def main():
global img_height, img_width
# Read in image using the imread function
img = cv2.imread('./phoneimg.jpg', cv2.IMREAD_GRAYSCALE)
img = cv2.blur(img, (15, 15))
detector = cv2.SimpleBlobDetector()
keyPoints = detector.detect(img)
im_with_keypoints = cv2.drawKeyPoints(
img, keyPoints, np.array([]),
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
'''
add_color_range_to_detect([0], [53]) # Detect red
img_mask = get_mask(img)
blobs = get_blobs(img_mask)
object_positions_list = get_blob_centroids(blobs)
centroid_group = get_close_centroid(object_positions_list)
img_markup = img.copy()
for obj_pos in centroid_group:
centroid_group = np.array(obj_pos).astype(np.int32) # In case your object positions weren't numpy arrays
img_markup = cv2.circle(img_markup,(centroid_group[1], centroid_group[0]),5,(0,0,0),10)
#print("Object pos: " + str(obj_pos_vector))
centroid_group = get_close_centroid(object_positions_list)
#print(centroid_group)
cv2.imshow('orig', img)
cv2.imshow('img_mask', img_mask)
cv2.imshow('located', img_markup)
'''
cv2.imshow("keyPoints", im_with_keypoints)
cv2.waitKey(-1) # Wait until a key is pressed to exit the program
cv2.destroyAllWindows() # Close all the windows
# Pepper mode
num_pepper = np.ceil(amount* image.size * (1. - s_vs_p))
coords = # TODO
out[coords] = 0
return out
def mse(raw,recon):
return ((raw-recon) **2).mean()
#-----------------Lab2-----------------------
## Opencv
# ColorMap
cv2.applyColorMap()
# Padding
cv2.copyMakeBorder() #use cv2.BORDER_REFLECT
# SURF
cv2.xfeatures2d.SURF_create()
cv2.drawKeyPoints()
## Numpy scipy
# Save & load
np.save(),np.load()
# concatenate
np.concatenate()
# mean
np.mean()
# reciprocal
np.reciprocal()
# load .mat file
import scipy.io as sio
sio.loadmat()
# Euclidean distance
from scipy.spatial import distance
import numpy as np
import cv2
cap = cv2.VideoCapture(1)
while (True):
ret, img = cap.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
###Do your procecssing here###
sift = cv2.SIFT()
kp = sift.detect(gray, None)
image = cv2.drawKeyPoints(gray, kp)
###Video frame processing ends###
cv2.imshow('dst', image)
delay = 1
if cv2.waitKey(delay) & 0xff == 27:
break
cap.release()
cv2.destroyAllWindows()
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import cv2
import numpy as np
img = cv2.imread('/Users/amourlee/Desktop/convex.png')
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
sift = cv2.SIFT()
kp = sift.detect(img_gray, None)
img = cv2.drawKeyPoints(img_gray, kp)
cv2.imshow('Image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
os.chdir("/Users/macintosh/Desktop")
# load the image
image = cv2.imread("circles.jpg", 0)
cv2.imshow("Original Image", image)
cv2.waitKey()
# initialize the detector using the default parameters
detector = cv2.SimpleBlobDetector()
# detect blobs
keypoints = detector.detect(image)
# draw blobs on our image as red circles
blank = np.zeros((1, 1))
blobs = cv2.drawKeyPoints(image, keypoints, blank, (0, 0, 255),
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
number_of_blobs = len(keypoints)
text = "Total Number of Blobs: " + str(len(keypoints))
cv2.putText(blobs, text, (20, 550), cv2.FONT_HERSHEY_SIMPLEX, 1, (100, 0, 255),
2)
# display image with blob keypoints
cv2.imshow("Blobs using default parameters", blobs)
cv2.waitKey()
# set out filtering parameters
# initialize the parameter setting usng cv2.SimpleBlobDetector
params = cv2.SimpleBlobDetector_Params()
# set area filtering parameters
import cv2
import numpy as np
cap = cv2.VideoCapture(0)
while (cap.isOpened()):
ret, frame = cap.read()
frame = cv2.flip(frame, 1)
roi = frame[100:900, 100:900]
hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
cv2.imshow('frame', frame)
fr = frame
sift = cv2.xfeatures2d.SIFT_CREATE(fr, None)
kp = sift.detect(fr, None)
fr = cv2.drawKeyPoints(fr, kp, None)
cv2.imshow('Keypoints', fr)
lower_lim = np.array([0, 20, 70], dtype=np.uint8)
upper_lim = np.array([20, 255, 255], dtype=np.uint8)
mask = cv2.inRange(hsv, lower_lim, upper_lim)
mask = cv2.GaussianBlur(mask, (5, 5), 100)
cv2.imshow('mask', mask)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()