Merge remote-tracking branch 'upstream/3.4' into merge-3.4

2019-05-15 18:01:21 +00:00
parent b2abd8ca41 84fd8190f3
commit e21262deba
54 changed files with 1545 additions and 748 deletions
@@ -0,0 +1,50 @@
+import numpy as np
+import cv2 as cv
+import argparse
+
+parser = argparse.ArgumentParser(description='This sample demonstrates the camshift algorithm. \
+                                              The example file can be downloaded from: \
+                                              https://www.bogotobogo.com/python/OpenCV_Python/images/mean_shift_tracking/slow_traffic_small.mp4')
+parser.add_argument('image', type=str, help='path to image file')
+args = parser.parse_args()
+
+cap = cv.VideoCapture(args.image)
+
+# take first frame of the video
+ret,frame = cap.read()
+
+# setup initial location of window
+x, y, w, h = 300, 200, 100, 50 # simply hardcoded the values
+track_window = (x, y, w, h)
+
+# set up the ROI for tracking
+roi = frame[y:y+h, x:x+w]
+hsv_roi =  cv.cvtColor(roi, cv.COLOR_BGR2HSV)
+mask = cv.inRange(hsv_roi, np.array((0., 60.,32.)), np.array((180.,255.,255.)))
+roi_hist = cv.calcHist([hsv_roi],[0],mask,[180],[0,180])
+cv.normalize(roi_hist,roi_hist,0,255,cv.NORM_MINMAX)
+
+# Setup the termination criteria, either 10 iteration or move by atleast 1 pt
+term_crit = ( cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 1 )
+
+while(1):
+    ret, frame = cap.read()
+
+    if ret == True:
+        hsv = cv.cvtColor(frame, cv.COLOR_BGR2HSV)
+        dst = cv.calcBackProject([hsv],[0],roi_hist,[0,180],1)
+
+        # apply camshift to get the new location
+        ret, track_window = cv.CamShift(dst, track_window, term_crit)
+
+        # Draw it on image
+        pts = cv.boxPoints(ret)
+        pts = np.int0(pts)
+        img2 = cv.polylines(frame,[pts],True, 255,2)
+        cv.imshow('img2',img2)
+
+        k = cv.waitKey(30) & 0xff
+        if k == 27:
+            break
+    else:
+        break
@@ -0,0 +1,49 @@
+import numpy as np
+import cv2 as cv
+import argparse
+
+parser = argparse.ArgumentParser(description='This sample demonstrates the meanshift algorithm. \
+                                              The example file can be downloaded from: \
+                                              https://www.bogotobogo.com/python/OpenCV_Python/images/mean_shift_tracking/slow_traffic_small.mp4')
+parser.add_argument('image', type=str, help='path to image file')
+args = parser.parse_args()
+
+cap = cv.VideoCapture(args.image)
+
+# take first frame of the video
+ret,frame = cap.read()
+
+# setup initial location of window
+x, y, w, h = 300, 200, 100, 50 # simply hardcoded the values
+track_window = (x, y, w, h)
+
+# set up the ROI for tracking
+roi = frame[y:y+h, x:x+w]
+hsv_roi =  cv.cvtColor(roi, cv.COLOR_BGR2HSV)
+mask = cv.inRange(hsv_roi, np.array((0., 60.,32.)), np.array((180.,255.,255.)))
+roi_hist = cv.calcHist([hsv_roi],[0],mask,[180],[0,180])
+cv.normalize(roi_hist,roi_hist,0,255,cv.NORM_MINMAX)
+
+# Setup the termination criteria, either 10 iteration or move by atleast 1 pt
+term_crit = ( cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 1 )
+
+while(1):
+    ret, frame = cap.read()
+
+    if ret == True:
+        hsv = cv.cvtColor(frame, cv.COLOR_BGR2HSV)
+        dst = cv.calcBackProject([hsv],[0],roi_hist,[0,180],1)
+
+        # apply meanshift to get the new location
+        ret, track_window = cv.meanShift(dst, track_window, term_crit)
+
+        # Draw it on image
+        x,y,w,h = track_window
+        img2 = cv.rectangle(frame, (x,y), (x+w,y+h), 255,2)
+        cv.imshow('img2',img2)
+
+        k = cv.waitKey(30) & 0xff
+        if k == 27:
+            break
+    else:
+        break
@@ -0,0 +1,61 @@
+import numpy as np
+import cv2 as cv
+import argparse
+
+parser = argparse.ArgumentParser(description='This sample demonstrates Lucas-Kanade Optical Flow calculation. \
+                                              The example file can be downloaded from: \
+                                              https://www.bogotobogo.com/python/OpenCV_Python/images/mean_shift_tracking/slow_traffic_small.mp4')
+parser.add_argument('image', type=str, help='path to image file')
+args = parser.parse_args()
+
+cap = cv.VideoCapture(args.image)
+
+# params for ShiTomasi corner detection
+feature_params = dict( maxCorners = 100,
+                       qualityLevel = 0.3,
+                       minDistance = 7,
+                       blockSize = 7 )
+
+# Parameters for lucas kanade optical flow
+lk_params = dict( winSize  = (15,15),
+                  maxLevel = 2,
+                  criteria = (cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 0.03))
+
+# Create some random colors
+color = np.random.randint(0,255,(100,3))
+
+# Take first frame and find corners in it
+ret, old_frame = cap.read()
+old_gray = cv.cvtColor(old_frame, cv.COLOR_BGR2GRAY)
+p0 = cv.goodFeaturesToTrack(old_gray, mask = None, **feature_params)
+
+# Create a mask image for drawing purposes
+mask = np.zeros_like(old_frame)
+
+while(1):
+    ret,frame = cap.read()
+    frame_gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
+
+    # calculate optical flow
+    p1, st, err = cv.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params)
+
+    # Select good points
+    good_new = p1[st==1]
+    good_old = p0[st==1]
+
+    # draw the tracks
+    for i,(new,old) in enumerate(zip(good_new, good_old)):
+        a,b = new.ravel()
+        c,d = old.ravel()
+        mask = cv.line(mask, (a,b),(c,d), color[i].tolist(), 2)
+        frame = cv.circle(frame,(a,b),5,color[i].tolist(),-1)
+    img = cv.add(frame,mask)
+
+    cv.imshow('frame',img)
+    k = cv.waitKey(30) & 0xff
+    if k == 27:
+        break
+
+    # Now update the previous frame and previous points
+    old_gray = frame_gray.copy()
+    p0 = good_new.reshape(-1,1,2)
@@ -0,0 +1,23 @@
+import numpy as np
+import cv2 as cv
+cap = cv.VideoCapture(cv.samples.findFile("vtest.avi"))
+ret, frame1 = cap.read()
+prvs = cv.cvtColor(frame1,cv.COLOR_BGR2GRAY)
+hsv = np.zeros_like(frame1)
+hsv[...,1] = 255
+while(1):
+    ret, frame2 = cap.read()
+    next = cv.cvtColor(frame2,cv.COLOR_BGR2GRAY)
+    flow = cv.calcOpticalFlowFarneback(prvs,next, None, 0.5, 3, 15, 3, 5, 1.2, 0)
+    mag, ang = cv.cartToPolar(flow[...,0], flow[...,1])
+    hsv[...,0] = ang*180/np.pi/2
+    hsv[...,2] = cv.normalize(mag,None,0,255,cv.NORM_MINMAX)
+    bgr = cv.cvtColor(hsv,cv.COLOR_HSV2BGR)
+    cv.imshow('frame2',bgr)
+    k = cv.waitKey(30) & 0xff
+    if k == 27:
+        break
+    elif k == ord('s'):
+        cv.imwrite('opticalfb.png',frame2)
+        cv.imwrite('opticalhsv.png',bgr)
+    prvs = next