Mardax007
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Posts posted by Mardax007
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I want to upgrade my GPU to the RTX 30 series when it's available.
Which GPU will fit(both meanings) my current pc?Of course I will upgrade the PSU too.
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Have you tried other ram. Maybe you can borrow it from a other pc?
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Have you done a RAM reseat?
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When did this problem accure? Was it like that the first time that you ran it?
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Edge and Discord are the ones using the most memory:
1. Use chrome or firefox
2. What are you doing on Discord and Edge? How many tabs do you have open on Edge and what websites?
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2 minutes ago, TofuHaroto said:
What parts do you already have exactly ? And what workload?
20 minutes ago, lavishly said:game
He plays games
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I recoment a the msi b450 tomahawk max instead of your b450m.
Also the ryzen 7 2700x is 10$ extra and gives you a little more power.
Pcpartpicker has for some reason removed these products removed from there list but you can find them on amazon or newegg.
In total all of this saves you 8$ and costs you 10$ so for 2$ extra you get a quite a bit more pc.
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1. WINDOWS 7!?!?!?!?!?!?!?
2. Have you tryed a other screen? If it is still yellow-ish it is the pc else your monitor is 1.broken 2. has a setting enabled like night mode. If the new screen doesn't fix it I don't know how you can fix it in windows.
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I think that if you go for a new system with DDR4-3200 Mhz memory, Ryzen cpu and a b450 motherboard the performance leap will be quite big but it isn't 100% the memory the cpu and motherboard are I think bottlenecking the gpu. I don't know how mutch the memory is bottlenecking but a entire system upgrade(apart from the graficscard) will be worth it. A year a go I went from a 7 year old prebuild to a 800$ new pc and I went from 10 to 60 fps to 100 to 440 fps in minecraft. I think that if you upgrade your gpu can sine and your performance will be better.
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29 minutes ago, Stoigeboiii said:
Are there any benefits to running a game on 3990x, 3970x, 3960x, 3950x, vs 3900x? What if they were streaming how would it change for each CPU then.
This is because my friends works at a media company that has threadripper PC and plays games during lunch. Just wanted to know what the expected difference is (maybe assume using a 2080 ti)
Buy a ryzen and spent the extra money on memory, a higherend graficscard, storage or the best choice RGB!!!
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24 minutes ago, OmarJO said:
So In Nov. 2017 I purchased and fixed my current PC with the following specs:
CORSAIR Vengeance RGB DRAM 16GB (2 x 8GB) 288-Pin DDR4 3200 (PC4 25600)
EVGA SuperNOVA 850 G2 220-G2-0850-XR 80+ GOLD 850W Fully Modular EVGA
SAMSUNG 850 EVO M.2 2280 500GB SATA III 3D NAND Internal SSD Single Unit Version MZ-N5E500BW
4TB HDD Seagate Barracuda
Intel Core i5-8600K Coffee Lake 6-Core 3.6 GHz (4.3 GHz Turbo) LGA 1151
In 2019 I got the 2070 Super
A few days ago I sold my I5 CPU to upgrade to I9 9900k (As I do streaming and wanted a better quality stream) after checking the MoBo compatibility I went and got it,
When I plugged it in my MoBo my BIOS detected a new CPU installed and requested to go to settings and I did (CPU was detected and had a Temp of 28c) so all was good and I did my first boot,
This is where the problem started, Right before login screen BSOD appeared with the following error: rtwlanu.sysI tried to look for the error and couldn’t find anything (Other than its Realtek WLAN driver error), so I so someone posting a similar problem and they say when you install new CPU its always recommended to reinstall windows as some of the drivers won't work,
Did my new fresh windows installation and I had the same problem right after the first reboot and now Im stuck with the (The computer restarted unexpectedly or encountered an unexpected error windows installation cannot)
Any idea what could be the issue, Im pretty sure it is related to the Mobo drivers but how to fix this?
Just a BIOS update should fix it
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Doing some training. HoG is has really fast training. Going thru 6200 photo's in 30 minutes. Before it was 7 hours!
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I have done it. I needed to set the methode to HoG is stead of CNN. Now it is blazing fast! Full code coming soon.
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On 6/14/2020 at 2:17 PM, Mardax007 said:
Thanks for the face detection code but it already had that. I was looking for a face recognition program with deep learning. And I found it yesterday. But there is one problem:
# python recognize_faces_image.py --encodings encodings.pickle --image examples/example_01.png # import the necessary packages import face_recognition import argparse import pickle import cv2 # construct the argument parser and parse the arguments ap = argparse.ArgumentParser() ap.add_argument("-e", "--encodings", required=True, help="path to serialized db of facial encodings") ap.add_argument("-i", "--image", required=True, help="path to input image") ap.add_argument("-d", "--detection-method", type=str, default="cnn", help="face detection model to use: either `hog` or `cnn`") args = vars(ap.parse_args()) # load the known faces and embeddings print("[INFO] loading encodings...") data = pickle.loads(open(args["encodings"], "rb").read()) # load the input image and convert it from BGR to RGB image = cv2.imread(args["image"]) rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # detect the (x, y)-coordinates of the bounding boxes corresponding # to each face in the input image, then compute the facial embeddings # for each face print("[INFO] recognizing faces...") boxes = face_recognition.face_locations(rgb, model=args["detection_method"]) encodings = face_recognition.face_encodings(rgb, boxes) # initialize the list of names for each face detected names = [] # loop over the facial embeddings for encoding in encodings: # attempt to match each face in the input image to our known # encodings matches = face_recognition.compare_faces(data["encodings"], encoding) name = "Unknown" # check to see if we have found a match if True in matches: # find the indexes of all matched faces then initialize a # dictionary to count the total number of times each face # was matched matchedIdxs = [i for (i, b) in enumerate(matches) if b] counts = {} # loop over the matched indexes and maintain a count for # each recognized face face for i in matchedIdxs: name = data["names"][i] counts[name] = counts.get(name, 0) + 1 # determine the recognized face with the largest number of # votes (note: in the event of an unlikely tie Python will # select first entry in the dictionary) name = max(counts, key=counts.get) # update the list of names names.append(name) # loop over the recognized faces for ((top, right, bottom, left), name) in zip(boxes, names): # draw the predicted face name on the image cv2.rectangle(image, (left, top), (right, bottom), (0, 255, 0), 2) y = top - 15 if top - 15 > 15 else top + 15 cv2.putText(image, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 2) # show the output image cv2.imshow("Image", image) cv2.waitKey(0)
Line 32 where boxes is defined takes 28 seconds to define and that's way to long. Is there any way to define it outside the function or make the process faster?
My and goal is this: The camera sees a face( an other program), it calls the id function, the id function takes a picture and quickly1 ids the person. (There is more but that's irrelevant)
1.quickly = 2 to 10 seconds
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On 6/12/2020 at 10:43 AM, MonkeyPants said:
I've only heard good things about Google TensorFlow.
It has an easy API and is probably the most trained model.Yes me to. Haven't figured out what is exactly out what it is but as said above I already have a face detection and id programs. Thanks for the tip!
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Thanks for the face detection code but it already had that. I was looking for a face recognition program with deep learning. And I found it yesterday. But there is one problem:
# python recognize_faces_image.py --encodings encodings.pickle --image examples/example_01.png # import the necessary packages import face_recognition import argparse import pickle import cv2 # construct the argument parser and parse the arguments ap = argparse.ArgumentParser() ap.add_argument("-e", "--encodings", required=True, help="path to serialized db of facial encodings") ap.add_argument("-i", "--image", required=True, help="path to input image") ap.add_argument("-d", "--detection-method", type=str, default="cnn", help="face detection model to use: either `hog` or `cnn`") args = vars(ap.parse_args()) # load the known faces and embeddings print("[INFO] loading encodings...") data = pickle.loads(open(args["encodings"], "rb").read()) # load the input image and convert it from BGR to RGB image = cv2.imread(args["image"]) rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # detect the (x, y)-coordinates of the bounding boxes corresponding # to each face in the input image, then compute the facial embeddings # for each face print("[INFO] recognizing faces...") boxes = face_recognition.face_locations(rgb, model=args["detection_method"]) encodings = face_recognition.face_encodings(rgb, boxes) # initialize the list of names for each face detected names = [] # loop over the facial embeddings for encoding in encodings: # attempt to match each face in the input image to our known # encodings matches = face_recognition.compare_faces(data["encodings"], encoding) name = "Unknown" # check to see if we have found a match if True in matches: # find the indexes of all matched faces then initialize a # dictionary to count the total number of times each face # was matched matchedIdxs = [i for (i, b) in enumerate(matches) if b] counts = {} # loop over the matched indexes and maintain a count for # each recognized face face for i in matchedIdxs: name = data["names"][i] counts[name] = counts.get(name, 0) + 1 # determine the recognized face with the largest number of # votes (note: in the event of an unlikely tie Python will # select first entry in the dictionary) name = max(counts, key=counts.get) # update the list of names names.append(name) # loop over the recognized faces for ((top, right, bottom, left), name) in zip(boxes, names): # draw the predicted face name on the image cv2.rectangle(image, (left, top), (right, bottom), (0, 255, 0), 2) y = top - 15 if top - 15 > 15 else top + 15 cv2.putText(image, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 2) # show the output image cv2.imshow("Image", image) cv2.waitKey(0)
Line 32 where boxes is defined takes 28 seconds to define and that's way to long. Is there any way to define it outside the function or make the process faster?
My and goal is this: The camera sees a face( an other program), it calls the id function, the id function takes a picture and quickly1 ids the person. (There is more but that's irrelevant)
1.quickly = 2 to 10 seconds
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Hi, I am building a face recognition program in python3.
I started with getting a face id program.
Then I added blinkdetection to insure that its a living person and not a photo.
Then I added a face detection model.
Then I came back to the face recognition part.
Turns out that 60% meens 60% unsure.
Now I am looking for a better face recognition program.
I heard that a face recognition program with deep learning will increase accurcy a lot.
I have googled it a lot but I still can't find a working program.
If someone has a solution/tip please comment below.
Thanks!
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5 minutes ago, Fasauceome said:
there are parts that don't have price data, are you not buying from any local retailers?
if not:
Made some adjustments. Much better PSU, better and cheaper CPU, better ram, better motherboard, and better and cheaper GPU. Not super sure whatto do about the case and SSD.
I advice more ssd storage like 240GB
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Here they made a custom monitor is that what you want to do?
I need a new phone.
in LTT Releases
Posted
With a mous case!