def main():
img = read_img('./grace_hopper.png')
# Feature Detection
if not os.path.exists("./feature_detection"):
os.makedirs("./feature_detection")
# -- TODO Task 5: Corner Score --
# (a): Complete corner_score()
# (b)
# Define offsets and window size and calulcate corner score
u, v, W = 5, 0, (5, 5)
score = corner_score(img, u, v, W)
save_img(score, "./feature_detection/corner_score.png")
#breakpoint()
# (c): No Code
# -- TODO Task 6: Harris Corner Detector --
# (a): Complete harris_detector()
# (b)
harris_corners = harris_detector(img)
save_img(harris_corners, "./feature_detection/harris_response.png")
def p2(p1, p2, savename):
# read left and right images
imgleft = read_img(p1)
imgright = read_img(p2)
# stitch image
output = stitchimage(imgleft, imgright)
# save stitched image
save_img(output, './{}.jpg'.format(savename))
def LoginCodeVerificatin(driver):
try:
detailCode = pq(driver.page_source)
imageURL = detailCode.find('#J_CheckCodeImg1').attr('src')
save_img(imageURL, 'picDic/TaoBaoPicDic.png')
driver.find_element_by_xpath('//*[@id="J_CodeInput"]').clear()
# 设置要请求的头，让服务器不会以为你是机器人
headers = {
'UserAgent':
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.101 Safari/537.36'
}
f = open('picDic/TaoBaoPicDic.png',
'rb') # 二进制打开图文件 CrawlResult/1111.png
ls_f = base64.b64encode(f.read()) # 读取文件内容，转换为base64编码
f.close()
values = {
"softwareId": 7616,
"softwareSecret": "p2AXUYMaTDcV72UoULYQQt7ubVPwTUXXlXIw7A3S",
"username": "******",
"password": "******",
"captchaData": ls_f,
"captchaType": 1017,
"captchaMinLength": 4,
"captchaMaxLength": 8
} # @ZHOUZEnan1993
data = json.dumps(values)
# 发送一个http请求
request = urllib2.Request(url=url, headers=headers, data=data)
# 获得回送的数据
response = urllib2.urlopen(request)
datas = eval(response.read())
print 'data---%s---%s--%s--%s' % (datas['code'], datas['message'],
datas['data']['recognition'],
datas['data']['captchaId'])
code = str(datas['data']['recognition']).replace('\r\n', '').replace(
' ', '').replace('\n', '').replace('\t', '')
driver.find_element_by_xpath('//*[@id="J_CodeInput"]').send_keys(code)
time.sleep(random.uniform(5, 8))
driver.find_element_by_xpath('//*[@id="J_submit"]').click()
time.sleep(random.uniform(5, 8))
print '测试结束************'
driver.switch_to.default_content()
except Exception as e:
print '验证码登录错了子卡-------%s' % e
def main():
# The main function
########################
img = read_img('./grace_hopper.png')
##### Feature Detection #####
if not os.path.exists("./feature_detection"):
os.makedirs("./feature_detection")
# define offsets and window size and calulcate corner score
u, v, W = 0, 2, (5,5)
score = corner_score(img, u, v, W)
save_img(score, "./feature_detection/corner_score.png")
harris_corners = harris_detector(img)
save_img(harris_corners, "./feature_detection/harris_response.png")
def draw_matches(img1, img2, kp1, kp2, matches):
'''
Creates an output image where the two source images stacked vertically
connecting matching keypoints with a line.
Input - img1: Input image 1 of shape (H1,W1,3)
img2: Input image 2 of shape (H2,W2,3)
kp1: Keypoint matrix for image 1 of shape (N,4)
kp2: Keypoint matrix for image 2 of shape (M,4)
matches: List of matching pairs indices between the 2 sets of
keypoints (K,2)
Output - Image where 2 input images stacked vertically with lines joining
the matched keypoints
Hint: see cv2.line
'''
#Hint:
#Use common.get_match_points() to extract keypoint locations
#breakpoint()
match_points = common.get_match_points(kp1, kp2, matches).astype(np.int32)
test_image = np.vstack((img1, img2))
color = (0, 255, 0)
thickness = 1
H1 = img1.shape[0]
#breakpoint()
#match_points.shape[0]
for i in range(match_points.shape[0]):
start_point = (match_points[i, 0], match_points[i, 1])
end_point = (match_points[i, 2], match_points[i, 3] + H1)
test_image = cv2.line(test_image, start_point, end_point, color,
thickness)
output = test_image
file_name = 'draw_matches1.png'
save_img(output, file_name)
#plt.imshow(output)
#plt.show()
#cv2.imshow(window_name, output)
#cv2.waitKey(0)
return output
def main():
# The main function
img = read_img('./grace_hopper.png')
""" Image Patches """
if not os.path.exists("./image_patches"):
os.makedirs("./image_patches")
# -- Image Patches --
# Q1
# patches = image_patches(img)
# TODO choose a few patches and save them
# chosen_patches = patches[:3]
# save_img(chosen_patches[0], "./image_patches/q1_patch1.png")
# save_img(chosen_patches[1], "./image_patches/q1_patch2.png")
# save_img(chosen_patches[2], "./image_patches/q1_patch3.png")
# Q2: No code
""" Gaussian Filter """
# if not os.path.exists("./gaussian_filter"):
# os.makedirs("./gaussian_filter")
# -- Gaussian Filter --
# Q1: No code
# Q2
# TODO: Calculate the kernel described in the question.
# There is tolerance for the kernel.
va = 1 / (2 * np.log(2))
k = np.zeros((3, 3))
for i in range(3):
for j in range(3):
k[i, j] = 1 / (2 * np.pi * va) * np.exp(-((i - 1)**2 +
(j - 1)**2) / (2 * va))
filtered_gaussian = convolve(img, k)
save_img(filtered_gaussian, "./gaussian_filter/q2_gaussian.png")
# # Q3
edge_detect, _, _ = edge_detection(img)
save_img(edge_detect, "./gaussian_filter/q3_edge.png")
edge_with_gaussian, _, _ = edge_detection(filtered_gaussian)
save_img(edge_with_gaussian, "./gaussian_filter/q3_edge_gaussian.png")
print("Gaussian Filter is done. ")
#breakpoint()
output = cv2.warpPerspective(img,
H, (output_w, output_h),
flags=cv2.INTER_LINEAR)
#breakpoint()
return output
if __name__ == "__main__":
# Task 5
case_name = "threebody"
I = read_img(os.path.join("task5", case_name, "book.jpg"))
corners = np.load(os.path.join("task5", case_name, "corners.npy"))
size = np.load(os.path.join("task5", case_name, "size.npy"))
result = make_synthetic_view(I, corners, size)
save_img(result, case_name + "_frontoparallel.jpg")
case_name = "palmer"
I = read_img(os.path.join("task5", case_name, "book.jpg"))
corners = np.load(os.path.join("task5", case_name, "corners.npy"))
size = np.load(os.path.join("task5", case_name, "size.npy"))
result = make_synthetic_view(I, corners, size)
save_img(result, case_name + "_frontoparallel.jpg")
#breakpoint()
draw_matches(img1, img2, keypoints1, keypoints2, matches)
test = keypoints1[matches[:, [0]], [0]]
XY = np.hstack((keypoints1[matches[:, [0]],
[0]], keypoints1[matches[:, [0]], [1]]))
XY = np.hstack((XY, keypoints2[matches[:, [1]], [0]]))
XY = np.hstack((XY, keypoints2[matches[:, [1]], [1]]))
H = RANSAC_fit_homography(XY)
#breakpoint()
stitched = warp_and_combine(img1, img2, H, XY)
return stitched
if __name__ == "__main__":
#Possible starter code; you might want to loop over the task 6 images
#to_stitch = 'eynsham'
#to_stitch = 'mertoncourtyard'
to_stitch_list = [
'eynsham', 'florence2', 'florence3', 'florence3_alt', 'lowetag',
'mertonchapel', 'mertoncourtyard', 'vgg'
]
#to_stitch_list = ['eynsham', 'mertoncourtyard']
for to_stitch in to_stitch_list:
I1 = read_img(os.path.join('task6', to_stitch, 'p1.jpg'))
I2 = read_img(os.path.join('task6', to_stitch, 'p2.jpg'))
res = make_warped(I1, I2)
save_img(res, "result_" + to_stitch + ".jpg")
print("finished:", to_stitch)
def main():
# The main function
img = read_img('./grace_hopper.png')
""" Image Patches """
if not os.path.exists("./image_patches"):
os.makedirs("./image_patches")
# -- TODO Task 1: Image Patches --
#(a)
#First complete image_patches()
patches = image_patches(img)
# Now choose any three patches and save them
# chosen_patches should have those patches stacked vertically/horizontally
chosen_patches = patches[0]
chosen_patches = np.vstack((chosen_patches, patches[1]))
chosen_patches = np.vstack((chosen_patches, patches[2]))
save_img(chosen_patches, "./image_patches/q1_patch.png")
#(b), (c): No code
""" Convolution and Gaussian Filter """
if not os.path.exists("./gaussian_filter"):
os.makedirs("./gaussian_filter")
#breakpoint()
# -- TODO Task 2: Convolution and Gaussian Filter --
# (a): No code
# (b): Complete convolve()
# (c)
# Calculate the Gaussian kernel described in the question.
# There is tolerance for the kernel.
sigma = 0.572
#sigma = 2.0
kernel_gaussian = gaussian_kernel_generator(sigma)
filtered_gaussian = convolve(img, kernel_gaussian)
save_img(filtered_gaussian, "./gaussian_filter/q2_gaussian.png")
#breakpoint()
#
# (d), (e): No code
# (f): Complete edge_detection()
# (g)
# Use edge_detection() to detect edges
# for the orignal and gaussian filtered images.
_, _, edge_detect = edge_detection(img)
save_img(edge_detect, "./gaussian_filter/q3_edge.png")
_, _, edge_with_gaussian = edge_detection(filtered_gaussian)
save_img(edge_with_gaussian, "./gaussian_filter/q3_edge_gaussian.png")
print("Gaussian Filter is done. ")
#breakpoint()
# -- TODO Task 3: Sobel Operator --
if not os.path.exists("./sobel_operator"):
os.makedirs("./sobel_operator")
# (a): No code
# (b): Complete sobel_operator()
# (c)
Gx, Gy, edge_sobel = sobel_operator(img)
save_img(Gx, "./sobel_operator/q2_Gx.png")
save_img(Gy, "./sobel_operator/q2_Gy.png")
save_img(edge_sobel, "./sobel_operator/q2_edge_sobel.png")
print("Sobel Operator is done. ")
#breakpoint()
# -- TODO Task 4: LoG Filter --
if not os.path.exists("./log_filter"):
os.makedirs("./log_filter")
# (a)
kernel_LoG1 = np.array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
kernel_LoG2 = np.array([[0, 0, 3, 2, 2, 2, 3, 0, 0],
[0, 2, 3, 5, 5, 5, 3, 2, 0],
[3, 3, 5, 3, 0, 3, 5, 3, 3],
[2, 5, 3, -12, -23, -12, 3, 5, 2],
[2, 5, 0, -23, -40, -23, 0, 5, 2],
[2, 5, 3, -12, -23, -12, 3, 5, 2],
[3, 3, 5, 3, 0, 3, 5, 3, 3],
[0, 2, 3, 5, 5, 5, 3, 2, 0],
[0, 0, 3, 2, 2, 2, 3, 0, 0]])
#breakpoint()
filtered_LoG1 = convolve(img, kernel_LoG1)
filtered_LoG2 = convolve(img, kernel_LoG2)
# Use convolve() to convolve img with kernel_LOG1 and kernel_LOG2
save_img(filtered_LoG1, "./log_filter/q1_LoG1.png")
save_img(filtered_LoG2, "./log_filter/q1_LoG2.png")
# (b)
# Follow instructions in pdf to approximate LoG with a DoG
data = np.load('log1d.npz')
LoG_50 = data['log50']
gauss_50 = data['gauss50']
gauss_53 = data['gauss53']
DoG = gauss_53 - gauss_50
x = np.arange(-250, 251)
plt.plot(x, LoG_50, label='Laplacian')
plt.plot(x, DoG, label='DoG')
plt.legend()
plt.show()
#breakpoint()
print("LoG Filter is done. ")