def locate(self, geo_image, image, marked_image):
'''Find QR codes in image and decode them. Return list of FieldItems representing valid QR codes.'''
# Threshold grayscaled image to make white QR codes stands out.
#gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
#_, mask = cv2.threshold(gray_image, 100, 255, 0)
hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
lower_white = np.array([0, 0, 160], np.uint8)
upper_white = np.array([179, 65, 255], np.uint8)
mask = cv2.inRange(hsv_image, lower_white, upper_white)
# Find outer contours (edges) and 'approximate' them to reduce the number of points along nearly straight segments.
contours, hierarchy = cv2.findContours(mask.copy(), cv2.cv.CV_RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
#contours = [cv2.approxPolyDP(contour, .1, True) for contour in contours]
# Create bounding box for each contour.
bounding_rectangles = [cv2.minAreaRect(contour) for contour in contours]
# Remove any rectangles that couldn't be a QR item based off specified side length.
filtered_rectangles = filter_by_size(bounding_rectangles, geo_image.resolution, self.qr_min_size, self.qr_max_size)
if ImageWriter.level <= ImageWriter.DEBUG:
# Debug save intermediate images
thresh_filename = postfix_filename(geo_image.file_name, 'thresh')
ImageWriter.save_debug(thresh_filename, mask)
# Scan each rectangle with QR reader to remove false positives and also extract data from code.
qr_items = []
for rectangle in filtered_rectangles:
qr_data = self.scan_image_different_trims_and_threshs(image, rectangle, trims=[0, 3, 8, 12])
scan_successful = len(qr_data) != 0 and len(qr_data[0]) != 0
if scan_successful:
qr_code = create_qr_code(qr_data[0])
if qr_code is None:
print 'WARNING: Invalid QR data found ' + qr_data[0]
else:
qr_code.bounding_rect = rectangle
qr_items.append(qr_code)
elif self.missed_code_finder is not None:
# Scan wasn't successful so tag this rectangle for later analysis.
self.missed_code_finder.add_possibly_missed_code(rectangle, geo_image)
if marked_image is not None:
# Show success/failure using colored bounding box
success_color = (0, 255, 0) # green
if scan_successful and qr_code is not None and qr_code.type == 'RowCode':
success_color = (0, 255, 255) # yellow for row codes
failure_color = (0, 0, 255) # red
item_color = success_color if scan_successful else failure_color
draw_rect(marked_image, rectangle, item_color, thickness=2)
return qr_items
def locate(self, geo_image, image, marked_image):
'''Find possible plant leaves in image and return list of rotated rectangle instances.'''
# Convert Blue-Green-Red color space to HSV
hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# Threshold the HSV image to get only green colors that correspond to healthy plants.
lower_green = np.array([35, 80, 20], np.uint8)
upper_green = np.array([90, 255, 255], np.uint8)
plant_mask = cv2.inRange(hsv_image, lower_green, upper_green)
# Now do the same thing for greenish dead plants.
#lower_dead_green = np.array([10, 35, 60], np.uint8)
#upper_dead_green = np.array([90, 255, 255], np.uint8)
#dead_green_plant_mask = cv2.inRange(hsv_image, lower_dead_green, upper_dead_green)
# Now do the same thing for yellowish dead plants.
#lower_yellow = np.array([10, 50, 125], np.uint8)
#upper_yellow = np.array([40, 255, 255], np.uint8)
#dead_yellow_plant_mask = cv2.inRange(hsv_image, lower_yellow, upper_yellow)
filtered_rectangles = []
for i, mask in enumerate([plant_mask]):
# Open mask (to remove noise) and then dilate it to connect contours.
kernel = np.ones((3,3), np.uint8)
mask_open = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
mask = cv2.dilate(mask_open, kernel, iterations = 1)
# Find outer contours (edges) and 'approximate' them to reduce the number of points along nearly straight segments.
contours, hierarchy = cv2.findContours(mask.copy(), cv2.cv.CV_RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# Create bounding box for each contour.
bounding_rectangles = [cv2.minAreaRect(contour) for contour in contours]
if marked_image is not None and ImageWriter.level <= ImageWriter.DEBUG:
for rectangle in bounding_rectangles:
# Show rectangles using bounding box.
draw_rect(marked_image, rectangle, (0,0,0), thickness=2)
# Remove any rectangles that couldn't be a plant based off specified size.
filtered_rectangles.extend(filter_by_size(bounding_rectangles, geo_image.resolution, self.min_leaf_size, self.max_leaf_size, enforce_min_on_w_and_h=False))
if ImageWriter.level <= ImageWriter.DEBUG:
# Debug save intermediate images
mask_filename = postfix_filename(geo_image.file_name, 'mask_{}'.format(i))
ImageWriter.save_debug(mask_filename, mask)
if marked_image is not None:
for rectangle in filtered_rectangles:
# Show rectangles using colored bounding box.
purple = (255, 0, 255)
draw_rect(marked_image, rectangle, purple, thickness=2)
return filtered_rectangles
def locate(self, geo_image, image, marked_image):
'''Find possible blue sticks in image and return list of rotated bounding box instances.'''
# Convert Blue-Green-Red color space to HSV
hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# Create binary mask image where potential sticks are white.
lower_blue = np.array([90, 31, 16], np.uint8)
upper_blue = np.array([130, 255, 255], np.uint8)
mask = cv2.inRange(hsv_image, lower_blue, upper_blue)
# Open mask (to remove noise) and then dilate it to connect contours.
kernel = np.ones((3,3), np.uint8)
mask_open = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
mask = cv2.dilate(mask_open, kernel, iterations = 1)
# Find outer contours (edges)
contours, hierarchy = cv2.findContours(mask.copy(), cv2.cv.CV_RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# Create bounding box for each contour.
bounding_rectangles = [cv2.minAreaRect(contour) for contour in contours]
if marked_image is not None and ImageWriter.level <= ImageWriter.DEBUG:
for rectangle in bounding_rectangles:
# Show rectangles using bounding box.
draw_rect(marked_image, rectangle, (255,255,255), thickness=1)
# Remove any rectangles that couldn't be a stick part based off specified size.
filtered_rectangles = filter_by_size(bounding_rectangles, geo_image.resolution, self.min_stick_part_size, self.max_stick_part_size, enforce_min_on_w_and_h=True)
if ImageWriter.level <= ImageWriter.DEBUG:
# Debug save intermediate images
mask_filename = postfix_filename(geo_image.file_name, 'blue_thresh')
ImageWriter.save_debug(mask_filename, mask)
if marked_image is not None:
for rectangle in filtered_rectangles:
# Show rectangles using colored bounding box.
purple = (50, 255, 255)
draw_rect(marked_image, rectangle, purple, thickness=2)
return filtered_rectangles
def debug_draw_plants_in_images_subset(debug_geo_images, possible_plants, actual_plants, image_out_directory):
debug_images = []
DebugImage = namedtuple('DebugImage', 'image existed')
for geo_image in debug_geo_images:
if hasattr(geo_image, 'debug_filepath'):
path = geo_image.debug_filepath
already_existed = True
else:
path = geo_image.file_path
already_existed = False
img = cv2.imread(path, cv2.CV_LOAD_IMAGE_COLOR)
if img is None:
print "Could not open image {}".format(path)
continue
debug_images.append(DebugImage(img, already_existed))
for item in possible_plants:
from random import randint
item_color = (randint(50, 255), randint(50, 100), randint(50, 255))
for k, geo_image in enumerate(debug_geo_images):
for ext_item in [item] + item.get('items',[]):
image_rect = rect_to_image(ext_item['rect'], geo_image)
center, size, theta = image_rect
x, y = center
if x >= 0 and x < geo_image.width and y >= 0 and y < geo_image.height:
draw_rect(debug_images[k].image, image_rect, item_color, thickness=2)
for plant in actual_plants:
for ref in plant.all_refs:
if not ref.parent_image_filename:
continue
geo_image_filenames = [geo_image.file_name for geo_image in debug_geo_images]
try:
debug_img_index = geo_image_filenames.index(ref.parent_image_filename)
except ValueError:
continue
debug_img = debug_images[debug_img_index].image
if ref.type == 'CreatedPlant':
draw_rect(debug_img, ref.bounding_rect, (255, 255, 255), thickness=6)
else:
draw_rect(debug_img, ref.bounding_rect, (0, 255, 0), thickness=5)
debug_filepaths = [os.path.join(image_out_directory, postfix_filename(geo_image.file_name, 'marked')) for geo_image in debug_geo_images]
for k, (debug_image, filepath) in enumerate(zip(debug_images, debug_filepaths)):
if not debug_image.existed:
output_debug_image = debug_image.image
# KLM - don't make image smaller or bounding rect coordinates won't be right next time try to draw on image.
#output_debug_image = cv2.resize(debug_image.image, (0,0), fx=0.25, fy=0.25)
else:
output_debug_image = debug_image.image
cv2.imwrite(filepath, output_debug_image)
debug_geo_images[k].debug_filepath = filepath
