def findstereocorrespondence(image_left, image_right): # image_left and image_right are the input 8-bit single-channel images # from the left and the right cameras, respectively (r, c) = (image_left.rows, image_left.cols) disparity_left = cv.CreateMat(r, c, cv.CV_16S) disparity_right = cv.CreateMat(r, c, cv.CV_16S) state = cv.CreateStereoGCState(16, 2) cv.FindStereoCorrespondenceGC(image_left, image_right, disparity_left, disparity_right, state, 0) return (disparity_left, disparity_right)
def graphCut(self, maxIters=4): numRow, numCol = self.grayL.shape disparityLeft = cv.CreateMat(numRow, numCol, cv.CV_16S) disparityRight = cv.CreateMat(numRow, numCol, cv.CV_16S) stereo = cv.CreateStereoGCState(self.ndisparities, maxIters) cv.FindStereoCorrespondenceGC(left=cv.fromarray(self.grayL), right=cv.fromarray(self.grayR), dispLeft=disparityLeft, dispRight=disparityRight, state=stereo, useDisparityGuess=0) disparity_visual = -1 * np.array(disparityLeft) return disparity_visual
for i in range(0, image.height): for j in range(0, image.width): # keep closer object if cv.GetReal2D(disparity, i, j) > threshold: cv.Set2D(disparity, i, j, cv.Get2D(image, i, j)) # loading the stereo pair left = cv.LoadImage('scene_l.bmp', cv.CV_LOAD_IMAGE_GRAYSCALE) right = cv.LoadImage('scene_r.bmp', cv.CV_LOAD_IMAGE_GRAYSCALE) disparity_left = cv.CreateMat(left.height, left.width, cv.CV_16S) disparity_right = cv.CreateMat(left.height, left.width, cv.CV_16S) # data structure initialization state = cv.CreateStereoGCState(16, 2) # running the graph-cut algorithm cv.FindStereoCorrespondenceGC(left, right, disparity_left, disparity_right, state) disp_left_visual = cv.CreateMat(left.height, left.width, cv.CV_8U) cv.ConvertScale(disparity_left, disp_left_visual, -16) cv.Save("disparity.pgm", disp_left_visual) # save the map # cutting the object farthest of a threshold (120) cut(disp_left_visual, left, 120) cv.NamedWindow('Disparity map', cv.CV_WINDOW_AUTOSIZE) cv.ShowImage('Disparity map', disp_left_visual) cv.WaitKey()
def run(self): # Initialize # log_file_name = "tracker_output.log" # log_file = file( log_file_name, 'a' ) print "hello" frame = cv.QueryFrame(self.capture) frame_size = cv.GetSize(frame) # Capture the first frame from webcam for image properties display_image = cv.QueryFrame(self.capture) # Greyscale image, thresholded to create the motion mask: grey_image = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_8U, 1) # The RunningAvg() function requires a 32-bit or 64-bit image... running_average_image = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_32F, 3) # ...but the AbsDiff() function requires matching image depths: running_average_in_display_color_depth = cv.CloneImage(display_image) # RAM used by FindContours(): mem_storage = cv.CreateMemStorage(0) # The difference between the running average and the current frame: difference = cv.CloneImage(display_image) target_count = 1 last_target_count = 1 last_target_change_t = 0.0 k_or_guess = 1 codebook = [] frame_count = 0 last_frame_entity_list = [] t0 = time.time() # For toggling display: image_list = [ "camera", "difference", "threshold", "display", "faces" ] image_index = 3 # Index into image_list # Prep for text drawing: text_font = cv.InitFont(cv.CV_FONT_HERSHEY_COMPLEX, .5, .5, 0.0, 1, cv.CV_AA) text_coord = (5, 15) text_color = cv.CV_RGB(255, 255, 255) ############################### # ## Face detection stuff # haar_cascade = cv.Load( 'haarcascades/haarcascade_frontalface_default.xml' ) haar_cascade = cv.Load('E:\\Softwares\\opencv\\data\\haarcascades\\haarcascade_frontalface_alt.xml') # haar_cascade = cv.Load( 'haarcascades/haarcascade_frontalface_alt2.xml' ) # haar_cascade = cv.Load( 'haarcascades/haarcascade_mcs_mouth.xml' ) # haar_cascade = cv.Load( 'haarcascades/haarcascade_eye.xml' ) # haar_cascade = cv.Load( 'haarcascades/haarcascade_frontalface_alt_tree.xml' ) # haar_cascade = cv.Load( 'haarcascades/haarcascade_upperbody.xml' ) # haar_cascade = cv.Load( 'haarcascades/haarcascade_profileface.xml' ) # Set this to the max number of targets to look for (passed to k-means): max_targets = 5 while True: # Capture frame from webcam camera_image = cv.QueryFrame(self.capture) frame_count += 1 frame_t0 = time.time() # Create an image with interactive feedback: display_image = cv.CloneImage(camera_image) # Create a working "color image" to modify / blur color_image = cv.CloneImage(display_image) # Smooth to get rid of false positives cv.Smooth(color_image, color_image, cv.CV_GAUSSIAN, 19, 0) # Use the Running Average as the static background # a = 0.020 leaves artifacts lingering way too long. # a = 0.320 works well at 320x240, 15fps. (1/a is roughly num frames.) cv.RunningAvg(color_image, running_average_image, 0.320, None) # Convert the scale of the moving average. cv.ConvertScale(running_average_image, running_average_in_display_color_depth, 1.0, 0.0) # Subtract the current frame from the moving average. cv.AbsDiff(color_image, running_average_in_display_color_depth, difference) # Convert the image to greyscale. cv.CvtColor(difference, grey_image, cv.CV_RGB2GRAY) # Threshold the image to a black and white motion mask: cv.Threshold(grey_image, grey_image, 2, 255, cv.CV_THRESH_BINARY) # Smooth and threshold again to eliminate "sparkles" cv.Smooth(grey_image, grey_image, cv.CV_GAUSSIAN, 19, 0) cv.Threshold(grey_image, grey_image, 240, 255, cv.CV_THRESH_BINARY) grey_image_as_array = numpy.asarray(cv.GetMat(grey_image)) non_black_coords_array = numpy.where(grey_image_as_array > 3) # Convert from numpy.where()'s two separate lists to one list of (x, y) tuples: non_black_coords_array = zip(non_black_coords_array[1], non_black_coords_array[0]) points = [] # Was using this to hold either pixel coords or polygon coords. bounding_box_list = [] # Now calculate movements using the white pixels as "motion" data contour = cv.FindContours(grey_image, mem_storage, cv.CV_RETR_CCOMP, cv.CV_CHAIN_APPROX_SIMPLE) while contour: bounding_rect = cv.BoundingRect(list(contour)) point1 = (bounding_rect[0], bounding_rect[1]) point2 = (bounding_rect[0] + bounding_rect[2], bounding_rect[1] + bounding_rect[3]) bounding_box_list.append((point1, point2)) polygon_points = cv.ApproxPoly(list(contour), mem_storage, cv.CV_POLY_APPROX_DP) # To track polygon points only (instead of every pixel): # points += list(polygon_points) # Draw the contours: # ##cv.DrawContours(color_image, contour, cv.CV_RGB(255,0,0), cv.CV_RGB(0,255,0), levels, 3, 0, (0,0) ) cv.FillPoly(grey_image, [ list(polygon_points), ], cv.CV_RGB(255, 255, 255), 0, 0) cv.PolyLine(display_image, [ polygon_points, ], 0, cv.CV_RGB(255, 255, 255), 1, 0, 0) # cv.Rectangle( display_image, point1, point2, cv.CV_RGB(120,120,120), 1) contour = contour.h_next() # Find the average size of the bbox (targets), then # remove any tiny bboxes (which are prolly just noise). # "Tiny" is defined as any box with 1/10th the area of the average box. # This reduces false positives on tiny "sparkles" noise. box_areas = [] for box in bounding_box_list: box_width = box[right][0] - box[left][0] box_height = box[bottom][0] - box[top][0] box_areas.append(box_width * box_height) # cv.Rectangle( display_image, box[0], box[1], cv.CV_RGB(255,0,0), 1) average_box_area = 0.0 if len(box_areas): average_box_area = float(sum(box_areas)) / len(box_areas) trimmed_box_list = [] for box in bounding_box_list: box_width = box[right][0] - box[left][0] box_height = box[bottom][0] - box[top][0] # Only keep the box if it's not a tiny noise box: if (box_width * box_height) > average_box_area * 0.1: trimmed_box_list.append(box) # Draw the trimmed box list: # for box in trimmed_box_list: # cv.Rectangle( display_image, box[0], box[1], cv.CV_RGB(0,255,0), 2 ) bounding_box_list = merge_collided_bboxes(trimmed_box_list) # Draw the merged box list: for box in bounding_box_list: cv.Rectangle(display_image, box[0], box[1], cv.CV_RGB(0, 255, 0), 1) # Here are our estimate points to track, based on merged & trimmed boxes: estimated_target_count = len(bounding_box_list) # Don't allow target "jumps" from few to many or many to few. # Only change the number of targets up to one target per n seconds. # This fixes the "exploding number of targets" when something stops moving # and the motion erodes to disparate little puddles all over the place. if frame_t0 - last_target_change_t < .350: # 1 change per 0.35 secs estimated_target_count = last_target_count else: if last_target_count - estimated_target_count > 1: estimated_target_count = last_target_count - 1 if estimated_target_count - last_target_count > 1: estimated_target_count = last_target_count + 1 last_target_change_t = frame_t0 # Clip to the user-supplied maximum: estimated_target_count = min(estimated_target_count, max_targets) # The estimated_target_count at this point is the maximum number of targets # we want to look for. If kmeans decides that one of our candidate # bboxes is not actually a target, we remove it from the target list below. # Using the numpy values directly (treating all pixels as points): points = non_black_coords_array center_points = [] if len(points): # If we have all the "target_count" targets from last frame, # use the previously known targets (for greater accuracy). k_or_guess = max(estimated_target_count, 1) # Need at least one target to look for. if len(codebook) == estimated_target_count: k_or_guess = codebook # points = vq.whiten(array( points )) # Don't do this! Ruins everything. codebook, distortion = vq.kmeans(array(points), k_or_guess) # Convert to tuples (and draw it to screen) for center_point in codebook: center_point = (int(center_point[0]), int(center_point[1])) center_points.append(center_point) # cv.Circle(display_image, center_point, 10, cv.CV_RGB(255, 0, 0), 2) # cv.Circle(display_image, center_point, 5, cv.CV_RGB(255, 0, 0), 3) # Now we have targets that are NOT computed from bboxes -- just # movement weights (according to kmeans). If any two targets are # within the same "bbox count", average them into a single target. # # (Any kmeans targets not within a bbox are also kept.) trimmed_center_points = [] removed_center_points = [] for box in bounding_box_list: # Find the centers within this box: center_points_in_box = [] for center_point in center_points: if center_point[0] < box[right][0] and center_point[0] > box[left][0] and \ center_point[1] < box[bottom][1] and center_point[1] > box[top][1] : # This point is within the box. center_points_in_box.append(center_point) # Now see if there are more than one. If so, merge them. if len(center_points_in_box) > 1: # Merge them: x_list = y_list = [] for point in center_points_in_box: x_list.append(point[0]) y_list.append(point[1]) average_x = int(float(sum(x_list)) / len(x_list)) average_y = int(float(sum(y_list)) / len(y_list)) trimmed_center_points.append((average_x, average_y)) # Record that they were removed: removed_center_points += center_points_in_box if len(center_points_in_box) == 1: trimmed_center_points.append(center_points_in_box[0]) # Just use it. # If there are any center_points not within a bbox, just use them. # (It's probably a cluster comprised of a bunch of small bboxes.) for center_point in center_points: if (not center_point in trimmed_center_points) and (not center_point in removed_center_points): trimmed_center_points.append(center_point) # Draw what we found: # for center_point in trimmed_center_points: # center_point = ( int(center_point[0]), int(center_point[1]) ) # cv.Circle(display_image, center_point, 20, cv.CV_RGB(255, 255,255), 1) # cv.Circle(display_image, center_point, 15, cv.CV_RGB(100, 255, 255), 1) # cv.Circle(display_image, center_point, 10, cv.CV_RGB(255, 255, 255), 2) # cv.Circle(display_image, center_point, 5, cv.CV_RGB(100, 255, 255), 3) # Determine if there are any new (or lost) targets: actual_target_count = len(trimmed_center_points) last_target_count = actual_target_count # Now build the list of physical entities (objects) this_frame_entity_list = [] # An entity is list: [ name, color, last_time_seen, last_known_coords ] for target in trimmed_center_points: # Is this a target near a prior entity (same physical entity)? entity_found = False entity_distance_dict = {} for entity in last_frame_entity_list: entity_coords = entity[3] delta_x = entity_coords[0] - target[0] delta_y = entity_coords[1] - target[1] distance = sqrt(pow(delta_x, 2) + pow(delta_y, 2)) entity_distance_dict[ distance ] = entity # Did we find any non-claimed entities (nearest to furthest): distance_list = entity_distance_dict.keys() distance_list.sort() for distance in distance_list: # Yes; see if we can claim the nearest one: nearest_possible_entity = entity_distance_dict[ distance ] # Don't consider entities that are already claimed: if nearest_possible_entity in this_frame_entity_list: # print "Target %s: Skipping the one iwth distance: %d at %s, C:%s" % (target, distance, nearest_possible_entity[3], nearest_possible_entity[1] ) continue # print "Target %s: USING the one iwth distance: %d at %s, C:%s" % (target, distance, nearest_possible_entity[3] , nearest_possible_entity[1]) # Found the nearest entity to claim: entity_found = True nearest_possible_entity[2] = frame_t0 # Update last_time_seen nearest_possible_entity[3] = target # Update the new location this_frame_entity_list.append(nearest_possible_entity) # log_file.write( "%.3f MOVED %s %d %d\n" % ( frame_t0, nearest_possible_entity[0], nearest_possible_entity[3][0], nearest_possible_entity[3][1] ) ) break if entity_found == False: # It's a new entity. color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)) name = hashlib.md5(str(frame_t0) + str(color)).hexdigest()[:6] last_time_seen = frame_t0 new_entity = [ name, color, last_time_seen, target ] this_frame_entity_list.append(new_entity) # log_file.write( "%.3f FOUND %s %d %d\n" % ( frame_t0, new_entity[0], new_entity[3][0], new_entity[3][1] ) ) # Now "delete" any not-found entities which have expired: entity_ttl = 1.0 # 1 sec. for entity in last_frame_entity_list: last_time_seen = entity[2] if frame_t0 - last_time_seen > entity_ttl: # It's gone. # log_file.write( "%.3f STOPD %s %d %d\n" % ( frame_t0, entity[0], entity[3][0], entity[3][1] ) ) pass else: # Save it for next time... not expired yet: this_frame_entity_list.append(entity) # For next frame: last_frame_entity_list = this_frame_entity_list # Draw the found entities to screen: for entity in this_frame_entity_list: center_point = entity[3] c = entity[1] # RGB color tuple cv.Circle(display_image, center_point, 20, cv.CV_RGB(c[0], c[1], c[2]), 1) cv.Circle(display_image, center_point, 15, cv.CV_RGB(c[0], c[1], c[2]), 1) cv.Circle(display_image, center_point, 10, cv.CV_RGB(c[0], c[1], c[2]), 2) cv.Circle(display_image, center_point, 5, cv.CV_RGB(c[0], c[1], c[2]), 3) # print "min_size is: " + str(min_size) # Listen for ESC or ENTER key c = cv.WaitKey(7) % 0x100 if c == 27 or c == 10: break # Toggle which image to show # if chr(c) == 'd': # image_index = ( image_index + 1 ) % len( image_list ) # # image_name = image_list[ image_index ] # # # Display frame to user # if image_name == "camera": # image = camera_image # cv.PutText( image, "Camera (Normal)", text_coord, text_font, text_color ) # elif image_name == "difference": # image = difference # cv.PutText( image, "Difference Image", text_coord, text_font, text_color ) # elif image_name == "display": # image = display_image # cv.PutText( image, "Targets (w/AABBs and contours)", text_coord, text_font, text_color ) # elif image_name == "threshold": # # Convert the image to color. # cv.CvtColor( grey_image, display_image, cv.CV_GRAY2RGB ) # image = display_image # Re-use display image here # cv.PutText( image, "Motion Mask", text_coord, text_font, text_color ) # elif image_name == "faces": # # Do face detection # detect_faces( camera_image, haar_cascade, mem_storage ) # image = camera_image # Re-use camera image here # cv.PutText( image, "Face Detection", text_coord, text_font, text_color ) # cv.ShowImage( "Target", image ) image1 = display_image detect_faces(camera_image, haar_cascade, mem_storage) image2 = camera_image cv.ShowImage("Target 1", image1) cv.ShowImage("Target 2", image2) # if self.writer: # cv.WriteFrame( self.writer, image ); # log_file.flush() # If only using a camera, then there is no time.sleep() needed, # because the camera clips us to 15 fps. But if reading from a file, # we need this to keep the time-based target clipping correct: frame_t1 = time.time() # If reading from a file, put in a forced delay: if not self.writer: delta_t = frame_t1 - frame_t0 if delta_t < (1.0 / 15.0): time.sleep((1.0 / 15.0) - delta_t) [rows, cols] = cv.GetSize(frame) image = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_8U, frame.nChannels) cv.Copy(frame, image) cv.ShowImage("camera", frame) leftImage = cv.CreateImage((image.width, image.height), 8, 1) cv.CvtColor(image, leftImage, cv.CV_BGR2GRAY) frame2 = cv.QueryFrame(self.capture2) print type(frame2) image2 = cv.CreateImage(cv.GetSize(frame2), cv.IPL_DEPTH_8U, frame2.nChannels) cv.Copy(frame2, image2) cv.ShowImage("camera2", frame2) rightImage = cv.CreateImage((image2.width, image2.height), 8, 1) cv.CvtColor(image2, rightImage, cv.CV_BGR2GRAY) disparity_left = cv.CreateMat(leftImage.height, leftImage.width, cv.CV_16S) disparity_right = cv.CreateMat(rightImage.height, rightImage.width, cv.CV_16S) # data structure initialization state = cv.CreateStereoGCState(16, 2) # print leftImage.width # print leftImage.height # print rightImage.width # print rightImage.height # running the graph-cut algorithm cv.FindStereoCorrespondenceGC(leftImage, rightImage, disparity_left, disparity_right, state) disp_left_visual = cv.CreateMat(leftImage.height, leftImage.width, cv.CV_8U) cv.ConvertScale(disparity_left, disp_left_visual, -16); # cv.Save("disparity.pgm", disp_left_visual); # save the map # # cutting the object farthest of a threshold (120) cut(disp_left_visual, leftImage, 120) # cv.NamedWindow('Disparity map', cv.CV_WINDOW_AUTOSIZE) cv.ShowImage('Disparity map', disp_left_visual) # # minimum value for the intensity maxValue = 0 maxPoint = None # now for all the moving object centers get the average intensity value for entity in this_frame_entity_list: center_point = entity[3] c = entity[1] # RGB color tuple # cv.Circle(display_image, center_point, 20, cv.CV_RGB(c[0], c[1], c[2]), 1) # cv.Circle(display_image, center_point, 15, cv.CV_RGB(c[0], c[1], c[2]), 1) # cv.Circle(display_image, center_point, 10, cv.CV_RGB(c[0], c[1], c[2]), 2) # cv.Circle(display_image, center_point, 5, cv.CV_RGB(c[0], c[1], c[2]), 3) # cv.Avg(arr) print center_point print type(disp_left_visual) print size(disp_left_visual) print center_point print "value " + str(cv.Get2D(disp_left_visual, center_point[1], center_point[0])) value = cv.Get2D(disp_left_visual, center_point[1], center_point[0])[0] + cv.Get2D(disp_left_visual, center_point[1], center_point[0])[1] + cv.Get2D(disp_left_visual, center_point[1], center_point[0])[2] if value > maxValue: maxValue = value maxPoint = center_point print "min value is " + str(maxValue) print " min point is " + str(maxPoint) if maxPoint != None: cv.Circle(display_image, maxPoint, 17, cv.CV_RGB(100, 100, 100), 1) #distance = 1 / maxValue # find if it is right or left or in the middle if maxValue > 100: middle = width / 2 if maxPoint[0] > middle + 20: message = "look left" elif maxPoint[0] < middle - 20: message = "look right" else: message = "look middle" print "message " + message t1 = time.time() time_delta = t1 - t0 processed_fps = float(frame_count) / time_delta print "Got %d frames. %.1f s. %f fps." % (frame_count, time_delta, processed_fps)