You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
77 lines
2.1 KiB
Python
77 lines
2.1 KiB
Python
|
|
import cv2
|
|
import numpy as np
|
|
|
|
|
|
|
|
watch_cascade = cv2.CascadeClassifier('./model/cascade.xml')
|
|
|
|
|
|
def computeSafeRegion(shape,bounding_rect):
|
|
top = bounding_rect[1] # y
|
|
bottom = bounding_rect[1] + bounding_rect[3] # y + h
|
|
left = bounding_rect[0] # x
|
|
right = bounding_rect[0] + bounding_rect[2] # x + w
|
|
|
|
min_top = 0
|
|
max_bottom = shape[0]
|
|
min_left = 0
|
|
max_right = shape[1]
|
|
|
|
# print "computeSateRegion input shape",shape
|
|
if top < min_top:
|
|
top = min_top
|
|
# print "tap top 0"
|
|
if left < min_left:
|
|
left = min_left
|
|
# print "tap left 0"
|
|
|
|
if bottom > max_bottom:
|
|
bottom = max_bottom
|
|
#print "tap max_bottom max"
|
|
if right > max_right:
|
|
right = max_right
|
|
#print "tap max_right max"
|
|
|
|
# print "corr",left,top,right,bottom
|
|
return [left,top,right-left,bottom-top]
|
|
|
|
|
|
def cropped_from_image(image,rect):
|
|
x, y, w, h = computeSafeRegion(image.shape,rect)
|
|
return image[y:y+h,x:x+w]
|
|
|
|
|
|
def detectPlateRough(image_gray,resize_h = 720,en_scale =1.08 ,top_bottom_padding_rate = 0.05):
|
|
print image_gray.shape
|
|
|
|
if top_bottom_padding_rate>0.2:
|
|
print "error:top_bottom_padding_rate > 0.2:",top_bottom_padding_rate
|
|
exit(1)
|
|
|
|
height = image_gray.shape[0]
|
|
padding = int(height*top_bottom_padding_rate)
|
|
scale = image_gray.shape[1]/float(image_gray.shape[0])
|
|
|
|
image = cv2.resize(image_gray, (int(scale*resize_h), resize_h))
|
|
|
|
image_color_cropped = image[padding:resize_h-padding,0:image_gray.shape[1]]
|
|
|
|
image_gray = cv2.cvtColor(image_color_cropped,cv2.COLOR_RGB2GRAY)
|
|
|
|
watches = watch_cascade.detectMultiScale(image_gray, en_scale, 2, minSize=(36, 9),maxSize=(36*40, 9*40))
|
|
|
|
cropped_images = []
|
|
for (x, y, w, h) in watches:
|
|
cropped_origin = cropped_from_image(image_color_cropped, (int(x), int(y), int(w), int(h)))
|
|
x -= w * 0.14
|
|
w += w * 0.28
|
|
y -= h * 0.6
|
|
h += h * 1.1;
|
|
|
|
cropped = cropped_from_image(image_color_cropped, (int(x), int(y), int(w), int(h)))
|
|
|
|
|
|
cropped_images.append([cropped,[x, y+padding, w, h],cropped_origin])
|
|
return cropped_images
|