Module AmpliVision.src.objs.image.processors.image_processor
Classes
class ColorContourExtractor-
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ColorContourExtractor
This class is responsible for processing an image to isolate the color of the pins.
Methods
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process_image(scanned_image: np.ndarray) -> np.ndarray- This method pre-processes the image to isolate the color of the pins.
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show_result(edges: np.ndarray) -> None- This method shows the result of the pre-processing.
Example
import cv2 as cv import numpy as np from src.objs.image.processors.image_processor import ImageProcessor scanned_image = cv.imread('path/to/image.jpg') edges = ImageProcessor.process_image(scanned_image) ImageProcessor.show_result(edges)Expand source code
class ColorContourExtractor: """" ## ColorContourExtractor This class is responsible for processing an image to isolate the color of the pins. ### Methods - `process_image(scanned_image: np.ndarray) -> np.ndarray` - This method pre-processes the image to isolate the color of the pins. - `show_result(edges: np.ndarray) -> None` - This method shows the result of the pre-processing. ### Example ```python import cv2 as cv import numpy as np from src.objs.image.processors.image_processor import ImageProcessor scanned_image = cv.imread('path/to/image.jpg') edges = ImageProcessor.process_image(scanned_image) ImageProcessor.show_result(edges) ``` """ # A function that pre-processes the image to isolate the color of the pins. @staticmethod def process_image( scanned_image: np.ndarray, hsv_lower = [0, 55, 0], hsv_upper = [360, 255,255], double_thresh:bool = False, display:bool=False) -> np.ndarray: """ This method pre-processes the image to isolate the color of the pins in Grid to find blocks. It is also used in TestAnalyzer to find the positive spots with hsv mask. Thread carefully """ # Copy the image to avoid modifying the original image scanned_image_copy = scanned_image.copy() # Convert the image to HSV color space. Hue Saturation Value. # Similar to RGB but more useful for color isolation. img_hsv = cv.cvtColor(scanned_image_copy, cv.COLOR_BGR2HSV) # Define the lower and upper bounds for the color you want to isolate # These values are the product of trial and error and are not necessarily perfect. hsv_lower_color = np.array(hsv_lower) hsv_upper_color = np.array(hsv_upper) # Create a mask to filter out the grayscale colors isolating the color of the pins. color_mask = cv.inRange(img_hsv, hsv_lower_color, hsv_upper_color) # Visualize the mask on top of the original image before thresholding #mask_before_thresholding = color_mask.copy() edges = cv.Canny(color_mask, 0, 255) if double_thresh: second_mask = cv.bitwise_and(scanned_image_copy, scanned_image_copy, mask=color_mask) #cv.imshow('bitwise and image + mask 1', cv.resize(color_mask,(200,200))) # make all black pixels white second_mask[second_mask == 0] = 255 #cv.imshow('make black pixels white', cv.resize(color_mask, (200, 200))) # thresholding second_mask = cv.cvtColor(second_mask, cv.COLOR_BGR2GRAY) #cv.imshow('grey', cv.resize(color_mask, (200, 200))) second_mask = cv.bitwise_not(second_mask) #cv.imshow('bitwise not', cv.resize(second_mask, (200, 200))) #cv.waitKey(0) cv.threshold(second_mask, 125, 255, cv.THRESH_BINARY, second_mask) edges = cv.Canny(second_mask, 0, 255) #cv.imshow('second thresholding ', cv.resize( # cv.bitwise_and(scanned_image_copy,scanned_image_copy, mask=second_mask), (400, 400))) #cv.imshow('first thresholding ', cv.resize( # cv.bitwise_and(scanned_image_copy,scanned_image_copy, mask=color_mask), (400, 400))) #cv.waitKey(0) #cv.destroyAllWindows() contours, _ = cv.findContours(edges, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_NONE) if display: pass contours = ColorContourExtractor.show_result(contours, scanned_image_copy) return contours # Show the result of the pre-processing. @staticmethod def show_result(contours: np.ndarray, image) -> None: """ this method shows the result of the pre-processing.""" copy = image.copy() # show only the pixels where sqrt(a^2 + b^2) > 10 # this will remove the background noise lab = cv.cvtColor(copy, cv.COLOR_BGR2LAB) lab = cv.blur(lab, (3, 3)) # split the image into L, A, and B channels l, a, b = cv.split(lab) r, g, b = cv.split(copy) plt.imshow(copy) plt.title('Color Contour Extractor - COPY') plt.show() for row in range(len(l)): for col in range(len(l[0])): if l[row][col] >= 225: l[row][col] = 0 a[row][col] = 0 b[row][col] = 0 else: l[row][col] = 255 a[row][col] = 255 b[row][col] = 255 lab = cv.merge((l, a, b)) mask = cv.bitwise_and(lab, copy) mask = cv.cvtColor(mask, cv.COLOR_BGR2GRAY) mask = cv.bitwise_not(mask) # draw the contours edges = cv.Canny(mask, 0, 255) contours, _ = cv.findContours(edges, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_NONE) cv.drawContours(copy, contours, -1, (0, 255, 0), 1) copy = cv.resize(copy, (400, 400)) """ copy = image.copy() cv.drawContours(copy, contours, -1, (0, 255, 0), 1) copy = cv.resize(copy, (400, 400)) """ plt.imshow(copy) plt.title('Color Contour Extractor') plt.show() return max(contours, key = cv.contourArea)Static methods
def process_image(scanned_image: numpy.ndarray, hsv_lower=[0, 55, 0], hsv_upper=[360, 255, 255], double_thresh: bool = False, display: bool = False) ‑> numpy.ndarray-
This method pre-processes the image to isolate the color of the pins in Grid to find blocks. It is also used in TestAnalyzer to find the positive spots with hsv mask. Thread carefully
def show_result(contours: numpy.ndarray, image) ‑> None-
this method shows the result of the pre-processing.
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