv2.destroyAllWindows() |
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