Added Sky Detection

Author: SyedImtiyaz-1

AirObjects/air.py

@@ -0,0 +1,138 @@
+import cv2
+from InitCaffe import *
+
+# Open video
+# cap = cv2.VideoCapture('NASA_video1.mp4') # video with airplane.
+
+# patch_size = 15 # Size of image patch to extract around featuns points
+patch_size = 25 # Size of image patch to extract around feature points
+
+
+count      = 0  # Loop counter to control frequency of object recognition
+objfreq    = 5  # Frequence of object recognition
+# NumCorners = 50 # Number of corners to extract in a given frame
+NumCorners = 10 # Number of corners to extract in a given frame
+'''
+# fourcc = cv2.cv.CV_FOURCC(*'XVID')
+# out = cv2.VideoWriter('result.avi', fourcc, 20.0, (450,170))
+# Read each frame of video and do object recognition at specified frequency
+while(cap.isOpened()):
+    carNum = 0 # Number of cars detected
+    # Read frame
+    ret, frame = cap.read()
+    # Resize each frame to a smaller size for speed
+    frame = cv2.resize(frame,(500, 300), interpolation = cv2.INTER_CUBIC)
+    #frame = frame[260:450,200:700]
+    # Implement object recognition at specified frequency
+    if count%objfreq == 0:
+'''
+# frame = cv2.imread('patch8901.png')
+frame = cv2.imread('frame184.png')
+# frame = cv2.imread('patch1.png')
+if 1:
+    if 1:
+        # Convert to gray scale
+        gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
+
+        # Find corners in gray scale image
+        corners = cv2.goodFeaturesToTrack(gray,NumCorners,0.01,100)
+        print 'The corners are:',corners
+        corners = np.int0(corners)
+
+        # For each corner found, extract a patch and classify patch
+        for j,i in enumerate(corners):
+            x,y = i.ravel()
+            #cv2.circle(frame,(x,y),3,255,-1)
+            print 'The x pos of the corner is: ', x
+            print 'The y pos of the corner is: ', y
+            print 'The i of the corners is: ', i
+            print 'The j of the corners is: ', j
+            # Define size of patch in image coordinates
+            xstart = x - patch_size
+            xend   = x + patch_size
+            ystart = y - patch_size
+            yend   = y + patch_size
+            
+
+            # clip image patch based on image size
+            xlen   = frame.shape[1]
+            ylen   = frame.shape[0]
+
+            if xend > xlen:
+                xend = xlen
+            if xstart < 0:
+                xstart = 0
+
+            if yend > ylen:
+                yend = ylen
+            if ystart < 0:
+                ystart = 0
+                
+            cv2.rectangle(frame,(xstart,ystart),(xend,yend),(255,0,0),2)
+            # Extract the image patch from each frame in the video
+            img_patch         = frame[ystart:yend,xstart:xend]
+
+
+            # Transform image to use caffe library
+            transformed_image = transformer.preprocess('data', img_patch)
+        
+            # copy the image data into the memory allocated for the net
+            net.blobs['data'].data[j,:,:,:] = transformed_image
+    
+        ### perform classification
+        output = net.forward()
+    
+        # Go through image patch for each corner and find if there are any airplanes
+        Position = []
+        for i,j in enumerate(corners):
+            x,y = j.ravel()
+            output_prob = output['prob'][i] 
+    
+            # sort top five predictions from softmax output
+            # top_inds = output_prob.argsort()[::-1][:5]  # reverse sort and take five largest items
+            top_inds = output_prob.argsort()[::-1][:10]  # reverse sort and take five largest items
+            print 'The classes are:', top_inds 
+            # print 'predicted class is:', output_prob.argmax()
+            # print 'output label:', labels[output_prob.argmax()]    
+            # print 'prob', output_prob[top_inds[0]]
+
+            # If airlane, record position to draw bounding box
+
+            AirplaneLabels = [895,404,405,812]  # Airplane label ids in caffe database
+            #437,566,556,570,706,735,752,818,830,848
+            #VehicleLabels = [867,717,675,757,569,734,751,817,864,656] # Car, truck, van label ids in caffe database
+            # for k in range (0,5):
+            for k in range (0,10):
+                if (top_inds[k] in AirplaneLabels ):
+                    if output_prob[top_inds[0]] > 0.0:
+                        print 'Shown class is:', top_inds[k]
+                        print 'output label:', labels[top_inds[k]]    
+                        print 'prob', output_prob[top_inds[k]]
+                        Position.append((x,y))
+#                        carNum = carNum + 1
+                   #     break
+        # Draw rectangles around each airplane
+        # print 'The number of cars detected are:', carNum
+        print 'The number of frame is:', count+1
+        for pos in Position:
+            xpos = pos[0]
+            ypos = pos[1]
+            cv2.rectangle(frame,(xpos-patch_size,ypos-patch_size),(xpos+patch_size,ypos+patch_size),(0,255,0),2)
+           # break
+        # out.write(frame)
+        cv2.imshow('frame',frame)
+        cv2.waitKey()
+    # Show image frame on screen
+    count = count + 1
+    # out.write(frame)
+    # cv2.imshow('frame',frame)
+    # cv2.waitKey()
+'''
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+    if count > cap.get(7)/2:
+        break
+'''
+#out.release()
+cap.release()
+cv2.destroyAllWindows()