erickahmed
153 lines
4.5 KiB
Python
153 lines
4.5 KiB
Python
from genericpath import isfile
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import io
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import os
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import sys
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import numpy as np
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import cv2
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import json
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from pyautogui import size
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from google.cloud import vision
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# Minimum score required for labels detection
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MIN_SCORE_REQUIRED = 0.80
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def shape_selection(event, x, y, flags, param):
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# grab references to the global variables
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global ref_point, crop
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# if the left mouse button was clicked, record the starting
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# (x, y) coordinates and indicate that cropping is being performed
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if event == cv2.EVENT_LBUTTONDOWN:
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ref_point = [(x, y)]
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# check to see if the left mouse button was released
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elif event == cv2.EVENT_LBUTTONUP:
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# record the ending (x, y) coordinates and indicate that
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# the cropping operation is finished
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ref_point.append((x, y))
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# draw a rectangle around the region of interest
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cv2.rectangle(image, ref_point[0], ref_point[1], (0, 255, 0), 2)
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cv2.imshow("image", image)
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# If a key is present in /keys and has a correct name, add it to enviroment variable
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key_folder = os.path.join("..", "keys")
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key_names = ["devKey.json",
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"devkey.json",
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"developerkey.json",
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"developerKey.json"]
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for i in key_names:
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success = False
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key_rel_path = os.path.join(key_folder, i)
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key_abs_path = os.path.abspath(key_rel_path)
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if os.path.exists(key_abs_path):
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success = True
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os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = key_abs_path
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break
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if not success:
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print("API keys not found. Closing executable.")
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sys.exit(0)
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# User input from the command line
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image_name = sys.argv[1]
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# The name of the image file to annotate
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file_name = os.path.abspath(image_name)
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# File path check
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if not(os.path.exists(file_name)):
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while True:
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print("Wrong file or file path\n")
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# Press q to exit or enter correct path
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userin = input("Enter the correct path or press q to quit: ")
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if userin == "q" or "Q":
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sys.exit(0)
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else:
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file_name = userin
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break
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# Initialize the list of reference point
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ref_point = []
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crop = False
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image = cv2.imread(file_name, 1)
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#Fetch screen and image res data
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scr_width, scr_height = size()
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img_width, img_height = image.shape[0], image.shape[1]
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img_ratio = img_height / img_width
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resize_factor = 0.8 # 1 is fullscreen
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# Check if image is larger than monitor
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if img_width / scr_width > 1 or img_height / scr_height > 1:
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#scale image to a more reasonable size
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scaled_width = int((2 - (img_width / scr_width)) * img_width * resize_factor)
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scaled_height = int(scaled_width / img_ratio)
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image = cv2.resize(image, (scaled_width, scaled_height), interpolation=cv2.INTER_CUBIC)
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cv2.imwrite('~/vision-env/images/testset/test0/copy_res.jpg', image)
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#FIXME: fix absolute/relative path problem on Linux on cloning image
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clone = image.copy()
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cv2.namedWindow("image")
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cv2.setMouseCallback("image", shape_selection)
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# keep looping until the 'q' key is pressed
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while True:
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# display the image and wait for a keypress
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cv2.imshow("image", image)
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key = cv2.waitKey(1) & 0xFF
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# press 'r' to reset the window
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if key == ord("r"):
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image = clone.copy()
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# press 'q' to quit
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if key == ord("q"):
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sys.exit(0)
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# if the 'c' key is pressed, break from the loop
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elif key == ord("c"):
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break
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if len(ref_point) == 2:
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crop_img = clone[ref_point[0][1]:ref_point[1][1], ref_point[0][0]:ref_point[1][0]]
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#cv2.imshow("crop_img", crop_img) #uncomment --> show cropped image
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cv2.waitKey(0) #maybe useless
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cv2.imwrite(os.path.join("..", "images/testset/test0/copy1.jpg"), crop_img)
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# close all open windows
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cv2.destroyAllWindows()
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# File used for label detection
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target_file = os.path.abspath(os.path.join("..", "images/testset/test0/copy1.jpg"))
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# Loads the image into memory
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with io.open(target_file, 'rb') as image_file:
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content = image_file.read()
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# Instantiates a client
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client = vision.ImageAnnotatorClient()
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#image = vision.Image(content=content)
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image = vision.Image(content=content)
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# Performs label detection on the image file
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response = client.label_detection(image=image)
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labels = response.label_annotations
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# Labels sorted by attribute score
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sorted_labels = sorted(labels, key=lambda x:x.score, reverse=True)
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# Labels filtered by attribute score
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filtered_labels = filter(lambda x:x.score > MIN_SCORE_REQUIRED, sorted_labels)
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print('Labels:')
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for label in filtered_labels:
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print(label.description + ' ---> ' + str(label.score))
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