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 extract_items(field_items, geo_image, image, marked_image, filter_edge_items=True):
'''Extract items into separate images. Return updated list of field items.'''
if filter_edge_items:
# Filter out any items that touch the image border since it likely doesn't represent entire item.
items_without_border_elements = []
for item in field_items:
if touches_image_border(item, geo_image):
# Mark as special color to show user why it wasn't included.
if marked_image is not None:
dark_orange = (0, 140, 255) # dark orange
draw_rect(marked_image, item.bounding_rect, dark_orange, thickness=2)
else:
items_without_border_elements.append(item)
field_items = items_without_border_elements
# Extract field items into separate image
for item in field_items:
extracted_image = extract_square_image(image, item.bounding_rect, 20)
extracted_image_fname = postfix_filename(geo_image.file_name, "_{}_{}".format(item.type, item.name))
extracted_image_path = ImageWriter.save_normal(extracted_image_fname, extracted_image)
item.image_path = extracted_image_path
item.parent_image_filename = geo_image.file_name
item.position = calculate_item_position(item, geo_image)
item.zone = geo_image.zone
return field_items
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 extract_global_plants_from_images(plants, geo_images, out_directory):
for plant in plants:
global_bounding_rect = plant.bounding_rect
if global_bounding_rect is None:
continue
found_in_image = False
for k, geo_image in enumerate(geo_images):
from src.util.clustering import rect_to_image
image_rect = rect_to_image(global_bounding_rect, geo_image)
x, y = image_rect[0]
if x > 0 and x < geo_image.width and y > 0 and y < geo_image.height:
found_in_image = True
if not plant.parent_image_filename.strip():
plant.bounding_rect = image_rect
plant.parent_image_filename = geo_image.file_name
else:
plant_copy = plant.__class__('plant', position=plant.position, zone=plant.zone)
plant_copy.bounding_rect = image_rect
plant_copy.parent_image_filename = geo_image.file_name
plant.add_other_item(plant_copy)
if out_directory is None:
continue
image_out_directory = os.path.join(out_directory, os.path.splitext(geo_image.file_name)[0])
ImageWriter.output_directory = image_out_directory
img = cv2.imread(geo_image.file_path, cv2.CV_LOAD_IMAGE_COLOR)
if img is not None:
plant_img = extract_square_image(img, image_rect, 200)
plant_image_fname = postfix_filename(geo_image.file_name, "_{}".format(plant.type))
plant.image_path = ImageWriter.save_normal(plant_image_fname, plant_img)
if not found_in_image:
# Can't convert global rect to a rotated image rect so remove it to be consistent.
plant.bounding_rect = None
