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mainML.py
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mainML.py
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import re
import cv2
import numpy as np
from tensorflow.lite.python.interpreter import Interpreter
import csv
import datetime
CSV_FILE_PATH = 'traffic_data.csv'
CAMERA_WIDTH = 640
CAMERA_HEIGHT = 360
def write_data_to_csv(data):
with open(CSV_FILE_PATH, 'a', newline='') as file:
writer = csv.writer(file)
writer.writerow(data)
def set_input_tensor(interpreter, image):
"""Sets the input tensor."""
tensor_index = interpreter.get_input_details()[0]['index']
input_tensor = interpreter.tensor(tensor_index)()[0]
input_tensor[:, :] = np.expand_dims((image-255)/255, axis=0)
def get_output_tensor(interpreter, index):
"""Returns the output tensor at the given index."""
output_details = interpreter.get_output_details()[index]
tensor = np.squeeze(interpreter.get_tensor(output_details['index']))
return tensor
def detect_objects(interpreter, image, threshold):
"""Returns a list of detection results, each a dictionary of object info."""
set_input_tensor(interpreter, image)
interpreter.invoke()
# Get all output details
boxes = get_output_tensor(interpreter, 0)
classes = get_output_tensor(interpreter, 1)
scores = get_output_tensor(interpreter, 2)
count = 0 # Access the first element
for i in range(9):
if(boxes[i] >= 0.75):
count += 1
else:
break
results = []
for i in range(count):
if scores >= threshold:
box_coordinates = classes[i]
ymin, xmin, ymax, xmax = box_coordinates
result = {
'bounding_box': [ymin, xmin, ymax, xmax],
'class_id': 'car',
'score': scores
}
results.append(result)
return results
def compare_rows(row, previous_row): # check if car has passed the middle of the frame
currX = float(row[1])
prevX = float(previous_row[1])
if(currX > 320 and prevX < 320 and abs(currX - prevX) <= 80):
return 1;
if (currX < 320 and prevX > 320 and abs(currX - prevX) <= 80):
return -1;
return 0;
def append_to_csv(direction, timestamp, date):
with open('final_data.csv', 'a', newline='') as file:
writer = csv.writer(file)
writer.writerow([direction, timestamp, date])
def delete_data_in_csv():
with open(CSV_FILE_PATH, 'w') as file:
file.truncate(0)
def process_csv():
car_count_left_to_right = 0
car_count_right_to_left = 0
with open(CSV_FILE_PATH, 'r') as file:
reader = csv.reader(file)
# Initialize the previous_row
previous_row = None
# Iterate through the remaining rows
for row in reader:
add = 0
# Compare with the previous row
if previous_row is not None:
add = compare_rows(row, previous_row)
if(add == 1):
car_count_left_to_right += 1
append_to_csv("right", row[5], row[6])
elif(add == -1):
car_count_right_to_left += 1
append_to_csv("left", row[5], row[6])
# Update the previous_row variable
previous_row = row
print(f"Total cars (Left to Right): {car_count_left_to_right}")
print(f"Total cars (Right to Left): {car_count_right_to_left}")
print("Processed data saved to final_data.csv")
delete_data_in_csv()
def main():
label = "car"
interpreter = Interpreter('detect.tflite')
interpreter.allocate_tensors()
_, input_height, input_width, _ = interpreter.get_input_details()[0]['shape']
cap = cv2.VideoCapture(0)
car_data = {}
while cap.isOpened():
ret, frame = cap.read()
img = cv2.resize(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB), (320, 320))
res = detect_objects(interpreter, img, 0.8)
for result in res:
box_coordinates = result['bounding_box']
ymin, xmin, ymax, xmax = box_coordinates
xmin = int(max(1, xmin * CAMERA_WIDTH))
xmax = int(min(CAMERA_WIDTH, xmax * CAMERA_WIDTH))
ymin = int(max(1, ymin * CAMERA_HEIGHT))
ymax = int(min(CAMERA_HEIGHT, ymax * CAMERA_HEIGHT))
car_id = None
for existing_car_id, existing_car_data in car_data.items():
existing_xmin = existing_car_data['xmin']
existing_ymin = existing_car_data['ymin']
existing_xmax = existing_car_data['xmax']
existing_ymax = existing_car_data['ymax']
if (
xmin >= existing_xmin and ymin >= existing_ymin and
xmax <= existing_xmax and ymax <= existing_ymax
):
car_id = existing_car_id
break
if car_id is None:
car_id = f'car_{len(car_data) + 1}'
car_data[car_id] = {
'xmin': xmin,
'ymin': ymin,
'xmax': xmax,
'ymax': ymax
}
cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), (0, 255, 0), 3)
cv2.putText(frame, result['class_id'], (xmin, min(ymax, CAMERA_HEIGHT-20)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1, cv2.LINE_AA)
timestamp = datetime.datetime.now().strftime('%H:%M:%S')
date = datetime.datetime.now().strftime('%Y-%m-%d')
data = [car_id, xmin, ymin, xmax, ymax, timestamp, date]
write_data_to_csv(data)
cv2.imshow('Car Detection', frame)
if cv2.waitKey(10) & 0xFF == ord('q'):
cap.release()
cv2.destroyAllWindows()
# after loop breaks we want to exec on processData.py (pasted funcs over)
process_csv()
if __name__ == "__main__":
main()