def main():
orig_imgs = glob.glob('photoset/cleaned/*/*.JPG')
for filename in orig_imgs:
# Find all cards in each image
image = cv2.imread(filename)
cards = get_white_rectangles(image)
for card in cards:
image, corners = card
# For each card, detect the shape
shape = detect_shape(image)
print shape
actual_list = [] error_list = [] for location in all_locations: shapes = [] distances = [] start = time.time() for shape in all_locations[location]: image_name = all_locations[location][shape] image = cv2.imread(image_name) # start2 = time.time() cleaned_image, rectangle_coordinates = white_rect_finder.get_white_rectangles(image)[0] # elapsed2 = time.time() - start2 # start3 = time.time() estimated_shape = identify_shapes.detect_shape(cleaned_image) # elapsed3 = time.time() - start3 print shape, estimated_shape #, elapsed2, elapsed3 shapes.append(shape) # find y-coordinates of rectangle low_y = (rectangle_coordinates[0][1] + rectangle_coordinates[1][1]) / 2. high_y = (rectangle_coordinates[2][1] + rectangle_coordinates[3][1]) / 2. # find pixel height
# then find distance from present location to the shape landmark
for shape in shape_name_to_coordinates:
if shape in image_path:
shape_coordinates = shape_name_to_coordinates[shape]
present_coordinates_string = image_path[2].split(',')
present_coordinates = np.array([float(present_coordinates_string[0]),
float(present_coordinates_string[1])])
distance = np.linalg.norm(shape_coordinates - present_coordinates)
y = np.append(y,distance)
break
image = cv2.imread(image_name)
rectangle_coordinates = white_rect_finder.get_white_rectangles(image, DEBUG=False)[0][1]
# find y-coordinates of rectangle
low_y = (rectangle_coordinates[0][1] + rectangle_coordinates[1][1]) / 2.
high_y = (rectangle_coordinates[2][1] + rectangle_coordinates[3][1]) / 2.
# find pixel height
pixels = high_y - low_y
x = np.append(x, pixels)
# print pixels, distance, shape, image_path[-1]
image_dict["pixels"], image_dict["distance"], image_dict["shape"], image_dict["filename"] = \
pixels, distance, shape, image_path[-1]
pixels_to_distance.append(image_dict)
# A short test to verify the shapes are correctly identified
import os
import cv2
from white_rect_finder import get_white_rectangles
from identify_shapes import detect_shape
base_dir = "photoset_labeled/cleaned"
correctly_classified = 0
incorrectly_classified = 0
for dir in os.listdir(base_dir):
shape_dir = base_dir + "/" + dir
for shape_folder in os.listdir(shape_dir):
photo_dir = shape_dir + "/" + shape_folder
for photo in os.listdir(photo_dir):
image = cv2.imread(photo_dir + "/" + photo)
cards = get_white_rectangles(image)
for card in cards:
image, corners = card
shape = detect_shape(image)
if shape == shape_folder:
correctly_classified += 1
else:
shape = detect_shape(image, show_shape=True)
incorrectly_classified +=1
print "Incorrect:",shape,shape_folder,photo_dir,photo
print "Correct: ", correctly_classified
print "Incorrect: ", incorrectly_classified
