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from infer_detector import Infer | |
gtf = Infer() | |
gtf.Model(model_name="mobilenet", weights="weights/Final_RFB_mobile_COCO.pth", use_gpu=True) | |
gtf.Image_Params(class_file, input_size=300) | |
gtf.Setup() | |
output = gtf.Predict(img_path, thresh=0.195, font_size=1, line_size=3) |
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from train_detector import Detector | |
gtf = Detector() | |
gtf.Train_Dataset(root_dir, coco_dir, img_dir, batch_size=32,image_size=300, num_workers=3) | |
gtf.Model(model_name="mobilenet", use_gpu=True, ngpu=1) | |
gtf.Set_HyperParams(lr=0.0001, momentum=0.9, weight_decay=0.0005, gamma=0.1, jaccard_threshold=0.5) | |
gtf.Train(epochs=10, log_iters=True, output_weights_dir="weights", saved_epoch_interval=10) |
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import numpy as np | |
import cv2 | |
from matplotlib import pyplot as plt | |
img = cv2.imread("imgs/chapter5/sudoku.png", 0); | |
kernel = [ | |
[1, 0, -1], | |
[1, 0, -1], | |
[1, 0, -1] | |
] |
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import numpy as np | |
import cv2 | |
from matplotlib import pyplot as plt | |
img = cv2.imread("imgs/chapter5/sudoku.png", 0); | |
kernel = [ | |
[1, 1, 1], | |
[0, 0, 0], | |
[-1, -1, -1] | |
] |
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# Gabor filter in x direction - Black to white gradient | |
#Theta - 0 degree | |
%matplotlib inline | |
import numpy as np | |
import cv2 | |
from matplotlib import pyplot as plt | |
img = cv2.imread("imgs/chapter5/chess_slant.jpg", 0); | |
pi = 3.14; |
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import numpy as np | |
import cv2 | |
import skimage | |
from matplotlib import pyplot as plt | |
img = cv2.imread("imgs/chapter5/sudoku.png", 0); | |
img = cv2.blur(img, (3, 3)); | |
###################################FOCUS####################################### | |
robert_filter = skimage.filters.hessian(img, sigmas=range(1, 2, 1)) # Image binarization |
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import numpy as np | |
import cv2 | |
from matplotlib import pyplot as plt | |
img = cv2.imread("imgs/chapter5/sudoku.png", 0); | |
img = cv2.blur(img, (3, 3)); | |
laplacian = cv2.Laplacian(img,cv2.CV_8UC1) #Binarized | |
f = plt.figure(figsize=(15,15)) | |
f.add_subplot(1, 2, 1).set_title('Original Image'); | |
plt.imshow(img, cmap = "gray") | |
f.add_subplot(1, 2, 2).set_title('Filtered Image'); |
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import numpy as np | |
import cv2 | |
from matplotlib import pyplot as plt | |
img = cv2.imread("imgs/chapter5/text2.png", 0); | |
#img = cv2.blur(img, (3, 3)); | |
# global thresholding | |
ret1,th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY) |
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import numpy as np | |
import cv2 | |
from matplotlib import pyplot as plt | |
img = cv2.imread("imgs/chapter5/text.png", 0); | |
#img = cv2.blur(img, (3, 3)) | |
# threshold -> 127 | |
# maxval -> 255 | |
# Output: |
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import numpy as np | |
import cv2 | |
from matplotlib import pyplot as plt | |
img = cv2.imread("imgs/chapter5/text2.png", 0); | |
#img = cv2.blur(img, (3, 3)) | |
# threshold -> 127 | |
# maxval -> 255 | |
# Output: |
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