mirror of
https://github.com/ARM-software/workload-automation.git
synced 2025-02-22 12:58:36 +00:00
01f2a5f412
- Added statedetect.py in utils which is a standalone module that
contains all the methods needed for state detection
- Modified the setup() and run() methods of the GameWorkload class
in common/android/workload.py to have a parameter that enables
state checks and run the check after setup and run if requested.
State detection uses the template matching method available in
OpenCV to determine the state of the workload by detecting
predefined unique elements on a screenshot from the device.
137 lines
4.5 KiB
Python
Executable File
137 lines
4.5 KiB
Python
Executable File
# Copyright 2013-2016 ARM Limited
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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State detection functionality for revent workloads. Uses OpenCV to analyse screenshots from the device.
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Requires a 'statedetection' directory in the workload directory that includes the state definition yaml file,
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and the 'templates' folder with PNGs of all templates mentioned in the yaml file.
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Requires the following Python libraries:
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numpy, pyyaml (yaml), imutils and opencv (cv2)
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"""
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import os
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import yaml
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try:
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import numpy as np
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except ImportError:
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np = None
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try:
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import cv2
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except ImportError:
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cv2 = None
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try:
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import imutils
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except ImportError:
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imutils = None
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class StateDefinitionError(RuntimeError):
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def __init__(self, arg):
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self.args = arg
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def auto_canny(image, sigma=0.33):
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# compute the median of the single channel pixel intensities
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v = np.median(image)
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# apply automatic Canny edge detection using the computed median
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lower = int(max(0, (1.0 - sigma) * v))
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upper = int(min(255, (1.0 + sigma) * v))
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edged = cv2.Canny(image, lower, upper)
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# return the edged image
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return edged
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def match_state(screenshot_file, defpath, state_definitions):
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# check dependencies
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if np == None or cv2 == None or imutils == None:
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raise RuntimeError("State detection requires numpy, opencv (cv2) and imutils.")
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# check if file exists, then load screenshot into opencv and create edge map
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if not os.path.isfile(screenshot_file):
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raise StateDefinitionError("Screenshot file not found")
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img_rgb = cv2.imread(screenshot_file)
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img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
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img_edge = auto_canny(img_gray)
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# make a list of all templates defined in the state definitions
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template_list = []
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for state in state_definitions["workload_states"]:
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template_list.extend(state["templates"])
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# check all template PNGs exist
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for template_png in template_list:
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if not os.path.isfile(os.path.join(defpath, 'templates', template_png+'.png')):
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raise StateDefinitionError("Missing template PNG file: " + template_png + ".png")
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# try to match each PNG
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matched_templates = []
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for template_png in template_list:
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template = cv2.imread(os.path.join(defpath, 'templates', template_png+'.png'), 0)
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template_edge = auto_canny(template)
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w, h = template.shape[::-1]
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res = cv2.matchTemplate(img_edge, template_edge, cv2.TM_CCOEFF_NORMED)
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threshold = 0.5
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loc = np.where(res >= threshold)
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zipped = zip(*loc[::-1])
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if len(zipped) > 0:
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matched_templates.append(template_png)
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# determine the state according to the matched templates
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matched_state = "none"
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for state in state_definitions["workload_states"]:
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# look in the matched templates list for each template of this state
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match_count = 0
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for template in state["templates"]:
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if template in matched_templates:
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match_count += 1
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if match_count >= state["matches"]:
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# we have a match
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matched_state = state["state_name"]
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break
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return matched_state
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def verify_state(screenshot_file, state_defs_path, workload_phase):
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# load and parse state definition file
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statedefs_file = os.path.join(state_defs_path, 'definition.yaml')
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if not os.path.isfile(statedefs_file):
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raise StateDefinitionError("Missing state definitions yaml file: "+statedefs_file)
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with open(statedefs_file) as fh:
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state_definitions = yaml.load(fh)
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# run a match on the screenshot
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matched_state = match_state(screenshot_file, state_defs_path, state_definitions)
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# find what the expected state is for the given workload phase
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expected_state = None
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for phase in state_definitions["workload_phases"]:
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if phase["phase_name"] == workload_phase:
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expected_state = phase["expected_state"]
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if expected_state is None:
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raise StateDefinitionError("Phase not defined")
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return expected_state == matched_state
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