Skip to content

2
2022-235 Vehicle parking system
Commit 9bb92e52 authored by @Thilakasiri_M.D.T.S's avatar @Thilakasiri_M.D.T.S
Browse files
Options

Update componet 4

parent e4821c01
Hide whitespace changes
Inline Side-by-side
Showing with 100 additions and 74 deletions
component_4/human_action_detector.py
View file @ 9bb92e52
...@@ -11,74 +11,86 @@ mp_drawing = mp.solutions.drawing_utils ...@@ -11,74 +11,86 @@ mp_drawing = mp.solutions.drawing_utils
mp_holistic = mp.solutions.holistic mp_holistic = mp.solutions.holistic
mp_pose = mp.solutions.pose mp_pose = mp.solutions.pose
mp_drawing.DrawingSpec(color=(0, 0, 255), thickness=2, circle_radius=2) threshold = 0.25
def calculate_angle(a, b, c):
a = np.array(a) # First
b = np.array(b) # Mid
c = np.array(c) # End
radians = np.arctan2(c[1] - b[1], c[0] - b[0]) - np.arctan2(a[1] - b[1], a[0] - b[0]) mp_drawing.DrawingSpec(color=(0, 0, 255), thickness=2, circle_radius=2)
angle = np.abs(radians * 180.0 / np.pi)
if angle > 180.0:
angle = 360 - angle
return angle def distanceCalculate(p1, p2):
"""p1 and p2 in format (x1,y1) and (x2,y2) tuples"""
dis = ((p2[0] - p1[0]) ** 2 + (p2[1] - p1[1]) ** 2) ** 0.5
return dis
def get_human_thred(): def get_human_thred():
cap = cv2.VideoCapture(0) cap = cv2.VideoCapture('input_videos/2.mp4')
## Setup mediapipe instance ## Setup mediapipe instance
index_num = 0
with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose: with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:
while cap.isOpened(): while cap.isOpened():
ret, frame = cap.read() ret, frame = cap.read()
# Recolor image to RGB if ret:
image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image.flags.writeable = False
# Make detection # Recolor image to RGB
results = pose.process(image) cv2.imwrite('images/' + str(index_num) + '.jpg', frame)
index_num += 1
image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image.flags.writeable = False
# Recolor back to BGR # Make detection
image.flags.writeable = True results = pose.process(image)
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# Extract landmarks # Recolor back to BGR
try: image.flags.writeable = True
landmarks = results.pose_landmarks.landmark image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# Get coordinates # Extract landmarks
shoulder = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x, try:
landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y] landmarks = results.pose_landmarks.landmark
elbow = [landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].x,
landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].y] nose = (landmarks[mp_pose.PoseLandmark.NOSE.value].x,
wrist = [landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x, landmarks[mp_pose.PoseLandmark.NOSE.value].y)
landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y]
left_wrist = (landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x,
# Calculate angle landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y)
angle = calculate_angle(shoulder, elbow, wrist)
right_wrist = (landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].x,
# Visualize angle landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].y)
cv2.putText(image, str(angle),
tuple(np.multiply(elbow, [640, 480]).astype(int)), distance_left = distanceCalculate(nose, left_wrist)
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA distance_right = distanceCalculate(nose, right_wrist)
)
print('distance - ', distance_left)
except: if distance_left < threshold or distance_right < threshold:
pass print('danger')
cv2.putText(image, 'Danger Detection',
tuple(np.multiply(nose, [640, 480]).astype(int)),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2, cv2.LINE_AA
)
else:
print('no danger')
cv2.putText(image, 'No Danger Detection',
tuple(np.multiply(nose, [640, 480]).astype(int)),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2, cv2.LINE_AA
)
# Render detections
mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,
mp_drawing.DrawingSpec(color=(245, 117, 66), thickness=2, circle_radius=2),
mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=2)
)
cv2.imshow('Mediapipe Feed', image) except:
pass
if cv2.waitKey(10) & 0xFF == ord('q'): # Render detections
mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,
mp_drawing.DrawingSpec(color=(245, 117, 66), thickness=2, circle_radius=2),
mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=2)
)
cv2.imshow('CCTV Feed', image)
if cv2.waitKey(10) & 0xFF == ord('q'):
break
else:
break break
cap.release() cap.release()
...@@ -88,38 +100,52 @@ def get_human_thred(): ...@@ -88,38 +100,52 @@ def get_human_thred():
def get_human_thred_temp(): def get_human_thred_temp():
with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose: with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:
sample_imgs = os.listdir('component_4/sample_data') sample_imgs = os.listdir('component_4/sample_data')
filename = 'component_4/sample_data/' + str(random.choice(sample_imgs))
frame = cv2.imread(filename)
frame = cv2.imread('component_4/sample_data/' + str(random.choice(sample_imgs))) try:
# Recolor image to RGB image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) image.flags.writeable = False
image.flags.writeable = False
# Make detection # Make detection
results = pose.process(image) results = pose.process(image)
# Recolor back to BGR # Recolor back to BGR
image.flags.writeable = True image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# Extract landmarks
# Extract landmarks
try:
landmarks = results.pose_landmarks.landmark landmarks = results.pose_landmarks.landmark
# Get coordinates nose = (landmarks[mp_pose.PoseLandmark.NOSE.value].x,
shoulder = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x, landmarks[mp_pose.PoseLandmark.NOSE.value].y)
landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y]
elbow = [landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].x, left_wrist = (landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x,
landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].y] landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y)
wrist = [landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x,
landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y]
# Calculate angle right_wrist = (landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].x,
angle = calculate_angle(shoulder, elbow, wrist) landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].y)
if angle > 60: distance_left = distanceCalculate(nose, left_wrist)
return True distance_right = distanceCalculate(nose, right_wrist)
if distance_left < threshold or distance_right < threshold:
cv2.putText(image, 'Danger Detection',
tuple(np.multiply(nose, [640, 480]).astype(int)),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2, cv2.LINE_AA
)
cv2.imwrite('component_4/out/out.png', image)
return True, filename
else: else:
return False cv2.putText(image, 'No Danger Detection',
tuple(np.multiply(nose, [640, 480]).astype(int)),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2, cv2.LINE_AA
)
cv2.imwrite('component_4/out/out.png', image)
return False, filename
except: except:
return False return False, filename
# get_human_thred()
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment