How to fix IndexError: invalid index to scalar variable
Question:
This code generates error:
IndexError: invalid index to scalar variable.
at the line: results.append(RMSPE(np.expm1(y_train[testcv]), [y[1] for y in y_test]))
How to fix it?
import pandas as pd
import numpy as np
from sklearn import ensemble
from sklearn import cross_validation
def ToWeight(y):
w = np.zeros(y.shape, dtype=float)
ind = y != 0
w[ind] = 1./(y[ind]**2)
return w
def RMSPE(y, yhat):
w = ToWeight(y)
rmspe = np.sqrt(np.mean( w * (y - yhat)**2 ))
return rmspe
forest = ensemble.RandomForestRegressor(n_estimators=10, min_samples_split=2, n_jobs=-1)
print ("Cross validations")
cv = cross_validation.KFold(len(train), n_folds=5)
results = []
for traincv, testcv in cv:
y_test = np.expm1(forest.fit(X_train[traincv], y_train[traincv]).predict(X_train[testcv]))
results.append(RMSPE(np.expm1(y_train[testcv]), [y[1] for y in y_test]))
testcv is:
[False False False ..., True True True]
Answers:
You are trying to index into a scalar (non-iterable) value:
[y[1] for y in y_test]
# ^ this is the problem
When you call [y for y in test] you are iterating over the values already, so you get a single value in y.
Your code is the same as trying to do the following:
y_test = [1, 2, 3]
y = y_test[0] # y = 1
print(y[0]) # this line will fail
I’m not sure what you’re trying to get into your results array, but you need to get rid of [y[1] for y in y_test].
If you want to append each y in y_test to results, you’ll need to expand your list comprehension out further to something like this:
[results.append(..., y) for y in y_test]
Or just use a for loop:
for y in y_test:
results.append(..., y)
Basically, 1 is not a valid index of y. If the visitor is coming from his own code he should check if his y contains the index which he tries to access (in this case the index is 1).
In the for, you have an iteration, then for each element of that loop which probably is a scalar, has no index. When each element is an empty array, single variable, or scalar and not a list or array you cannot use indices.
YOLO Object Detection
layer_names = net.getLayerNames() output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
Don’t need to indexing i in layer_names[i[0] – 1] . Just remove it and do layer_names[i – 1]
layer_names = net.getLayerNames() output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]
It Work For Me
Editing the yolo_video.py file in repo is required for those who are using darknet code.`This file works, replaced with required edits
# import the necessary packages
import numpy as np
import argparse
import imutils
import time
import cv2
import os
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--input", required=True,
help="path to input video")
ap.add_argument("-o", "--output", required=True,
help="path to output video")
ap.add_argument("-y", "--yolo", required=True,
help="base path to YOLO directory")
ap.add_argument("-c", "--confidence", type=float, default=0.5,
help="minimum probability to filter weak detections")
ap.add_argument("-t", "--threshold", type=float, default=0.3,
help="threshold when applyong non-maxima suppression")
args = vars(ap.parse_args())
# load the COCO class labels our YOLO model was trained on
labelsPath = os.path.sep.join([args["yolo"], "biscuits.names"])
LABELS = open(labelsPath).read().strip().split("n")
# initialize a list of colors to represent each possible class label
np.random.seed(42)
COLORS = np.random.randint(0, 255, size=(len(LABELS), 3),
dtype="uint8")
# derive the paths to the YOLO weights and model configuration
weightsPath = os.path.sep.join([args["yolo"], "yolov4-custom_best.weights"])
configPath = os.path.sep.join([args["yolo"], "yolov4-custom.cfg"])
# load our YOLO object detector trained on COCO dataset (80 classes)
# and determine only the *output* layer names that we need from YOLO
print("[INFO] loading YOLO from disk...")
net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)
ln = net.getLayerNames()
print("ln",net)
ln = [ln[i - 1] for i in net.getUnconnectedOutLayers()]
# initialize the video stream, pointer to output video file, and
# frame dimensions
vs = cv2.VideoCapture(args["input"])
writer = None
(W, H) = (None, None)
# try to determine the total number of frames in the video file
try:
prop = cv2.cv.CV_CAP_PROP_FRAME_COUNT if imutils.is_cv2()
else cv2.CAP_PROP_FRAME_COUNT
total = int(vs.get(prop))
print("[INFO] {} total frames in video".format(total))
# an error occurred while trying to determine the total
# number of frames in the video file
except:
print("[INFO] could not determine # of frames in video")
print("[INFO] no approx. completion time can be provided")
total = -1
# loop over frames from the video file stream
while True:
# read the next frame from the file
(grabbed, frame) = vs.read()
# if the frame was not grabbed, then we have reached the end
# of the stream
if not grabbed:
break
# if the frame dimensions are empty, grab them
if W is None or H is None:
(H, W) = frame.shape[:2]
# construct a blob from the input frame and then perform a forward
# pass of the YOLO object detector, giving us our bounding boxes
# and associated probabilities
blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416),
swapRB=True, crop=False)
net.setInput(blob)
start = time.time()
layerOutputs = net.forward(ln)
end = time.time()
# 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]
# filter out weak predictions by ensuring the detected
# probability is greater than the minimum probability
if confidence > args["confidence"]:
# 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, args["confidence"],
args["threshold"])
# 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])
# draw a bounding box rectangle and label on the frame
color = [int(c) for c in COLORS[classIDs[i]]]
cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
text = "{}: {:.4f}".format(LABELS[classIDs[i]],
confidences[i])
cv2.putText(frame, text, (x, y - 5),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
# check if the video writer is None
if writer is None:
# initialize our video writer
fourcc = cv2.VideoWriter_fourcc(*"MJPG")
writer = cv2.VideoWriter(args["output"], fourcc, 30,
(frame.shape[1], frame.shape[0]), True)
# some information on processing single frame
if total > 0:
elap = (end - start)
print("[INFO] single frame took {:.4f} seconds".format(elap))
print("[INFO] estimated total time to finish: {:.4f}".format(
elap * total))
# write the output frame to disk
writer.write(frame)
# release the file pointers
print("[INFO] cleaning up...")
writer.release()
vs.release()`
YOLO Object Detection
python <= 3.7
ln = net.getLayerNames()
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
python >3.7
ln = net.getLayerNames()
ln = [ln[i - 1] for i in net.getUnconnectedOutLayers()]
This code generates error:
IndexError: invalid index to scalar variable.
at the line: results.append(RMSPE(np.expm1(y_train[testcv]), [y[1] for y in y_test]))
How to fix it?
import pandas as pd
import numpy as np
from sklearn import ensemble
from sklearn import cross_validation
def ToWeight(y):
w = np.zeros(y.shape, dtype=float)
ind = y != 0
w[ind] = 1./(y[ind]**2)
return w
def RMSPE(y, yhat):
w = ToWeight(y)
rmspe = np.sqrt(np.mean( w * (y - yhat)**2 ))
return rmspe
forest = ensemble.RandomForestRegressor(n_estimators=10, min_samples_split=2, n_jobs=-1)
print ("Cross validations")
cv = cross_validation.KFold(len(train), n_folds=5)
results = []
for traincv, testcv in cv:
y_test = np.expm1(forest.fit(X_train[traincv], y_train[traincv]).predict(X_train[testcv]))
results.append(RMSPE(np.expm1(y_train[testcv]), [y[1] for y in y_test]))
testcv is:
[False False False ..., True True True]
You are trying to index into a scalar (non-iterable) value:
[y[1] for y in y_test]
# ^ this is the problem
When you call [y for y in test] you are iterating over the values already, so you get a single value in y.
Your code is the same as trying to do the following:
y_test = [1, 2, 3]
y = y_test[0] # y = 1
print(y[0]) # this line will fail
I’m not sure what you’re trying to get into your results array, but you need to get rid of [y[1] for y in y_test].
If you want to append each y in y_test to results, you’ll need to expand your list comprehension out further to something like this:
[results.append(..., y) for y in y_test]
Or just use a for loop:
for y in y_test:
results.append(..., y)
Basically, 1 is not a valid index of y. If the visitor is coming from his own code he should check if his y contains the index which he tries to access (in this case the index is 1).
In the for, you have an iteration, then for each element of that loop which probably is a scalar, has no index. When each element is an empty array, single variable, or scalar and not a list or array you cannot use indices.
YOLO Object Detection
layer_names = net.getLayerNames() output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
Don’t need to indexing i in layer_names[i[0] – 1] . Just remove it and do layer_names[i – 1]
layer_names = net.getLayerNames() output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]
It Work For Me
Editing the yolo_video.py file in repo is required for those who are using darknet code.`This file works, replaced with required edits
# import the necessary packages
import numpy as np
import argparse
import imutils
import time
import cv2
import os
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--input", required=True,
help="path to input video")
ap.add_argument("-o", "--output", required=True,
help="path to output video")
ap.add_argument("-y", "--yolo", required=True,
help="base path to YOLO directory")
ap.add_argument("-c", "--confidence", type=float, default=0.5,
help="minimum probability to filter weak detections")
ap.add_argument("-t", "--threshold", type=float, default=0.3,
help="threshold when applyong non-maxima suppression")
args = vars(ap.parse_args())
# load the COCO class labels our YOLO model was trained on
labelsPath = os.path.sep.join([args["yolo"], "biscuits.names"])
LABELS = open(labelsPath).read().strip().split("n")
# initialize a list of colors to represent each possible class label
np.random.seed(42)
COLORS = np.random.randint(0, 255, size=(len(LABELS), 3),
dtype="uint8")
# derive the paths to the YOLO weights and model configuration
weightsPath = os.path.sep.join([args["yolo"], "yolov4-custom_best.weights"])
configPath = os.path.sep.join([args["yolo"], "yolov4-custom.cfg"])
# load our YOLO object detector trained on COCO dataset (80 classes)
# and determine only the *output* layer names that we need from YOLO
print("[INFO] loading YOLO from disk...")
net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)
ln = net.getLayerNames()
print("ln",net)
ln = [ln[i - 1] for i in net.getUnconnectedOutLayers()]
# initialize the video stream, pointer to output video file, and
# frame dimensions
vs = cv2.VideoCapture(args["input"])
writer = None
(W, H) = (None, None)
# try to determine the total number of frames in the video file
try:
prop = cv2.cv.CV_CAP_PROP_FRAME_COUNT if imutils.is_cv2()
else cv2.CAP_PROP_FRAME_COUNT
total = int(vs.get(prop))
print("[INFO] {} total frames in video".format(total))
# an error occurred while trying to determine the total
# number of frames in the video file
except:
print("[INFO] could not determine # of frames in video")
print("[INFO] no approx. completion time can be provided")
total = -1
# loop over frames from the video file stream
while True:
# read the next frame from the file
(grabbed, frame) = vs.read()
# if the frame was not grabbed, then we have reached the end
# of the stream
if not grabbed:
break
# if the frame dimensions are empty, grab them
if W is None or H is None:
(H, W) = frame.shape[:2]
# construct a blob from the input frame and then perform a forward
# pass of the YOLO object detector, giving us our bounding boxes
# and associated probabilities
blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416),
swapRB=True, crop=False)
net.setInput(blob)
start = time.time()
layerOutputs = net.forward(ln)
end = time.time()
# 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]
# filter out weak predictions by ensuring the detected
# probability is greater than the minimum probability
if confidence > args["confidence"]:
# 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, args["confidence"],
args["threshold"])
# 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])
# draw a bounding box rectangle and label on the frame
color = [int(c) for c in COLORS[classIDs[i]]]
cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
text = "{}: {:.4f}".format(LABELS[classIDs[i]],
confidences[i])
cv2.putText(frame, text, (x, y - 5),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
# check if the video writer is None
if writer is None:
# initialize our video writer
fourcc = cv2.VideoWriter_fourcc(*"MJPG")
writer = cv2.VideoWriter(args["output"], fourcc, 30,
(frame.shape[1], frame.shape[0]), True)
# some information on processing single frame
if total > 0:
elap = (end - start)
print("[INFO] single frame took {:.4f} seconds".format(elap))
print("[INFO] estimated total time to finish: {:.4f}".format(
elap * total))
# write the output frame to disk
writer.write(frame)
# release the file pointers
print("[INFO] cleaning up...")
writer.release()
vs.release()`
YOLO Object Detection
python <= 3.7
ln = net.getLayerNames()
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
python >3.7
ln = net.getLayerNames()
ln = [ln[i - 1] for i in net.getUnconnectedOutLayers()]