def augment(img_data, config, augment=True): assert 'filepath' in img_data assert 'bboxes' in img_data assert 'width' in img_data assert 'height' in img_data img_data_aug = copy.deepcopy(img_data) img = cv2.imread(img_data_aug['filepath']) if augment: rows, cols = img.shape[:2] if config.use_horizontal_flips and np.random.randint(0, 2) == 0: img = cv2.flip(img, 1) for bbox in img_data_aug['bboxes']: x1 = bbox['x1'] x2 = bbox['x2'] bbox['x2'] = cols - x1 bbox['x1'] = cols - x2 if config.use_vertical_flips and np.random.randint(0, 2) == 0: img = cv2.flip(img, 0) for bbox in img_data_aug['bboxes']: y1 = bbox['y1'] y2 = bbox['y2'] bbox['y2'] = rows - y1 bbox['y1'] = rows - y2 if config.rot_90: angle = np.random.choice([0,90,180,270],1)[0] if angle == 270: img = np.transpose(img, (1,0,2)) img = cv2.flip(img, 0) elif angle == 180: img = cv2.flip(img, -1) elif angle == 90: img = np.transpose(img, (1,0,2)) img = cv2.flip(img, 1) elif angle == 0: pass for bbox in img_data_aug['bboxes']: x1 = bbox['x1'] x2 = bbox['x2'] y1 = bbox['y1'] y2 = bbox['y2'] if angle == 270: bbox['x1'] = y1 bbox['x2'] = y2 bbox['y1'] = cols - x2 bbox['y2'] = cols - x1 elif angle == 180: bbox['x2'] = cols - x1 bbox['x1'] = cols - x2 bbox['y2'] = rows - y1 bbox['y1'] = rows - y2 elif angle == 90: bbox['x1'] = rows - y2 bbox['x2'] = rows - y1 bbox['y1'] = x1 bbox['y2'] = x2 elif angle == 0: pass img_data_aug['width'] = img.shape[1] img_data_aug['height'] = img.shape[0] return img_data_aug, img
model_classifier.load_weights(C.model_path, by_name=True)
model_rpn.compile(optimizer='sgd', loss='mse')
model_classifier.compile(optimizer='sgd', loss='mse')
all_imgs, _, _ = get_data(options.test_path)
test_imgs = [s for s in all_imgs if s['imageset'] == 'test']
T = {}
P = {}
for idx, img_data in enumerate(test_imgs):
print('{}/{}'.format(idx, len(test_imgs)))
st = time.time()
filepath = img_data['filepath']
img = cv.imread(filepath)
X, fx, fy = format_img(img, C)
if K.image_dim_ordering() == 'tf':
X = np.transpose(X, (0, 2, 3, 1))
# get the feature maps and output from the RPN
[Y1, Y2, F] = model_rpn.predict(X)
R = roi_helpers.rpn_to_roi(Y1,
Y2,
C,
K.image_dim_ordering(),
overlap_thresh=0.7)
Lgeo_range = np.linspace(4 * 1e-12, 6 * 1e-12, Lgeo_numpoints)
Cgeo_range = np.linspace(50 * 1e-12, 65 * 1e-12, Cgeo_numpoints)
Ic_range = np.linspace(20 * 1e-6, 50 * 1e-6, Ic_numpoints)
"""
Lgeo_numpoints = 1
Cgeo_numpoints = 1
Ic_numpoints = 1
Lgeo_range = np.linspace(19/2*1e-12, 19*1e-12, 1)
Cgeo_range = np.linspace(30*1e-12, 30*1e-12, 1)
Ic_range = np.linspace(23*1e-6, 23*1e-6, 1)
"""
if __name__ == "__main__" and (len(sys.argv) == 2):
argv_filepath = sys.argv[1]
actual_filepath = "%s" % argv_filepath # why did I do it this way?
raw_image_gray = cv2.imread(actual_filepath, 0)
ROI, corner_vals = user_defined_rectangle(None, raw_image=raw_image_gray)
x1 = float(corner_vals[0])
x2 = float(corner_vals[1])
y1 = float(corner_vals[2])
y2 = float(corner_vals[3])
cropped_image_to_threshold = raw_image_gray[ROI[1]:ROI[1] + ROI[3],
ROI[0]:ROI[0] + ROI[2]]
binary_image, threshold_value = user_defined_threshold(
None, cropped_image_to_threshold)
# skeletonize here
skeleton = skeletonize(binary_image)
for c in self.clipboard_get(type='image/png'):
if c == ' ':
try:
b.append(int(h, 0))
except Exception as e:
print('Exception:{}'.format(e))
h = ''
else:
h += c
except tk.TclError as e:
b = None
print('TclError:{}'.format(e))
finally:
if b is not None:
with Image.open(io.BytesIO(b)) as img:
print('{}'.format(img))
self.label.image = ImageTk.PhotoImage(
img.resize((100, 100), Image.LANCZOS))
self.label.configure(image=self.label.image)
# Main
if __name__ == '__main__':
# Read image
im = cv2.imread('zbar-test.jpg')
decodedObjects = decode(im)
display(im, decodedObjects)
import sys, opencv as cv
img = cv.imread(sys.argv[1], 1)
cv.imshow("original", img)
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
gray = cv.GaussianBlur(gray, (7, 7), 1.5)
edges = cv.Canny(gray, 0, 50)
cv.imshow("edges", edges)
cv.waitKey()
import opencv as cv2
import numpy as np
import time
imgF = cv2.imread("/home/vishav/Documents/Project/DepthCal/Data/front.png", 0)
imgR = cv2.imread("/home/vishav/Documents/Project/DepthCal/Data/rear.png", 0)
imgF = cv2.resize(imgF, (480, 720))
imgR = cv2.resize(imgR, (480, 720))
imgF = imgF[300:338, 230:279]
imgR = imgR[227:260, 289:327]
start = time.time()
dC = -50
f = 532
liF = []
for i in range(imgF.shape[0]):
for j in range(imgF.shape[1]):
if imgF[i][j] >= 180 and imgF[i][j] <= 255:
imgF[i][j] = 255
else:
imgF[i][j] = 0
liF.append(i)
fy0 = min(liF)
fy1 = max(liF)
liR = []
for i in range(imgR.shape[0]):