start = time()
input_list = input_list_def(args.motor_rate, args.radius, args.weight, args.height, args.time)
output_list, unit = error_check_inputs(input_list)
weight, height, radius, rpm_value, time = output_list['weight'], output_list['height'], output_list['radius'], \
output_list['rpm_value'], output_list['time']
weight, height, radius, rpm_value, time_in_hrs = unit_conversion(unit, weight, height, radius, rpm_value, time)
#execute(MET_mph, args.motor_rate, args.radius, args.weight, args.height, args.time)
end = time()
print(end-start)
Error
end = time()
TypeError: 'float' object is not callable
Related
I want to pass continuous video stream to pyqt5 qlabel named label_cam to show up in ui using QThread but it keep failed showing the video stream on the qlabel. The video stream is later aimed to recognize people out.
I have been trying to connect the signal "change_Pixmap" from the VideoThread class to the "set_image" function in the "label_cam" object but i guess the flow of code or variable assign is wrong. Below is my code.
class FaceRecogScreen(QDialog):
def init(self):
super(FaceRecogScreen, self).init()
uic.loadUi("face_recog.ui", self)
self.update_date_time()
self.pushButton_back.clicked.connect(self.back_to_main3)
self.load_model()
def load_model(self):
self.prototxt = "deploy.prototxt.txt"
self.model = "res10_300x300_ssd_iter_140000.caffemodel"
print("[INFORMATION] Loading model....")
self.net = cv2.dnn.readNetFromCaffe(self.prototxt, self.model)
weight = "facenet_keras_weights.h5"
self.model2 = load_model('FaceNetModel.h5')
self.model2.load_weights(weight)
self.collected_encodings = pickle.loads(open('face_encoding.pickle', "rb").read())
infile = open('face_encoding', 'rb')
data = pickle.load(infile)
self.knownEncodings, self.knownNames = data['encodings'], data['names']
self.knownEncodings = np.array(self.knownEncodings)
self.knownNames = np.array(self.knownNames)
self.clf = svm.SVC(gamma="scale", probability=True, tol=0.01)
self.clf.fit(self.knownEncodings, self.knownNames)
# self.label_cam= VideoLabel()
self.thread = VideoThread(self)
self.thread.change_Pixmap.connect(self.set_image)
# call the run() function in VideoThread class
self.thread.start()
# self.thread.change_Pixmap.connect(self.label_cam.set_image)
# # call the run() function in VideoThread class
# self.thread.start()
# layout = self.layout()
# layout.addWidget(self.label_cam)
# self.thread.run.start()
def update_date_time(self):
# Get the current date and time
date_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
self.label_datetime.setText(date_time)
# Update the date and time in the table
def back_to_main3(self):
pushButton_back = WelcomeScreen()
widget.addWidget(pushButton_back)
widget.setCurrentIndex(widget.currentIndex()+1)
def set_image(self, frame):
self.setPixmap(QPixmap.fromImage(frame))
class VideoThread(QtCore.QThread):
change_Pixmap = QtCore.pyqtSignal(QtGui.QImage)
def run(self):
cap = cv2.VideoCapture(1)
while True:
ret, frame = cap.read()
if ret:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
h, w, ch = frame.shape
bytesPerLine = ch * w
convertToQtFormat = QtGui.QImage(frame.data, w, h, bytesPerLine, QtGui.QImage.Format_RGB888)
p = convertToQtFormat.scaled(640, 480, QtCore.Qt.KeepAspectRatio)
self.change_Pixmap.emit(p)
(h, w) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(
frame, (160, 160)), 1.0, (300, 300), (104, 177, 123))
self.net.setInput(blob)
detections = self.net.forward()
self.frame = frame
# self.frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
cv2.normalize(frame, None, 0, 1.0, cv2.NORM_MINMAX, dtype=cv2.CV_32F)
pixels = np.expand_dims(frame, axis=0)
encode = self.model2.predict(pixels)
face_name = []
for encoding in encode:
name = self.clf.predict([encoding])
face_name.extend(name)
for i in range(0, detections.shape[2]):
confidence = detections[0, 0, i, 2]
if confidence < 0.5:
continue
box = detections[0, 0, i, 3:7]*np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
text = "{:.2f}%".format(confidence*100)
y = startY - 10 if startY - 10 > 10 else startY*10
if name == 'unknown':
cv2.rectangle(frame, (startX, y), (endX, endY), (0, 0, 255), 2)
cv2.putText(frame, name, (startX, startY),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
else:
cv2.rectangle(frame, (startX, y), (endX, endY), (0, 255, 0), 2)
cv2.putText(frame, name[0], (startX, startY),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
I have trained a model using yolov7 https://colab.research.google.com/drive/1X9A8odmK4k6l26NDviiT6dd6TgR-piOa#scrollTo=nD-uPyQ_2jiN colab file. After training I got a file named best.pt and I want to use it as a service/application in python. There is a yolov3 example which I created. This one was using .weights so how can I use .pt?
import cv2
import numpy as np
import time
import os
def getPhoto():
ROOT_DIR = os.path.dirname(__file__)
URL = "http://192.168.1.3:4747/video"
PC_CAM = 0
net = cv2.dnn.readNet(
f"{ROOT_DIR}\\yolov3_custom_final.weights",
f"{ROOT_DIR}\\yolov3_custom.cfg",
)
classes = []
with open(f"{ROOT_DIR}\\classes.txt", "r") as f:
classes = f.read().splitlines()
timeElapsed = 0
wCam, hCam = 640, 360
font = cv2.FONT_HERSHEY_PLAIN
colors = np.random.uniform(0, 255, size=(2, 3))
cap = cv2.VideoCapture(URL)
cap.set(3, wCam)
cap.set(4, hCam)
while True:
_, img = cap.read()
cv2.imshow("Detection Screen", img)
if cv2.waitKey(1) == ord("c"):
break
height, width, _ = img.shape
blob = cv2.dnn.blobFromImage(
img, 1 / 255, (416, 416), (0, 0, 0), swapRB=True, crop=False
)
net.setInput(blob)
output_layers_names = net.getUnconnectedOutLayersNames()
layerOutputs = net.forward(output_layers_names)
boxes = []
confidences = []
class_ids = []
for output in layerOutputs:
for detection in output:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.6:
center_x = int(detection[0] * width)
center_y = int(detection[1] * height)
w = int(detection[2] * width)
h = int(detection[3] * height)
x = int(center_x - w / 2)
y = int(center_y - h / 2)
boxes.append([x, y, w, h])
confidences.append((float(confidence)))
class_ids.append(class_id)
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.2, 0.4)
if len(indexes) > 0:
for i in indexes.flatten():
x, y, w, h = boxes[i]
label = str(classes[class_ids[i]])
confidence = str(round(confidences[i], 2))
color = colors[i]
cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
cv2.putText(
img,
label + " " + confidence,
(x, y + 20),
font,
2,
(255, 255, 0),
2,
)
cv2.imwrite(f"{ROOT_DIR}\\DetectedPhoto.jpg", img)
print("Image Saved")
getPhoto()
I want to detect vehicle plates using .pt file and cut vehicle plates and save them as .jpeg.
you can do this with detect.py insided yolov7 folder. Run this:
python detect.py --weights best.pt --source image.jpg
you can download yolov7 with this method:
git clone https://github.com/WongKinYiu/yolov7.git
in yolov7 you can see detect.py
when I try to run the following code:
def pixel_drop(image, drop_rate=0.5):
img_h, img_w, _ = image.shape
pixel_count = img_h * img_w
drop_num = pixel_count * drop_rate
for drop in range(int(drop_num)):
rand_x = random.randint(0, img_h - 1)
rand_y = random.randint(0, img_w - 1)
image[rand_x, rand_y,:] = 0
return image
I seem to get the following error:
TypeError: 'Tensor' object does not support item assignment
It looks like I can't assign things to a tensor. How should I go about implementing this?
This notebook has the details about how to assign values to different variables and constants.
This example assigns zeros of the appropriate shape to the tensor. But you may have a different type of variable in your code.
import tensorflow as tf
import numpy as np
def pixel_drop(image, drop_rate=0.5):
img_h, img_w, _ = image.shape
pixel_count = img_h * img_w
drop_num = pixel_count * drop_rate
for drop in range(int(drop_num)):
rand_x = np.random.randint(0, img_h - 1)
rand_y = np.random.randint(0, img_w - 1)
image[rand_x, rand_y,:].assign(tf.zeros(shape=(3,)))
return image
img_data = tf.Variable(tf.random.uniform((100, 100, 3)))
print(pixel_drop(img_data))
I am developing an object detection application using SSD model and I have defined the bounding box and the prob_threshold, when I run the code I realise that the model double count person in frame. Please see below my code
## Setting Pro_threshold for person detection filtering
try:
prob_threshold = float(os.environ['PROB_THRESHOLD'])
except:
prob_threshold = 0.4
def draw_boxes(frame, result, width, height):
"""
:Draws bounding box when person is detected on video frame
:and the probability is more than the specified threshold
"""
present_count = 0
for obj in result[0][0]:
conf = obj[2]
if conf >= prob_threshold:
xmin = int(obj[3] * width)
ymin = int(obj[4] * height)
xmax = int(obj[5] * width)
ymax = int(obj[6] * height)
cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), (0, 255, 0), 3)
present_count += 1
return frame, present_count
In order to ensure that the number of people in the video frame was not double counted I first initialise the variables and used if statement to calculate the duration spent by each person in the video frame.
## Initialise variables##
present_request_id = 0
present_count = 0
start_time = 0
last_count = 0
total_count = 0
## Calculating the duration a person spent on video#
if present_count < last_count and int(time.time() - start_time) >=1:
duration = int(time.time() - start_time)
if duration > 0:
# Publish messages to the MQTT server
client.publish("person/duration",
json.dumps({"duration": duration + lagtime}))
else:
lagtime += 1
log.warning(lagtime)
adding below argument and experimenting between the seconds, in my case I experimented between 1secs and 3sec
int(time.time() - start_time) >=1
see GitHub Repo for explanation.
I am building a oneVsAll classifier in python and using scipy.optimize.fmin_cg to get optimum value of theta vector. This is my classifier function.
def oneVsAll(X, y, K, reg_parameter):
X = np.hstack((np.ones((X.shape[0],1)), X))
theta = initialiseTheta((K,X.shape[1]))
print(theta[0].shape)
for i in range(K):
print("In for loop")
digit_class = i if i else 10
theta[i] = opt.fmin_cg(f = lrCostFunction, fprime=None, x0=theta[i], args = (X, (y==digit_class).flatten() , reg_parameter), maxiter=50)
return theta
This is my cost function
def lrCostFunction(theta, X, y, reg_parameter):
m = y.shape[0] #number of training examples
J =0
grad = np.zeros(theta.shape)
Z = np.dot(X, theta)
print("Z shape", Z.shape)
hx = sigmoid(Z)
print("hx - y shape", (hx-y).shape)
print("X shape",(X[:,0].T).shape )
print("dot product shape", np.dot(X[:,0].T, (hx -y)))
J = -(1/m)*(np.sum(np.multiply(y, np.log(hx)) + np.multiply((1-y), np.log(1-hx)))) + (reg_parameter/(2*m))*(np.sum(np.power(theta[1:], 2)))
grad_unregularized = (1/m)*(np.dot(X[:,1:].T, (hx -y)))
grad[0] = (1/m)*(np.dot(X[:,0].T, (hx -y)))
grad[1:] = grad_unregularized + (reg_parameter/m)*(theta[1:])
return (J, grad)