Image Processing examples added

sampleImageprocessing/Controll Mouse with a moving object/HSV Color filter.py

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+import cv2
+import numpy as np
+
+def callback(x):
+    pass
+
+cap = cv2.VideoCapture(0)
+cv2.namedWindow('image')
+
+ilowH = 0
+ihighH = 179
+
+ilowS = 0
+ihighS = 255
+ilowV = 0
+ihighV = 255
+
+cv2.createTrackbar('lowH','image',ilowH,179,callback)
+cv2.createTrackbar('highH','image',ihighH,179,callback)
+cv2.createTrackbar('lowS','image',ilowS,255,callback)
+cv2.createTrackbar('highS','image',ihighS,255,callback)
+cv2.createTrackbar('lowV','image',ilowV,255,callback)
+cv2.createTrackbar('highV','image',ihighV,255,callback)
+
+
+
+while(True):
+    ret, frame = cap.read()
+    ilowH = cv2.getTrackbarPos('lowH', 'image')
+    ihighH = cv2.getTrackbarPos('highH', 'image')
+    ilowS = cv2.getTrackbarPos('lowS', 'image')
+    ihighS = cv2.getTrackbarPos('highS', 'image')
+    ilowV = cv2.getTrackbarPos('lowV', 'image')
+    ihighV = cv2.getTrackbarPos('highV', 'image')
+
+    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
+
+    lower_hsv = np.array([ilowH, ilowS, ilowV])
+    higher_hsv = np.array([ihighH, ihighS, ihighV])
+    mask = cv2.inRange(hsv, lower_hsv, higher_hsv)
+    mask = cv2.erode(mask, None, iterations=6)
+    mask = cv2.dilate(mask, None, iterations=6)
+    frame = cv2.bitwise_and(frame, frame, mask=mask)
+
+    cv2.imshow('image', frame)
+    k = cv2.waitKey(1000) & 0xFF # large wait time to remove freezing
+    if k == 113 or k == 27:
+        break
+
+
+cv2.destroyAllWindows()
+cap.release()

sampleImageprocessing/Controll Mouse with a moving object/Mouse.py

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+import cv2
+import numpy as np
+
+import win32api
+global cam
+cam = cv2.VideoCapture(0)
+
+def diffImg(t0, t1, t2):
+    d1 = cv2.absdiff(t2, t1)
+    d2 = cv2.absdiff(t1, t0)
+    return cv2.bitwise_and(d1, d2)
+def main():
+    global cam
+    t_minus = cv2.cvtColor(cam.read()[1], cv2.COLOR_RGB2GRAY)
+    t = cv2.cvtColor(cam.read()[1], cv2.COLOR_RGB2GRAY)
+    t_plus = cv2.cvtColor(cam.read()[1], cv2.COLOR_RGB2GRAY)
+    while True:
+          ret, frame = cam.read()
+          hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
+          lower_hsv = np.array([0, 130, 147])
+          higher_hsv = np.array([22, 255, 215])
+          mask = cv2.inRange(hsv, lower_hsv, higher_hsv)
+          mask = cv2.erode(mask, None, iterations=5)
+          mask = cv2.dilate(mask, None, iterations=5)
+          cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
+              cv2.CHAIN_APPROX_SIMPLE)
+          cnt=cnts[0]
+          if len(cnts)>0:
+              x,y,w,h = cv2.boundingRect(cnt)
+              xx,yy=(2*x+w)/2 , (2*y+h)/2
+              if xx>0 and xx<1366 and yy>0 and yy<768:
+                  win32api.SetCursorPos((1366-(xx*3),yy*3))
+
+
+          (cnts) = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
+            cv2.CHAIN_APPROX_SIMPLE)
+          t_minus = t
+          t = t_plus
+          t_plus = cv2.cvtColor(cam.read()[1], cv2.COLOR_RGB2GRAY)
+          cv2.imshow("Mouse", frame)
+          key = cv2.waitKey(10)
+          if key == 27:
+            cv2.destroyWindow(winName)
+            break
+main()
+
+

sampleImageprocessing/Controll Mouse with a moving object/README.md

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+# ImageProcessing
+Image Processing projects With OpenCV Python.
+
+This Program can control mouse cursor with a moving object.It detects the onject by its color.
+Use "HSV Color filter.py" to filter the object.
+Then Edit "Mouse.py" and change the following lines with values HSV values
+
+    lower_hsv = np.array([lowH, lowS,lowV])
+    higher_hsv = np.array([highH, highS, highV])
+
+Save it and RUN!

sampleImageprocessing/Hand Detection/Finger Detect.py

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+import numpy as np
+import cv2
+from math import *
+def diffImg(t0, t1, t2):
+  d1 = cv2.absdiff(t2, t1)
+  d2 = cv2.absdiff(t1, t0)
+  return cv2.bitwise_and(d1, d2)
+
+cap = cv2.VideoCapture(0)
+nl,pl=[0,0,0,0,0],[0,0,0,0,0]
+jjjj=[0,0,0,0,0]
+while(1):
+    ret, frame = cap.read()
+    crop_frame = frame[0:300, 0:300]
+    gray_image = cv2.cvtColor(crop_frame, cv2.COLOR_BGR2GRAY)
+    ret, mask = cv2.threshold(gray_image, 145, 255, cv2.THRESH_BINARY)
+    mask_inv = cv2.bitwise_not(mask)
+    mask_inv = cv2.erode(mask_inv, None, iterations=1)
+    mask_inv = cv2.dilate(mask_inv, None, iterations=1)
+    drawing=mask_inv
+    ret,thresh = cv2.threshold(mask_inv,127,255,0)
+    im2,contours,hierarchy = cv2.findContours(thresh, 1, 2)
+    cv2.rectangle(frame, (0, 0), (300, 300), (255,0,0), 2)
+
+
+    if len (contours)>0:
+        max_area=100
+        ci=0	
+        for i in range(len(contours)):
+            cnt=contours[i]
+            area = cv2.contourArea(cnt)
+            if(area>max_area):
+                max_area=area
+                ci=i  
+
+            #Largest area contour 			  
+        cnts = contours[ci]
+        M = cv2.moments(cnts)
+        cX = int(M["m10"] / M["m00"])
+        cY = int(M["m01"] / M["m00"])
+        cv2.circle(frame,(cX,cY),10,[100,255,255],3)
+        hull = cv2.convexHull(cnts)
+        hull2 = cv2.convexHull(cnts,returnPoints = False)
+        defects = cv2.convexityDefects(cnts,hull2)
+        FarDefect = []
+
+        try:
+          ccc=0
+          for i in range(defects.shape[0]):
+            s,e,f,d = defects[i,0]
+            start = tuple(cnts[s][0])
+            end = tuple(cnts[e][0])
+            far = tuple(cnts[f][0])
+            dist = sqrt( (start[0] - end[0])**2 + (start[1] - end[1])**2 )
+
+            if 1:
+                FarDefect.append(far)
+                #print end[0]-far[0],far[1]-end[1]
+                a=sqrt((far[0]-end[0])**2+ (end[1]-far[1])**2)
+                b=sqrt((start[0]-far[0])**2+ (start[1]-far[1])**2)
+                c=sqrt((start[1]-end[1])**2+(start[0]-end[0])**2)
+                angle=(a**2+b**2-c**2)/(2*a*b)
+                angle=acos(angle)
+
+                if (angle*180)/(22/7)<90 and dist>10:
+                    cv2.line(frame,(cX,cY),end,[125,255,0],1)
+                    cv2.line(frame,(cX,cY),end,[125,255,0] ,1)
+                    cv2.circle(frame,end,10,[100,255,255],3)
+          cv2.line(frame,(cX,cY),end,[125,255,0],1)        
+          cv2.circle(frame,end,10,[100,255,255],3)
+
+        except Exception as e:
+            pass
+
+        cv2.imshow('Dilation',drawing)
+
+    frame = cv2.resize(frame, (1366, 765), interpolation=cv2.INTER_CUBIC)
+    cv2.imshow('frame',frame)
+
+    #cv2.imshow('cropped frame',crop_frame)
+    cv2.imshow('grey cropped frame',gray_image)
+    cv2.imshow('t',mask)
+    cv2.imshow('t2',mask_inv)
+    #cv2.imshow('drawing',drawing)
+
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+cap.release()
+cv2.destroyAllWindows()

sampleImageprocessing/Hand Detection/README.md

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+# ImageProcessing
+Image Processing projects With OpenCV Python.
+
+This program identifies fingers of one hand.Just run "Finger Detect.py" and you can seea blue rectangle on the top left.
+Make that area with a light color background and bring your one hand to that area.
+
+See this video
+
+https://vimeo.com/228470426

sampleImageprocessing/Object Detect By Color/HSV Color filter.py

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+import cv2
+import numpy as np
+
+def callback(x):
+    pass
+
+cap = cv2.VideoCapture(0)
+cv2.namedWindow('image')
+
+ilowH = 0
+ihighH = 179
+
+ilowS = 0
+ihighS = 255
+ilowV = 0
+ihighV = 255
+
+cv2.createTrackbar('lowH','image',ilowH,179,callback)
+cv2.createTrackbar('highH','image',ihighH,179,callback)
+cv2.createTrackbar('lowS','image',ilowS,255,callback)
+cv2.createTrackbar('highS','image',ihighS,255,callback)
+cv2.createTrackbar('lowV','image',ilowV,255,callback)
+cv2.createTrackbar('highV','image',ihighV,255,callback)
+
+
+
+while(True):
+    ret, frame = cap.read()
+    ilowH = cv2.getTrackbarPos('lowH', 'image')
+    ihighH = cv2.getTrackbarPos('highH', 'image')
+    ilowS = cv2.getTrackbarPos('lowS', 'image')
+    ihighS = cv2.getTrackbarPos('highS', 'image')
+    ilowV = cv2.getTrackbarPos('lowV', 'image')
+    ihighV = cv2.getTrackbarPos('highV', 'image')
+
+    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
+
+    lower_hsv = np.array([ilowH, ilowS, ilowV])
+    higher_hsv = np.array([ihighH, ihighS, ihighV])
+    mask = cv2.inRange(hsv, lower_hsv, higher_hsv)
+    mask = cv2.erode(mask, None, iterations=6)
+    mask = cv2.dilate(mask, None, iterations=6)
+    frame = cv2.bitwise_and(frame, frame, mask=mask)
+
+    cv2.imshow('image', frame)
+    k = cv2.waitKey(1000) & 0xFF # large wait time to remove freezing
+    if k == 113 or k == 27:
+        break
+
+
+cv2.destroyAllWindows()
+cap.release()

sampleImageprocessing/Object Detect By Color/README.md

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+# ImageProcessing
+Image Processing projects With OpenCV Python 
+
+
+Run "HSV Color filter.py" and filter the object color you want to detect.
+Then edit "object detect by color.py" and change the following lines with values HSV values
+
+    lower_hsv = np.array([lowH, lowS,lowV])
+    higher_hsv = np.array([highH, highS, highV])
+
+Save it and RUN!

sampleImageprocessing/Object Detect By Color/object detect by color.py

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+import numpy as np
+import cv2
+
+cap = cv2.VideoCapture(0)
+dd=True
+while(dd):
+    ret, frame = cap.read()
+    fg=frame
+    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
+    lower_hsv = np.array([0, 163, 182])
+    higher_hsv = np.array([110, 255, 254])
+    mask = cv2.inRange(hsv, lower_hsv, higher_hsv)
+    mask = cv2.erode(mask, None, iterations=2)
+    mask = cv2.dilate(mask, None, iterations=2)
+
+    cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
+        cv2.CHAIN_APPROX_SIMPLE)
+    cnt = cnts[0]
+    M = cv2.moments(cnt)
+    if len(cnts)>0:
+        frame=fg
+
+        x,y,w,h = cv2.boundingRect(cnt)
+        cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),2)
+
+
+
+    cv2.imshow('frame',frame)
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+cap.release()
+cv2.destroyAllWindows()