https://github.com/romerorodriguezd/traffic-lights-tracking-and-color-detection-opencv
Traffic lights tracking and color detection with OpenCV. Combination of a MOSSE tracker and inner freehand rectangles.
https://github.com/romerorodriguezd/traffic-lights-tracking-and-color-detection-opencv
color-detection opencv tracking tracking-algorithm traffic-analysis traffic-light-classification
Last synced: 3 months ago
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Traffic lights tracking and color detection with OpenCV. Combination of a MOSSE tracker and inner freehand rectangles.
- Host: GitHub
- URL: https://github.com/romerorodriguezd/traffic-lights-tracking-and-color-detection-opencv
- Owner: RomeroRodriguezD
- Created: 2023-09-21T09:28:18.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-10-05T09:30:52.000Z (over 1 year ago)
- Last Synced: 2024-01-28T17:41:16.832Z (over 1 year ago)
- Topics: color-detection, opencv, tracking, tracking-algorithm, traffic-analysis, traffic-light-classification
- Language: Jupyter Notebook
- Homepage:
- Size: 3.77 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
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README
## Traffic Lights Tracker + Detection ##
### OpenCV ###
This algorithm attempts to identify traffic lights color assuming there's some movement on static cameras based on climatic conditions. In order to do that, it applies a combination of a MOSSE tracker, with a freehand rectangle.
**Why this combination?**
Because under certain conditions, like light changes caused by day-night cycles, the MOSSE tracker may lose the traffic lights because the edges of the object may become blurry or even unexistant.
This problem can be solved by applying a bigger region of interest for the tracker, which can be immune to light changes due to its content. But this implies adding a drawback: Tracking the exact color of the traffic light on a way bigger region.
To solve the second question, we'll draw a second ROI manually, inside the already chosen one for the MOSSE tracker, starting from the top left corner (that's just a personal choice).
### Import main modules ###
Pretty much all we'll need is OpenCV and Numpy.
```python
# Install opencv
!pip install opencv-contrib-python
```
Requirement already satisfied: opencv-contrib-python in c:\users\usuario\anaconda3\lib\site-packages (4.8.0.76)
Requirement already satisfied: numpy>=1.21.2 in c:\users\usuario\anaconda3\lib\site-packages (from opencv-contrib-python) (1.24.3)
```python
import cv2
print("OpenCV version:", cv2.__version__)
import numpy as np
from IPython.display import Image, display, clear_output
```
OpenCV version: 4.8.0
### Inference functions ###
```python
def predict_color(frame):
color_found = 'undefined'
# Color thresholds.
color_list = [
['Red', [0, 120, 70], [10, 255, 255]],
['Yellow', [12, 55, 80], [32, 255, 255]],
['Green / Off', [50, 5, 150], [86, 250, 250]],
['Red', [170, 120, 70], [180, 255, 255]]
]
# Change to HSV spectrum.
hsv_img = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# The state of the light is the one with the greatest number of white pixels.
max_count = 0
for color_name, lower_val, upper_val in color_list:
# Threshold the HSV image - any matching color will show up as white.
mask = cv2.inRange(hsv_img, np.array(lower_val), np.array(upper_val))
# Count white pixels on mask.
count = np.sum(mask)
if count > max_count:
color_found = color_name
max_count = count
if max_count < 1600: # Arbitrary threshold to define when it's off (rare cases, mostly at night).
color_found = "Green / Off"
lightColor = color_found
if lightColor == 'red':
class_id = 'Red'
elif lightColor == 'yellow':
class_id = 'Yellow'
elif lightColor == 'green':
class_id = 'Green'
print(lightColor)
return lightColor
```
**Runtime**
First, before the tracking loop starts, it will ask for how many lights we want to track. Determining the ROI's follow a simple
pattern of 'Define the outer ROI for MOSSE + define inner ROI for the traffic light'.
```python
if __name__ == "__main__":
# Sets the number of traffic lights.
traffic_lights_count = int(input('Set a number of traffic light: \n'))
TL_list = []
vid = cv2.VideoCapture("semaforo2_resized.mp4")
ok, frame = vid.read()
# For each traffic light, choose outer and inner ROI's.
for i in range(traffic_lights_count):
print('Select a ROI to track:')
initial_box = cv2.selectROI('image', frame)
tracker = cv2.legacy.TrackerMOSSE.create()
print('Select an area to track INSIDE the ROI')
x_box, y_box, w_box, h_box = cv2.selectROI('image', frame)
inside_box = (x_box, y_box, w_box, h_box)
TL_list.append((tracker, initial_box, inside_box))
while True:
ok, fr = vid.read()
# Breaks the loop if the video is over.
if fr is None:
break
# For each tracker, it will update the color detected inside
for tracker, initial_box, inside_box in TL_list:
if initial_box is not None:
(success, box) = tracker.update(fr)
if success:
(x, y, w, h) = [int(v) for v in box]
x2_inside = x
y2_inside = y
w2_inside = min(w_box, w)
h2_inside = min(h_box, h)
prediction = None
frame_piece = fr[y2_inside:y2_inside + h2_inside, x2_inside:x2_inside + w2_inside]
prediction = predict_color(frame_piece)
font = cv2.FONT_HERSHEY_SIMPLEX
font_scale = 0.5
thickness = 1
if prediction == 'Green / Off':
color = (0, 255, 0)
elif prediction == 'Red':
color = (0, 0, 255)
elif prediction == 'Yellow':
color = (255, 255, 0)
cv2.putText(fr, prediction, (x2_inside, y2_inside - 10), font, font_scale, color, thickness)
tracker.init(fr, initial_box)
#cv2.imshow("image", fr) -> That's the option if you're not in Jupyter Notebook, otherwise:
clear_output(wait=True) # Cleans previous frame in Jupyter Notebook
display(Image(data=cv2.imencode('.png', fr)[1].tobytes()))
key = cv2.waitKey(200) & 0xFF
cv2.destroyAllWindows()
```
**Output sample**
```python
display(Image(filename='sample_final.gif'))
display(Image(filename='sample_night.gif'))
```
### Conclusions ###
Even though the light changes and movement are a hard challenge, the combination of trackers looks quite accurate, even at night, since it just tend to lose the color under sunlight (where colors become a mess in general) or under some rare random condition.