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Detect.py
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Detect.py
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# Importing necessary files
import numpy as np
import time
import cv2
import imutils
from imutils.video import FPS
import cv2
import mediapipe as mp
import time
from imutils.video import VideoStream
# Input file name
INPUT_FILE='trial_004_kinect_webcam.avi'
# OUTPUT_FILE='output.avi'
# Getting the YOLO files and defining confidence threshold
LABELS_FILE='data/coco.names'
CONFIG_FILE='cfg/yolov3.cfg'
WEIGHTS_FILE='yolov3.weights'
CONFIDENCE_THRESHOLD=0.3
H=None
W=None
# Defining the Mediapipe pose detection class with all functions
class poseDetector():
# Initializing the variables with default values
def __init__(self, mode= False, upBody = False, smooth= True,
detectionConf = 0.5, trackConf = 0.5):
self.mode = mode
self.upBody = upBody
self.smooth = smooth
self.detectionConf = detectionConf
self.trackConf = trackConf
self.mpDraw = mp.solutions.drawing_utils
self.mpPose = mp.solutions.pose
self.pose = self.mpPose.Pose(self.mode,
self.upBody,
self.smooth,
self.detectionConf,
self.trackConf)
# Detecting the pose with image as input
def findPose(self, img, draw = True):
imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
self.results = self.pose.process(imgRGB)
if (self.results.pose_landmarks):
if draw:
self.mpDraw.draw_landmarks(img, self.results.pose_landmarks, self.mpPose.POSE_CONNECTIONS)
return img
# Drawing landmarks and printing results.
def findPosition(self, img, draw = True):
lmlist = []
if(self.results.pose_landmarks):
for id, lm in enumerate(self.results.pose_landmarks.landmark):
h, w, c = img.shape
# print(id, lm)
cx, cy = int(lm.x*w), int(lm.y*h)
lmlist.append([id, cx, cy])
if draw:
cv2.circle(img, (cx, cy), 2, (255, 0, 0), cv2.FILLED)
return lmlist
fps = FPS().start()
# fourcc = cv2.VideoWriter_fourcc(*"MJPG")
# writer = cv2.VideoWriter(OUTPUT_FILE, fourcc, 30,
# (800, 600), True)
LABELS = open(LABELS_FILE).read().strip().split("\n")
np.random.seed(4)
COLORS = np.random.randint(0, 255, size=(len(LABELS), 3),
dtype="uint8")
# Reading the neural network from Yolo framework for detection of "person"
net = cv2.dnn.readNetFromDarknet(CONFIG_FILE, WEIGHTS_FILE)
# Defining the video input
vs = cv2.VideoCapture(INPUT_FILE)
# determine only the *output* layer names that we need from YOLO
ln = net.getLayerNames()
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
cnt =0;
# Defining the poseDetector class
detector = poseDetector()
# Looping through the frames
while True:
cnt+=1
print ("Frame number", cnt)
try:
(grabbed, image) = vs.read()
except:
break
blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (416, 416),
swapRB=True, crop=False)
net.setInput(blob)
if W is None or H is None:
(H, W) = image.shape[:2]
layerOutputs = net.forward(ln)
# initialize our lists of detected bounding boxes, confidences, and
# class IDs, respectively
boxes = []
confidences = []
classIDs = []
# loop over each of the layer outputs
for output in layerOutputs:
# loop over each of the detections
for detection in output:
# extract the class ID and confidence (i.e., probability) of
# the current object detection
scores = detection[5:]
classID = np.argmax(scores)
confidence = scores[classID]
# print(classID, " Class id IS/")
# filter out weak predictions by ensuring the detected
# probability is greater than the minimum probability
if confidence > CONFIDENCE_THRESHOLD:
if LABELS[classID] == "person":
# scale the bounding box coordinates back relative to the
# size of the image, keeping in mind that YOLO actually
# returns the center (x, y)-coordinates of the bounding
# box followed by the boxes' width and height
box = detection[0:4] * np.array([W, H, W, H])
(centerX, centerY, width, height) = box.astype("int")
# use the center (x, y)-coordinates to derive the top and
# and left corner of the bounding box
x = int(centerX - (width / 2))
y = int(centerY - (height / 2))
# update our list of bounding box coordinates, confidences,
# and class IDs
boxes.append([x, y, int(width), int(height)])
confidences.append(float(confidence))
classIDs.append(classID)
# apply non-maxima suppression to suppress weak, overlapping bounding
# boxes
idxs = cv2.dnn.NMSBoxes(boxes, confidences, CONFIDENCE_THRESHOLD,
CONFIDENCE_THRESHOLD)
imageArr = []
# ensure at least one detection exists
if len(idxs) > 0:
# loop over the indexes we are keeping
for i in idxs.flatten():
# extract the bounding box coordinates
(x, y) = (boxes[i][0], boxes[i][1])
(w, h) = (boxes[i][2], boxes[i][3])
color = [int(c) for c in COLORS[classIDs[i]]]
xmin, ymin, xmax, ymax = boxes[i]
# crop detection from image (take an additional 5 pixels around all edges)
cropped_img = image[int(y) - 5:int(y+h) + 5, int(x) - 5:int(x+w) + 5]
imageArr.append(cropped_img)
cv2.rectangle(image, (x, y), (x + w, y + h), color, 2)
text = "{}: {:.4f}".format(LABELS[classIDs[i]], confidences[i])
cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX,
0.5, color, 2)
# show the output image
cv2.imshow("output", cv2.resize(image,(800, 600)))
i = 0
for cropped_img in imageArr:
img = detector.findPose(cropped_img)
lmlist = detector.findPosition(img, True)
print(lmlist)
cv2.imshow("crooped" + str(i), cv2.resize(cropped_img, (200, 600)))
i = i+1
# writer.write(cv2.resize(image,(800, 600)))
fps.update()
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
break
fps.stop()
print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
# do a bit of cleanup
cv2.destroyAllWindows()
# release the file pointers
print("[INFO] cleaning up...")
# writer.release()
vs.release()