i wanted to work on pose correction system for my college final year project and needed a my own datasets. but i am not able to get the pose coordinates from the video into CSV file no matter what i try. I tried but there is no code or reference which can help me in it.
So can anyone please tell how should i do it.
this is my code
import cv2
mp_drawing = mp.solutions.drawing_utils
mp_pose = mp.solutions.pose
cap = cv2.VideoCapture('C:/Users/lenovo/Downloads/pexels-julia-larson-6455077.mp4')
count=0
## Setup mediapipe instance
with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:
while cap.isOpened():
ret, frame = cap.read()
# Recolor image to RGB
image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image.flags.writeable = False
# Make detection
results = pose.process(image)
# Recolor back to BGR
image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# Extract landmarks
try:
landmarks = results.pose_landmarks.landmark
LANDMARKS = landmarks[mp_pose.PoseLandmark.value]
df = pd.DataFrame(LANDMARKS,columns)
df.to_csv (r'C:/Users/lenovo/Downloads/dataset4.csv', index = False, header=True)
except:
pass
# 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)
if cv2.waitKey(10) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
Try moving the lines saving the data to pandas DataFrame outside the while loop.
Create an empty list: landmarks_list
For each frame append landmarks to list.
Run the followning lines after the while has finished running over the video frames
df = pd.DataFrame(landmarks_list,columns)
df.to_csv (r'C:/Users/lenovo/Downloads/dataset4.csv', index = False, header=True)
Related
I'm making a Python script to save the landmarks obtained when using mediapipe. I want to save only the landmarks but I don't want to save the rest of the frame content. Is this possible?
My first idea was to create a black image and put on top of it the landmarks that are obtained when processing the frame that has a person, and when I show it with cv2.imshow(img) I get the landmarks but when I want to save the video I only see black frames. Can anyone help me, I leave below the function that I have done.
import cv2
import numpy as np
import mediapipe as mp
from mediapipe.python.solutions.face_mesh_connections import FACEMESH_CONTOURS
mp_drawing = mp.solutions.drawing_utils
mp_holistic = mp.solutions.holistic
def extract_bones(video_path, new_video_path):
# Captura de los videos
#cap = cv2.VideoCapture(video_path)
cap = cv2.VideoCapture(video_path)
# Se toma el ancho y alto del video
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
# Se crea el formato del nuevo video.
output = cv2.VideoWriter(new_video_path,cv2.VideoWriter_fourcc('M','J','P','G'), 30, (width,height))
# Initialize holistic model
with mp_holistic.Holistic(min_detection_confidence = 0.5, min_tracking_confidence = 0.5) as holistic:
while cap.isOpened():
# Read frame
ret, frame = cap.read()
if ret == True:
img = np.zeros((frame.shape[0], frame.shape[1], frame.shape[2]))
# Resize frame
#frame = cv2.resize(frame, (WIDTH, HEIGHT), interpolation = cv2.INTER_AREA)
# Change color from BGR to RGB
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame.flags.writeable = False
# Detect landmarks
results = holistic.process(frame)
# Left hand (azul)
mp_drawing.draw_landmarks(
img, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS,
mp_drawing.DrawingSpec(color=(255, 255, 0), thickness=2, circle_radius=1),
mp_drawing.DrawingSpec(color=(255, 0, 0), thickness=2))
print(img)
# Right hand (verde)
mp_drawing.draw_landmarks(
img, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS,
mp_drawing.DrawingSpec(color=(0, 255, 0), thickness=2, circle_radius=1),
mp_drawing.DrawingSpec(color=(57, 143, 0), thickness=2))
print(img)
# Pose
mp_drawing.draw_landmarks(
img, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS,
mp_drawing.DrawingSpec(color=(128, 0, 255), thickness=2, circle_radius=1),
mp_drawing.DrawingSpec(color=(255, 255, 255), thickness=2))
print(img)
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
cv2.imshow("Frame", frame)
cv2.imshow("Black", img)
output.write(img)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
cap.release()
Thank you very much
i need to track my cursor to the nose of a human to create a DIY aimbot with pose detection.
(just for fun, not intending to cheat, there would be so many better and easier options than to make my own)
i already have the first part of the code and it shows you your screen and the skeleton, as well as the exact coordinates of the nose with no problem,
but the method that im using to move my cursor over to that point is not working
im using mouse.move and have tried other stuff like pyautogui, tkinter.
it doesn't give me an error but still does not work
import cv2
import mediapipe as mp
import numpy as np
import time
import pyautogui
import mouse
mp_drawing = mp.solutions.drawing_utils
mp_pose = mp.solutions.pose
# display screen resolution, get it from your OS settings
SCREEN_SIZEX = (1920)
SCREEN_SIZEY = (1080)
# define the codec
fourcc = cv2.VideoWriter_fourcc(*"XVID")
# create the video write object
out = cv2.VideoWriter("output.avi", fourcc, 20.0, (SCREEN_SIZEX, SCREEN_SIZEY))
with mp_pose.Pose(min_detection_confidence=0.1, min_tracking_confidence=0.9) as pose:
while True:
# make a screenshot
img = pyautogui.screenshot()
# convert these pixels to a proper numpy array to work with OpenCV
frame = np.array(img)
# convert colors from BGR to RGB
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Recolor image to RGB
image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image.flags.writeable = False
# Make detection
results = pose.process(image)
# Recolor back to BGR
image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
try:
landmarks = results.pose_landmarks.landmark
lndmark = landmarks[mp_pose.PoseLandmark.NOSE.value]
x = [landmarks[mp_pose.PoseLandmark.NOSE.value].x]
y = [landmarks[mp_pose.PoseLandmark.NOSE.value].y]
#print(x)
#print(y)
mouse.move(x, y)
except:
pass
# 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)
)
# write the frame
out.write(frame)
pTime = 0
cTime = time.time()
fps = 1 / (cTime - pTime)
pTime = cTime
cv2.putText(image, str(int(fps)), (20, 50), cv2.FONT_HERSHEY_PLAIN, 3,
(255, 0, 0), 3)
cv2.imshow('Mediapipe Feed', image)
if cv2.waitKey(10) & 0xFF == ord('q'):
break
out.release()
cv2.destroyAllWindows()
#for lndmark in mp_pose.PoseLandmark:
#print(lndmark)
this is the part that doesn't work:
try:
landmarks = results.pose_landmarks.landmark
lndmark = landmarks[mp_pose.PoseLandmark.NOSE.value]
x = [landmarks[mp_pose.PoseLandmark.NOSE.value].x]
y = [landmarks[mp_pose.PoseLandmark.NOSE.value].y]
mouse.move(x, y)
except:
pass
i would assume that it is beacuse x and y are supposed to numbers or somehow it can't read or proccess it correctly
but it doesn't give me an error, so im asking it here hoping on of you guys had already figured this one out
This code is for detecting fire using tensorflow keras model and I do not know how to draw rectangle around the fire detected object:
model = tf.keras.models.load_model('my_model\\InceptionV3.h5')
video = cv2.VideoCapture(0)
while True:
_, frame = video.read()
im = Image.fromarray(frame, 'RGB')
im = im.resize((224, 224))
img_array = image.img_to_array(im)
img_array = np.expand_dims(img_array, axis=0) / 255
probabilities = model.predict(img_array)[0]
prediction = np.argmax(probabilities)
# if prediction is 0, which means there is fire in the frame.
if prediction == 0:
img = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
print(probabilities[prediction])
cv2.imshow("Capturing", frame)
cv2.imshow("Capturing", img)
if cv2.waitKey(1) & 0xff == ord('q'):
break
video.release()
cv2.destroyAllWindows()
thanks for taking the time to read, and hopefully help me.
I have an AR.Drone 2.0 that I have already started to program/develop. I am using python for coding with opencv for the image processing. I want to be able to feedback this code to the drone. I was thinking about obtaining frames from the video stream and have the AR.Drone perform some tasks based upon the images. I, however, don't know where to start. It would be helpful for me if someone can point me in the right direction.
import numpy as np
import cv2
# open the camera
cap = cv2.VideoCapture('tcp://192.168.1.1:5555')
def nothing(x):
pass
cv2.namedWindow('result')
# Starting with 100's to prevent error while masking
h,s,v = 100,100,100
# Creating track bar
cv2.createTrackbar('h', 'result',0,179,nothing)
cv2.createTrackbar('s', 'result',0,255,nothing)
cv2.createTrackbar('v', 'result',0,255,nothing)
while True:
#read the image from the camera
ret, frame = cap.read()
#You will need this later
frame = cv2.cvtColor(frame, 35)
#converting to HSV
hsv = cv2.cvtColor(frame,cv2.COLOR_BGR2HSV)
# get info from track bar and appy to result
h = cv2.getTrackbarPos('h','result')
s = cv2.getTrackbarPos('s','result')
v = cv2.getTrackbarPos('v','result')
# Normal masking algorithm
lower_blue = np.array([h,s,v])
upper_blue = np.array([180,255,255])
mask = cv2.inRange(hsv,lower_blue, upper_blue)
result = cv2.bitwise_and(frame,frame,mask = mask)
cv2.imshow('result',result)
#find center
cnts=cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)[-2]
center=None
if len(cnts)>0:
c=max(cnts, key=cv2.contourArea)
((x,y),radius)=cv2.minEnclosingCircle(c)
M=cv2.moments(c)
center=(int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))
if radius>10:
#cv2.circle(frame, (int(x),int(y)), int(radius), 2)
cv2.circle(frame, center,5,(0,0,255),-1)
# color detection limits
lB = 5
lG = 50
lR = 50
hB = 15
hG = 255
hR = 255
lowerLimits = np.array([lB, lG, lR])
upperLimits = np.array([hB, hG, hR])
# Our operations on the frame come here
thresholded = cv2.inRange(frame, lowerLimits, upperLimits)
outimage = cv2.bitwise_and(frame, frame, mask = thresholded)
cv2.imshow('original', frame)
# Display the resulting frame
cv2.imshow('processed',outimage)
# Quit the program when Q is pressed
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything done, release the capture
print 'closing program'
cap.release()
cv2.destroyAllWindows()'
I am using opencv to detect the color of objects by using HSV trackbars values and I want my python code to save the latest changes I would make to the trackbars in opencv , when I start the code again, the trackbars will have the last values? below is my code
import numpy as np
import cv2
# open the camera
cap = cv2.VideoCapture(0)
def nothing(x):
pass
cv2.namedWindow('result')
# Starting with 100's to prevent error while masking
h,s,v = 100,100,100
# Creating track bar
cv2.createTrackbar('h', 'result',0,179,nothing)
cv2.createTrackbar('s', 'result',0,255,nothing)
cv2.createTrackbar('v', 'result',0,255,nothing)
while True:
#read the image from the camera
ret, frame = cap.read()
#You will need this later
frame = cv2.cvtColor(frame, 35)
#converting to HSV
hsv = cv2.cvtColor(frame,cv2.COLOR_BGR2HSV)
# get info from track bar and appy to result
h = cv2.getTrackbarPos('h','result')
s = cv2.getTrackbarPos('s','result')
v = cv2.getTrackbarPos('v','result')
# Normal masking algorithm
lower_blue = np.array([h,s,v])
upper_blue = np.array([180,255,255])
mask = cv2.inRange(hsv,lower_blue, upper_blue)
result = cv2.bitwise_and(frame,frame,mask = mask)
cv2.imshow('result',result)
#find center
cnts=cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)[-2]
center=None
if len(cnts)>0:
c=max(cnts, key=cv2.contourArea)
((x,y),radius)=cv2.minEnclosingCircle(c)
M=cv2.moments(c)
center=(int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))
if radius>10:
#cv2.circle(frame, (int(x),int(y)), int(radius), 2)
cv2.circle(frame, center,5,(0,0,255),-1)
# color detection limits
lB = 5
lG = 50
lR = 50
hB = 15
hG = 255
hR = 255
lowerLimits = np.array([lB, lG, lR])
upperLimits = np.array([hB, hG, hR])
# Our operations on the frame come here
thresholded = cv2.inRange(frame, lowerLimits, upperLimits)
outimage = cv2.bitwise_and(frame, frame, mask = thresholded)
cv2.imshow('original', frame)
# Display the resulting frame
cv2.imshow('processed',outimage)
# Quit the program when Q is pressed
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything done, release the capture
print 'closing program'
cap.release()
cv2.destroyAllWindows()
One option is to write the values to a text file somewhere, then when the program starts, read the file and parse the values written in the file.
See: How could I save data after closing my program?