camshift and kelman

techfort · Jun 10, 2015 · 1748168 · 1748168
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diff --git a/chapter8/camshift.py b/chapter8/camshift.py
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+import numpy as np
+import cv2
+
+cap = cv2.VideoCapture(0)
+
+# take first frame of the video
+ret,frame = cap.read()
+
+# setup initial location of window
+r,h,c,w = 300,200,400,300  # simply hardcoded the values
+track_window = (c,r,w,h)
+
+
+roi = frame[r:r+h, c:c+w]
+hsv_roi =  cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
+mask = cv2.inRange(hsv_roi, np.array((100., 30.,32.)), np.array((180.,120.,255.)))
+roi_hist = cv2.calcHist([hsv_roi],[0],mask,[180],[0,180])
+cv2.normalize(roi_hist,roi_hist,0,255,cv2.NORM_MINMAX)
+term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )
+
+while(1):
+    ret ,frame = cap.read()
+
+    if ret == True:
+        hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
+        dst = cv2.calcBackProject([hsv],[0],roi_hist,[0,180],1)
+
+        ret, track_window = cv2.CamShift(dst, track_window, term_crit)
+        pts = cv2.boxPoints(ret)
+        pts = np.int0(pts)
+        img2 = cv2.polylines(frame,[pts],True, 255,2)
+
+        cv2.imshow('img2',img2)
+        k = cv2.waitKey(60) & 0xff
+        if k == 27:
+            break
+
+    else:
+        break
+
+cv2.destroyAllWindows()
+cap.release()
diff --git a/chapter8/hist.py b/chapter8/hist.py
@@ -0,0 +1,21 @@
+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+
+camera = cv2.VideoCapture(0)
+
+while True:
+  ret, img = camera.read()
+  color = ('b','g','r')
+  for i,col in enumerate(color):
+    histr = cv2.calcHist([img],[i],None,[256],[0,256])
+    plt.plot(histr,color = col)
+    plt.xlim([0,256])
+    plt.show()
+  #cv2.imshow("frame", img)
+  # k = cv2.waitKey(30) & 0xff
+  # if k == 27:
+  #     break
+
+camera.release()
+cv2.destroyAllWindows()
diff --git a/chapter8/kalman.py b/chapter8/kalman.py
@@ -0,0 +1,48 @@
+import numpy as np
+import cv2
+
+cap = cv2.VideoCapture(0)
+
+# take first frame of the video
+ret,frame = cap.read()
+
+# setup initial location of window
+r,h,c,w = 300,200,400,300  # simply hardcoded the values
+track_window = (c,r,w,h)
+
+
+roi = frame[r:r+h, c:c+w]
+hsv_roi =  cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
+mask = cv2.inRange(hsv_roi, np.array((100., 30.,32.)), np.array((180.,120.,255.)))
+roi_hist = cv2.calcHist([hsv_roi],[0],mask,[180],[0,180])
+cv2.normalize(roi_hist,roi_hist,0,255,cv2.NORM_MINMAX)
+term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )
+
+kalman = cv2.KalmanFilter(2,1,0)
+
+while(1):
+    ret ,frame = cap.read()
+
+    if ret == True:
+
+        hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
+        kalman.predict(hsv)
+        dst = cv2.calcBackProject([hsv],[0],roi_hist,[0,180],1)
+
+        ret, track_window = cv2.CamShift(dst, track_window, term_crit)
+        x, y, w, h = track_window
+        kalman.correct(frame)
+        pts = cv2.boxPoints(ret)
+        pts = np.int0(pts)
+        img2 = cv2.polylines(frame,[pts],True, 255,2)
+        kalman.correct(hsv)
+        cv2.imshow('img2',img2)
+        k = cv2.waitKey(60) & 0xff
+        if k == 27:
+            break
+
+    else:
+        break
+
+cv2.destroyAllWindows()
+cap.release()
diff --git a/chapter8/lk.py b/chapter8/lk.py
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+import numpy as np
+import cv2
+
+cap = cv2.VideoCapture(0)
+
+# 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 = (cv2.TERM_CRITERIA_EPS | cv2.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 = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)
+p0 = cv2.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 = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+    # calculate optical flow
+    p1, st, err = cv2.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 = cv2.line(mask, (a,b),(c,d), color[i].tolist(), 2)
+        frame = cv2.circle(frame,(a,b),5,color[i].tolist(),-1)
+    img = cv2.add(frame,mask)
+
+    cv2.imshow('frame',img)
+    k = cv2.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)
+
+cv2.destroyAllWindows()
+cap.release()
diff --git a/chapter8/meanshift.jpg b/chapter8/meanshift.jpg
diff --git a/chapter8/meanshift.py b/chapter8/meanshift.py
@@ -0,0 +1,48 @@
+import numpy as np
+import cv2
+
+cap = cv2.VideoCapture(0)
+# capture the first frame
+ret,frame = cap.read()
+# mark the ROI
+r,h,c,w = 10, 200, 10, 200
+# wrap in a tuple
+track_window = (c,r,w,h)
+
+# extract the ROI for tracking
+roi = frame[r:r+h, c:c+w]
+# switch to HSV
+hsv_roi =  cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
+# create a mask with upper and lower boundaries of colors you want to track
+mask = cv2.inRange(hsv_roi, np.array((100., 30.,32.)), np.array((180.,120.,255.)))
+# calculate histograms of roi
+roi_hist = cv2.calcHist([hsv_roi],[0],mask,[180],[0,180])
+cv2.normalize(roi_hist,roi_hist,0,255,cv2.NORM_MINMAX)
+
+# Setup the termination criteria, either 10 iteration or move by atleast 1 pt
+term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )
+
+while(1):
+    ret ,frame = cap.read()
+
+    if ret == True:
+        hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
+        dst = cv2.calcBackProject([hsv],[0],roi_hist,[0,180],1)
+        print dst
+        # apply meanshift to get the new location
+        ret, track_window = cv2.meanShift(dst, track_window, term_crit)
+
+        # Draw it on image
+        x,y,w,h = track_window
+        img2 = cv2.rectangle(frame, (x,y), (x+w,y+h), 255,2)
+        cv2.imshow('img2',img2)
+
+        k = cv2.waitKey(60) & 0xff
+        if k == 27:
+            break
+
+    else:
+        break
+
+cv2.destroyAllWindows()
+cap.release()
diff --git a/chapter8/movie.mpg b/chapter8/movie.mpg
diff --git a/chapter8/q.jpg b/chapter8/q.jpg
diff --git a/chapter8/�.jpg b/chapter8/�.jpg