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got bounding boxes repositioned for full frame

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blakeblackshear 2019-02-01 06:57:03 -06:00
parent 98ce5a4a59
commit a976403edc
1 changed files with 13 additions and 14 deletions
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27
detect_objects.py
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@ -60,22 +60,21 @@ def detect_objects(cropped_frame, full_frame, sess, detection_graph):
squeezed_boxes = np.squeeze(boxes) squeezed_boxes = np.squeeze(boxes)
squeezed_scores = np.squeeze(scores) squeezed_scores = np.squeeze(scores)
full_frame_shape = full_frame.shape
cropped_frame_shape = cropped_frame.shape
if(len(objects)>0): if(len(objects)>0):
# reposition bounding box based on full frame # reposition bounding box based on full frame
for i, box in enumerate(squeezed_boxes): for i, box in enumerate(squeezed_boxes):
if squeezed_scores[i] > .1: if box[2] > 0:
ymin = ((box[0] * 300) + 200)/1080 # ymin squeezed_boxes[i][0] = ((box[0] * cropped_frame_shape[0]) + 200)/full_frame_shape[0] # ymin
xmin = ((box[1] * 300) + 1300)/1920 # xmin squeezed_boxes[i][1] = ((box[1] * cropped_frame_shape[0]) + 1300)/full_frame_shape[1] # xmin
xmax = ((box[2] * 300) + 200)/1080 # ymax squeezed_boxes[i][2] = ((box[2] * cropped_frame_shape[0]) + 200)/full_frame_shape[0] # ymax
ymax = ((box[3] * 300) + 1300)/1920 # xmax squeezed_boxes[i][3] = ((box[3] * cropped_frame_shape[0]) + 1300)/full_frame_shape[1] # 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)
# draw boxes for detected objects on image # draw boxes for detected objects on image
vis_util.visualize_boxes_and_labels_on_image_array( vis_util.visualize_boxes_and_labels_on_image_array(
cropped_frame, full_frame,
squeezed_boxes, squeezed_boxes,
np.squeeze(classes).astype(np.int32), np.squeeze(classes).astype(np.int32),
squeezed_scores, 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) # cv2.rectangle(full_frame, (800, 100), (1250, 550), (255,0,0), 2)
return objects, cropped_frame return objects, full_frame
def main(): def main():
# capture a single frame and check the frame shape so the correct array # capture a single frame and check the frame shape so the correct array
@ -113,10 +112,10 @@ def main():
# TODO: make dynamic # TODO: make dynamic
shared_cropped_arr = mp.Array(ctypes.c_uint16, 300*300*3) shared_cropped_arr = mp.Array(ctypes.c_uint16, 300*300*3)
# create shared array for passing the image data from detect_objects to flask # 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 # create a numpy array with the image shape from the shared memory array
# this is used by flask to output an mjpeg stream # 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 = mp.Process(target=fetch_frames, args=(shared_arr, shared_cropped_arr, shared_frame_time, frame_shape))
capture_process.daemon = True 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) arr = tonumpyarray(shared_arr).reshape(frame_shape)
shared_cropped_frame = tonumpyarray(shared_cropped_arr).reshape(300,300,3) shared_cropped_frame = tonumpyarray(shared_cropped_arr).reshape(300,300,3)
# shape shared output array into frame so it can be copied into # 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 # Load a (frozen) Tensorflow model into memory before the processing loop
detection_graph = tf.Graph() detection_graph = tf.Graph()