From a976403edcbfc9c083682aa8ad5e7ec73da20b73 Mon Sep 17 00:00:00 2001 From: blakeblackshear Date: Fri, 1 Feb 2019 06:57:03 -0600 Subject: [PATCH] got bounding boxes repositioned for full frame --- detect_objects.py | 27 +++++++++++++-------------- 1 file changed, 13 insertions(+), 14 deletions(-) diff --git a/detect_objects.py b/detect_objects.py index 8c4ca2142..bce8835d4 100644 --- a/detect_objects.py +++ b/detect_objects.py @@ -60,22 +60,21 @@ def detect_objects(cropped_frame, full_frame, sess, detection_graph): squeezed_boxes = np.squeeze(boxes) squeezed_scores = np.squeeze(scores) + full_frame_shape = full_frame.shape + cropped_frame_shape = cropped_frame.shape + if(len(objects)>0): # reposition bounding box based on full frame for i, box in enumerate(squeezed_boxes): - if squeezed_scores[i] > .1: - ymin = ((box[0] * 300) + 200)/1080 # ymin - xmin = ((box[1] * 300) + 1300)/1920 # xmin - xmax = ((box[2] * 300) + 200)/1080 # ymax - ymax = ((box[3] * 300) + 1300)/1920 # xmax - print("ymin", box[0] * 300, ymin) - print("xmin", box[1] * 300, xmin) - print("ymax", box[2] * 300, ymax) - print("xmax", box[3] * 300, xmax) + if box[2] > 0: + squeezed_boxes[i][0] = ((box[0] * cropped_frame_shape[0]) + 200)/full_frame_shape[0] # ymin + squeezed_boxes[i][1] = ((box[1] * cropped_frame_shape[0]) + 1300)/full_frame_shape[1] # xmin + squeezed_boxes[i][2] = ((box[2] * cropped_frame_shape[0]) + 200)/full_frame_shape[0] # ymax + squeezed_boxes[i][3] = ((box[3] * cropped_frame_shape[0]) + 1300)/full_frame_shape[1] # xmax # draw boxes for detected objects on image vis_util.visualize_boxes_and_labels_on_image_array( - cropped_frame, + full_frame, squeezed_boxes, np.squeeze(classes).astype(np.int32), squeezed_scores, @@ -86,7 +85,7 @@ def detect_objects(cropped_frame, full_frame, sess, detection_graph): # cv2.rectangle(full_frame, (800, 100), (1250, 550), (255,0,0), 2) - return objects, cropped_frame + return objects, full_frame def main(): # capture a single frame and check the frame shape so the correct array @@ -113,10 +112,10 @@ def main(): # TODO: make dynamic shared_cropped_arr = mp.Array(ctypes.c_uint16, 300*300*3) # create shared array for passing the image data from detect_objects to flask - shared_output_arr = mp.Array(ctypes.c_uint16, 300*300*3)#flat_array_length) + shared_output_arr = mp.Array(ctypes.c_uint16, flat_array_length) # create a numpy array with the image shape from the shared memory array # this is used by flask to output an mjpeg stream - frame_output_arr = tonumpyarray(shared_output_arr).reshape(300,300,3) + frame_output_arr = tonumpyarray(shared_output_arr).reshape(frame_shape) capture_process = mp.Process(target=fetch_frames, args=(shared_arr, shared_cropped_arr, shared_frame_time, frame_shape)) capture_process.daemon = True @@ -199,7 +198,7 @@ def process_frames(shared_arr, shared_cropped_arr, shared_output_arr, shared_fra arr = tonumpyarray(shared_arr).reshape(frame_shape) shared_cropped_frame = tonumpyarray(shared_cropped_arr).reshape(300,300,3) # shape shared output array into frame so it can be copied into - output_arr = tonumpyarray(shared_output_arr).reshape(300,300,3) + output_arr = tonumpyarray(shared_output_arr).reshape(frame_shape) # Load a (frozen) Tensorflow model into memory before the processing loop detection_graph = tf.Graph()