Merge pull request #2325 from SyedImtiyaz-1/Sky

Added Sky Detection

Author: avinashkranjan

AirObjects/README.md

@@ -0,0 +1,42 @@
+
+### Air Object Detector
+
+Install with Pypi:
+
+pip3 install pyransac3d
+Take a look:
+Example 1 - Planar RANSAC
+import pyransac3d as pyrsc
+
+points = load_points(.) # Load your point cloud as a numpy array (N, 3)
+
+plane1 = pyrsc.Plane()
+best_eq, best_inliers = plane1.fit(points, 0.01)
+Results in the plane equation Ax+By+Cz+D: [0.720, -0.253, 0.646, 1.100]
+
+Example 2 - Spherical RANSAC
+Loading a noisy sphere's point cloud with r = 5 centered in 0 we can use the following code:
+
+import pyransac3d as pyrsc
+
+points = load_points(.) # Load your point cloud as a numpy array (N, 3)
+
+Documentation & other links
+The documentation is this Ṕage.
+Source code in the Github repository.
+Pypi pakage installer
+You can find the animations you see in the documentation on branch Animations. It needs Open3D library to run. The Animation branch is not regularly maintained, it only exists to create some cool visualizations ;D
+License
+Apache 2.0
+
+Citation
+Did this repository was useful for your work? =)
+Contributing is awesome!
+See CONTRIBUTING
+
+Contact
+Developed with heart by the internet
+
+Mainteiner: Leonardo Mariga
+
+Did you like it? Remember to click on 🌟 button.

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()